• Data Mining for Network Intrusion Detection

    Data Mining for Network Intrusion Detection: Experience with KDDCup’99 Data set

    published: 05 May 2015
  • 2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

    Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from t...

    published: 09 Sep 2013
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 2015
  • Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

    Intrusion Detection System Introduction, Types of Intruders in Hindi with Example Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnb7av...

    published: 06 Dec 2016
  • China's Armed Drones Appear Built from Stolen Data from US Cyber Intrusions

    China's Armed Drones Appear Built from Stolen Data from US Cyber Intrusions - by Bill Gertz China's vibrant military blogosphere presented a video this month revealing a missile-firing unmanned aerial vehicle in action, dropping bombs against ground targets. http://atimes.com/2015/12/chinas-armed-drones-appear-built-from-stolen-data-from-us-cyber-intrusions/ Disclaimer: This YouTube channel is in no way endorsed by or affiliated with the author of this article or the Asia Times. The brief text used in this video has been reproduced under section 107 of the Copyright Act 1976, for "fair use" for the purposes of news reporting, teaching, education and research only. No infringement of copyright or intellectual property intended.

    published: 02 Jan 2016
  • Wireshark and Recognizing Exploits, HakTip 138

    This week on HakTip, Shannon pinpoints an exploitation using Wireshark. Working on the shoulders of last week's episode, this week we'll discuss what exploits look like in Wireshark. The example I'm sharing is from Practical Packet Analysis, a book by Chris Sanders about Wireshark. Our example packet shows what happens when a user visits a malicious site using a bad version of IE. This is called spear phishing. First, we have HTTP traffic on port 80. We notice there is a 302 moved response from the malicious site and the location is all sorts of weird. Then a bunch of data gets transferred from the new site to the user. Click Follow TCP Stream. If you scroll down, you see some weird gibberish that doesn't make sense and an iframe script. In this case, it's the exploit being sent to the...

    published: 12 Mar 2015
  • Intrusion Detection (IDS) Best Practices

    Learn the top intrusion detection best practices. In network security no other tool is as valuable as intrusion detection. The ability to locate and identify malicious activity on your network by examining network traffic in real time gives you visibility unrivaled by any other detective control. More about intrusion detection with AlienVault: https://www.alienvault.com/solutions/intrusion-detection-system First be sure you are using the right tool for the right job. IDS are available in Network and Host forms. Host intrusion detection is installed as an agent on a machine you wish to protect and monitor. Network IDS examines the traffic between hosts - looking for patterns, or signatures, of nefarious behavior. Let’s examine some best practices for Network IDS: • Baselining or Profil...

    published: 24 Nov 2015
  • Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

    Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/

    published: 27 Jul 2015
  • Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    published: 05 May 2015
  • chongshm Destroy All Illegal network intrusions with big data techs

    KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.

    published: 05 May 2015
  • What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

    What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean? INTRUSION DETECTION SYSTEM meaning - INTRUSION DETECTION SYSTEM definition - INTRUSION DETECTION SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS,...

    published: 30 Mar 2017
  • Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

    Intrusion Detection Systems (IDS) and its Types (Network + Host Based) in Hindi Intro An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network.[citation needed] The most common classifications are network intrusion detection systems (NIDS) and h...

    published: 29 Mar 2017
  • Computer and Network Security - Intrusion Detection Systems

    Computer and Network Security - Intrusion Detection Systems

    published: 16 Nov 2013
  • Intrusion Detection System Tutorial: Setup Security Onion

    In this video, I'll show you how to setup Security Onion, an open-source intrusion detection system packaged into a Linux distro. SecOnion is perfect for getting an intrusion detection system up and running quickly, and has some cool additional features like HIDS, SIEM, root kit detection, and file integrity monitoring. For this to work, you will need a switch capable of SPANing/mirroring network traffic to a specific port. I will release a video/information about this process. For a small home network, I'd recommend the following: https://www.amazon.com/NETGEAR-ProSAFE-Gigabit-Managed-GS108E-300NAS/dp/B00M1C0186/ref=sr_1_sc_1?ie=UTF8&qid=1470783563&sr=8-1-spell&keywords=netgear+prosafe+plsu+8+port I'm also going to upload a video about utilizing SecOnion and Splunk to ingest and correl...

    published: 09 Aug 2016
  • Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

    In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Stre...

    published: 19 Oct 2016
  • Uber next in series of database intrusions

    Reports indicated that Uber fell victim to a data breach. Find out how this breach affected customer data.

    published: 19 Mar 2015
  • Prevent network intrusions with Dell SonicWALL security services

    http://www.DellSoftware.com/SW-IP Learn how the Dell Intrusion Prevention System service provides dynamically updated countermeasures for complete protection from application exploits and other malicious traffic.

    published: 30 Jun 2014
  • KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

    This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.

    published: 09 Dec 2013
  • We Lost the Battle against Intrusions. Are We Left to Raise Our Hands in Defeat?

    In this talk, we propose a new security strategy which looks at preventing the consequences of the cyber-attack. This approach takes into consideration that threat actors are already inside the organization, and so focuses on preventing the real risk to the enterprise– the actual exfiltration and hijacking of data.

    published: 25 Jul 2016
  • Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

    Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based ...

    published: 05 Jul 2015
  • "We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

    http://scarfolk.blogspot.com/2014/02/we-watch-you-while-you-sleep-tv-signal.html Here is a rare video from the Scarfolk archives. In 1975 there was a series of anonymous signal intrusions on the Scarfnada TV network. Many believed that the council itself was directly responsible for the illegal broadcasts, though this was never confirmed. However, In 1976 a BBC TV documentary revealed that the council had surreptitiously introduced tranquillisers to the water supply and employed council mediums to sing lullabies outside the bedroom windows of suspect citizens. Once a suspect had fallen asleep, the medium would break into their bedroom and secrete themselves in a wardrobe or beneath the bed. From these vantage points the mediums could record the suspect's dreams and nocturnal mumblings ...

    published: 19 Feb 2014
  • Intrusion Detection System Using Machine Learning Models

    published: 16 Jul 2015
  • Optical Encryption: Is your data fully protected?

    Protecting company and customer data is a core concern of every organization today. Ciena’s WaveLogic Encryption solution provides wire-speed transport-layer optical encryption that is always-on, enabling a highly secure fiber network infrastructure that safeguards all of your in-flight data from illicit intrusions, all of the time. With our industry-leading coherent optics and dedicated end-user key management tool, encryption is made simple. Is your data fully protected? Learn more at: http://www.ciena.com/solutions/wavelogic-encryption/

    published: 20 Jan 2016
  • HIP16-TALK Machine learning based ML techniques

    DAY 1 TALK 3 / Clarence Chio Machine learning-based (ML) techniques for network intrusion detection Machine learning-based (ML) techniques for network intrusion detection have gained notable traction in the web security industry over the past decade. Some Intrusion Detection Systems (IDS) successfully used these techniques to detect and deflect network intrusions before they could cause significant harm to network services. Simply put, IDS systems construct a signature model of how normal traffic looks, using data retrieved from web access logs as input. Then, an online processing system is put in place to maintain a model of how expected network traffic looks like, and/or how malicious traffic looks like. When traffic that is deviant from the expected model exceeds the defined threshold...

    published: 07 Sep 2016
  • Spearphishing data intrusion

    Virus prevention - http://www.afxsearch.com/

    published: 30 Sep 2013
  • Data Mining for Network Intrusion Detection

    Data Mining for Network Intrusion Detection: Experience with KDDCup’99 Data set

    published: 05 May 2015
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 2015
  • HP LaserJets - The World's Most Secure Printers

    http://www.hp.com/go/PrintersThatProtect New HP enterprise-class LaserJets, the world’s most secure printers, come with built-in self-healing security features including: HP Sure Start, Whitelisting and Run-time intrusion detection. Defend your network with the deepest device, data, and document security.

    published: 28 Sep 2015
  • Do more with Opera Mini mobile browser

    Opera Mini – A mobile browser with built-in newsfeed and video downloader! Opera Mini is a so much more than just a browser – get ready to read news, download video and more – all the while saving data! Get it for free: http://opr.as/2kszRcG Got the new Opera Mini browser? Opera Mini is one of the world’s most popular mobile browsers. This fast mobile browser blocks ads and saves you data. The unique compression technology lets you load pages faster and download more video – for free. Browse faster! With Opera Mini you get one of the fastest mobile browsers on the market, developed to run at high speed on any internet connection. So, whether you’re on a secluded island in the Philippines or in urban London, you’ll be browsing at top speed, much thanks to the unique compression techno...

    published: 27 Jan 2017
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 498
videos https://wn.com/Data_Mining_For_Network_Intrusion_Detection
2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report

  • Order:
  • Duration: 1:00:27
  • Updated: 09 Sep 2013
  • views: 1443
videos
Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records, which are processed according to the feature definitions, to generate intrusion detection rules. This framework is a "toolkit" (rather than a "replacement") for the IDS developers. I will discuss the design and implementation issues in utilizing expert domain knowledge in our framework. In the second half of the talk, I will give an overview of our current research efforts, which include: cost-sensitive analysis and modeling techniques for intrusion detection; information-theoretic approaches for anomaly detection; and correlation analysis techniques for understanding attack scenarios and early detection of intrusions. Wenke Lee is an Assistant Professor in the Computer Science Department at North Carolina State University. He received his Ph.D. in Computer Science from Columbia University and B.S. in Computer Science from Zhongshan University, China. His research interests include network security, data mining, and workflow management. He is a Principle Investigator (PI) for research projects in intrusion detection and network management, with funding from DARPA, North Carolina Network Initiatives, Aprisma Management Technologies, and HRL Laboratories. He received a Best Paper Award (applied research category) at the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), and Honorable Mention (runner-up) for Best Paper Award (applied research category) at both KDD-98 and KDD-97. He is a member of ACM and IEEE. (Visit: www.cerias.purdue.edu)
https://wn.com/2000_10_11_Cerias_Developing_Data_Mining_Techniques_For_Intrusion_Detection_A_Progress_Report
KDD99 - Machine Learning for Intrusion Detectors from attacking data

KDD99 - Machine Learning for Intrusion Detectors from attacking data

  • Order:
  • Duration: 45:56
  • Updated: 05 May 2015
  • views: 1405
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

Intrusion Detection System Introduction, Types of Intruders in Hindi with Example

  • Order:
  • Duration: 9:07
  • Updated: 06 Dec 2016
  • views: 11142
videos
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example Like FB Page - https://www.facebook.com/Easy-Engineering-Classes-346838485669475/ Complete Data Structure Videos - https://www.youtube.com/playlist?list=PLV8vIYTIdSna11Vc54-abg33JtVZiiMfg Complete Java Programming Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbL_fSaqiYpPh-KwNCavjIr Previous Years Solved Questions of Java - https://www.youtube.com/playlist?list=PLV8vIYTIdSnajIVnIOOJTNdLT-TqiOjUu Complete DBMS Video Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnYZjtUDQ5-9siMc2d8YeoB4 Previous Year Solved DBMS Questions - https://www.youtube.com/playlist?list=PLV8vIYTIdSnaPiMXU2bmuo3SWjNUykbg6 SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnb7av5opUF2p3Xv9CLwOfbq PL-SQL Programming Tutorials - https://www.youtube.com/playlist?list=PLV8vIYTIdSnadFpRMvtA260-3-jkIDFaG Control System Complete Lectures - https://www.youtube.com/playlist?list=PLV8vIYTIdSnbvRNepz74GGafF-777qYw4
https://wn.com/Intrusion_Detection_System_Introduction,_Types_Of_Intruders_In_Hindi_With_Example
China's Armed Drones Appear Built from Stolen Data from US Cyber Intrusions

China's Armed Drones Appear Built from Stolen Data from US Cyber Intrusions

  • Order:
  • Duration: 1:48
  • Updated: 02 Jan 2016
  • views: 133
videos
China's Armed Drones Appear Built from Stolen Data from US Cyber Intrusions - by Bill Gertz China's vibrant military blogosphere presented a video this month revealing a missile-firing unmanned aerial vehicle in action, dropping bombs against ground targets. http://atimes.com/2015/12/chinas-armed-drones-appear-built-from-stolen-data-from-us-cyber-intrusions/ Disclaimer: This YouTube channel is in no way endorsed by or affiliated with the author of this article or the Asia Times. The brief text used in this video has been reproduced under section 107 of the Copyright Act 1976, for "fair use" for the purposes of news reporting, teaching, education and research only. No infringement of copyright or intellectual property intended.
https://wn.com/China's_Armed_Drones_Appear_Built_From_Stolen_Data_From_US_Cyber_Intrusions
Wireshark and Recognizing Exploits, HakTip 138

Wireshark and Recognizing Exploits, HakTip 138

  • Order:
  • Duration: 6:07
  • Updated: 12 Mar 2015
  • views: 25876
videos
This week on HakTip, Shannon pinpoints an exploitation using Wireshark. Working on the shoulders of last week's episode, this week we'll discuss what exploits look like in Wireshark. The example I'm sharing is from Practical Packet Analysis, a book by Chris Sanders about Wireshark. Our example packet shows what happens when a user visits a malicious site using a bad version of IE. This is called spear phishing. First, we have HTTP traffic on port 80. We notice there is a 302 moved response from the malicious site and the location is all sorts of weird. Then a bunch of data gets transferred from the new site to the user. Click Follow TCP Stream. If you scroll down, you see some weird gibberish that doesn't make sense and an iframe script. In this case, it's the exploit being sent to the user. Scroll down to packet 21 and take a look at the .gif GET request. Lastly, Follow packet 25's TCP Stream. This shows us a windows command shell, and the attacker gaining admin priveledges to view our user's files. FREAKY. But now a network admin could use their intrusion detection system to set up a new alarm whenever an attack of this nature is seen. If someone is trying to do a MITM attack on a user, it might look like our next example packet. 54 and 55 are just ARP packets being sent back and forth, but in packet 56 the attacker sends another ARP packet with a different MAC address for the router, thereby sending the user's data to the attacker then to the router. Compare 57 to 40, and you see the same IP address, but different macs for the destination. This is ARP cache Poisoning. Let me know what you think. Send me a comment below or email us at tips@hak5.org. And be sure to check out our sister show, Hak5 for more great stuff just like this. I'll be there, reminding you to trust your technolust.
https://wn.com/Wireshark_And_Recognizing_Exploits,_Haktip_138
Intrusion Detection (IDS) Best Practices

Intrusion Detection (IDS) Best Practices

  • Order:
  • Duration: 2:55
  • Updated: 24 Nov 2015
  • views: 4299
videos
Learn the top intrusion detection best practices. In network security no other tool is as valuable as intrusion detection. The ability to locate and identify malicious activity on your network by examining network traffic in real time gives you visibility unrivaled by any other detective control. More about intrusion detection with AlienVault: https://www.alienvault.com/solutions/intrusion-detection-system First be sure you are using the right tool for the right job. IDS are available in Network and Host forms. Host intrusion detection is installed as an agent on a machine you wish to protect and monitor. Network IDS examines the traffic between hosts - looking for patterns, or signatures, of nefarious behavior. Let’s examine some best practices for Network IDS: • Baselining or Profiling normal network behavior is a key process for IDS deployment. Every environment is different and determining what’s “normal” for your network allows you to focus better on anomalous and potentially malicious behavior. This saves time and brings real threats to the surface for remediation. • Placement of the IDS device is an important consideration. Most often it is deployed behind the firewall on the edge of your network. This gives the highest visibility but it also excludes traffic that occurs between hosts. The right approach is determined by your available resources. Start with the highest point of visibility and work down into your network. • Consider having multiple IDS installations to cover intra-host traffic • Properly size your IDS installation by examining the amount of data that is flowing in BOTH directions at the area you wish to tap or examine. Add overhead for future expansion. • False positives occur when your IDS alerts you to a threat that you know is innocuous. • An improperly tuned IDS will generate an overwhelming number of False Positives. Establishing a policy that removes known False Positives will save time in future investigations and prevent unwarranted escalations. • Asset inventory and information go hand in hand with IDS. Knowing the role, function, and vulnerabilities of an asset will add valuable context to your investigations Next, let’s look at best practices for Host IDS: • The defaults are not enough. • The defaults for HIDS usually only monitor changes to the basic operating system files. They may not have awareness of applications you have installed or proprietary data you wish to safeguard. • Define what critical data resides on your assets and create policies to detect changes in that data • If your company uses custom applications, be sure to include the logs for them in your HIDS configuration • As with Network IDS removing the occurrence of False Positives is critical Finally, let’s examine best practices for WIDS: • Like physical network detection, placement of WIDS is also paramount. • Placement should be within the range of existing wireless signals • Record and Inventory existing Access Point names and whitelist them AlienVault Unified Security Management (USM) includes built-in network, host and wireless IDS’s. In addition to IDS, USM also includes Security Information and Event Management (SIEM), vulnerability management, behavioral network monitoring, asset discovery and more. Please download USM here to see for yourself: https://www.alienvault.com/free-trial
https://wn.com/Intrusion_Detection_(Ids)_Best_Practices
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

  • Order:
  • Duration: 49:38
  • Updated: 27 Jul 2015
  • views: 4204
videos
Machine learning techniques used in network intrusion detection are susceptible to “model poisoning” by attackers. The speaker will dissect this attack, analyze some proposals for how to circumvent such attacks, and then consider specific use cases of how machine learning and anomaly detection can be used in the web security context. Author: Clarence Chio More: http://www.phdays.com/program/tech/40866/
https://wn.com/Detecting_Network_Intrusions_With_Machine_Learning_Based_Anomaly_Detection_Techniques
Intrusion Detection based on KDD Cup Dataset

Intrusion Detection based on KDD Cup Dataset

  • Order:
  • Duration: 18:41
  • Updated: 05 May 2015
  • views: 2973
videos https://wn.com/Intrusion_Detection_Based_On_Kdd_Cup_Dataset
chongshm Destroy All Illegal network intrusions with big data techs

chongshm Destroy All Illegal network intrusions with big data techs

  • Order:
  • Duration: 26:50
  • Updated: 05 May 2015
  • views: 11
videos
KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.
https://wn.com/Chongshm_Destroy_All_Illegal_Network_Intrusions_With_Big_Data_Techs
What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean?

  • Order:
  • Duration: 5:09
  • Updated: 30 Mar 2017
  • views: 594
videos
What is INTRUSION DETECTION SYSTEM? What does INTRUSION DETECTION SYSTEM mean? INTRUSION DETECTION SYSTEM meaning - INTRUSION DETECTION SYSTEM definition - INTRUSION DETECTION SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network. The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of a HIDS, while a system that analyzes incoming network traffic is an example of a NIDS. It is also possible to classify IDS by detection approach: the most well-known variants are signature-based detection (recognizing bad patterns, such as malware) and anomaly-based detection (detecting deviations from a model of "good" traffic, which often relies on machine learning). Some IDS have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. Though they both relate to network security, an IDS differs from a firewall in that a firewall looks outwardly for intrusions in order to stop them from happening. Firewalls limit access between networks to prevent intrusion and do not signal an attack from inside the network. An IDS evaluates a suspected intrusion once it has taken place and signals an alarm. An IDS also watches for attacks that originate from within a system. This is traditionally achieved by examining network communications, identifying heuristics and patterns (often known as signatures) of common computer attacks, and taking action to alert operators. A system that terminates connections is called an intrusion prevention system, and is another form of an application layer firewall. Some systems may attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system. Intrusion detection and prevention systems (IDPS) are primarily focused on identifying possible incidents, logging information about them, and reporting attempts. In addition, organizations use IDPSes for other purposes, such as identifying problems with security policies, documenting existing threats and deterring individuals from violating security policies. IDPSes have become a necessary addition to the security infrastructure of nearly every organization. IDPSes typically record information related to observed events, notify security administrators of important observed events and produce reports. Many IDPSes can also respond to a detected threat by attempting to prevent it from succeeding. They use several response techniques, which involve the IDPS stopping the attack itself, changing the security environment (e.g. reconfiguring a firewall) or changing the attack's content. Intrusion prevention systems (IPS), also known as intrusion detection and prevention systems (IDPS), are network security appliances that monitor network or system activities for malicious activity. The main functions of intrusion prevention systems are to identify malicious activity, log information about this activity, report it and attempt to block or stop it.. Intrusion prevention systems are considered extensions of intrusion detection systems because they both monitor network traffic and/or system activities for malicious activity. The main differences are, unlike intrusion detection systems, intrusion prevention systems are placed in-line and are able to actively prevent or block intrusions that are detected. IPS can take such actions as sending an alarm, dropping detected malicious packets, resetting a connection or blocking traffic from the offending IP address. An IPS also can correct cyclic redundancy check (CRC) errors, defragment packet streams, mitigate TCP sequencing issues, and clean up unwanted transport and network layer options..
https://wn.com/What_Is_Intrusion_Detection_System_What_Does_Intrusion_Detection_System_Mean
Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

Hindi- Intrusion Detection Systems IDS and its Types (Network + Host Based)

  • Order:
  • Duration: 6:39
  • Updated: 29 Mar 2017
  • views: 1860
videos
Intrusion Detection Systems (IDS) and its Types (Network + Host Based) in Hindi Intro An intrusion detection system (IDS) is a device or software application that monitors a network or systems for malicious activity or policy violations. Any detected activity or violation is typically reported either to an administrator or collected centrally using a security information and event management (SIEM) system. A SIEM system combines outputs from multiple sources, and uses alarm filtering techniques to distinguish malicious activity from false alarms. There is a wide spectrum of IDS, varying from antivirus software to hierarchical systems that monitor the traffic of an entire backbone network.[citation needed] The most common classifications are network intrusion detection systems (NIDS) and host-based intrusion detection systems (HIDS). A system that monitors important operating system files is an example of a HIDS, while a system that analyzes incoming network traffic is an example of a NIDS. It is also possible to classify IDS by detection approach: the most well-known variants are signature-based detection (recognizing bad patterns, such as malware) and anomaly-based detection (detecting deviations from a model of "good" traffic, which often relies on machine learning). Some IDS have the ability to respond to detected intrusions. Systems with response capabilities are typically referred to as an intrusion prevention system. Network intrusion detection systems Network intrusion detection systems (NIDS) are placed at a strategic point or points within the network to monitor traffic to and from all devices on the network. It performs an analysis of passing traffic on the entire subnet, and matches the traffic that is passed on the subnets to the library of known attacks. Once an attack is identified, or abnormal behavior is sensed, the alert can be sent to the administrator. An example of an NIDS would be installing it on the subnet where firewalls are located in order to see if someone is trying to break into the firewall. Ideally one would scan all inbound and outbound traffic, however doing so might create a bottleneck that would impair the overall speed of the network. OPNET and NetSim are commonly used tools for simulation network intrusion detection systems. NID Systems are also capable of comparing signatures for similar packets to link and drop harmful detected packets which have a signature matching the records in the NIDS. When we classify the design of the NIDS according to the system interactivity property, there are two types: on-line and off-line NIDS, often referred to as inline and tap mode, respectively. On-line NIDS deals with the network in real time. It analyses the Ethernet packets and applies some rules, to decide if it is an attack or not. Off-line NIDS deals with stored data and passes it through some processes to decide if it is an attack or not. Host intrusion detection systems Host intrusion detection systems (HIDS) run on individual hosts or devices on the network. A HIDS monitors the inbound and outbound packets from the device only and will alert the user or administrator if suspicious activity is detected. It takes a snapshot of existing system files and matches it to the previous snapshot. If the critical system files were modified or deleted, an alert is sent to the administrator to investigate. An example of HIDS usage can be seen on mission critical machines, which are not expected to change their configurations. Intrusion detection systems can also be system-specific using custom tools and honeypots. Find More Info at https://goo.gl/L2XzQg Like Facebook Page https://www.facebook.com/genrontech Follow Twitter Page https://twitter.com/GenronTech Follow Google Pag https://plus.google.com/+Genrontechdotcom Follow Pinterest https://in.pinterest.com/genrontech
https://wn.com/Hindi_Intrusion_Detection_Systems_Ids_And_Its_Types_(Network_Host_Based)
Computer and Network Security - Intrusion Detection Systems

Computer and Network Security - Intrusion Detection Systems

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  • Duration: 25:20
  • Updated: 16 Nov 2013
  • views: 11820
videos https://wn.com/Computer_And_Network_Security_Intrusion_Detection_Systems
Intrusion Detection System Tutorial: Setup Security Onion

Intrusion Detection System Tutorial: Setup Security Onion

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  • Duration: 9:53
  • Updated: 09 Aug 2016
  • views: 11692
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In this video, I'll show you how to setup Security Onion, an open-source intrusion detection system packaged into a Linux distro. SecOnion is perfect for getting an intrusion detection system up and running quickly, and has some cool additional features like HIDS, SIEM, root kit detection, and file integrity monitoring. For this to work, you will need a switch capable of SPANing/mirroring network traffic to a specific port. I will release a video/information about this process. For a small home network, I'd recommend the following: https://www.amazon.com/NETGEAR-ProSAFE-Gigabit-Managed-GS108E-300NAS/dp/B00M1C0186/ref=sr_1_sc_1?ie=UTF8&qid=1470783563&sr=8-1-spell&keywords=netgear+prosafe+plsu+8+port I'm also going to upload a video about utilizing SecOnion and Splunk to ingest and correlate the data/alerts your Intrusion detection system will generate. SecOnion comes with ELSA, which you could use (along with Kibana) to display, visualize and create alerts. Finally, i'll upload a video detailing the install and integration of the Collective Intelligence framework with your IDS/SIEM. Expect these videos within the next couple weeks. Links for this video: VirtualBox: https://www.virtualbox.org/wiki/Downloads Security Onion: https://github.com/Security-Onion-Solutions/security-onion/blob/master/Verify_ISO.md
https://wn.com/Intrusion_Detection_System_Tutorial_Setup_Security_Onion
Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

Anomaly Detection in Telecommunications Using Complex Streaming Data | Whiteboard Walkthrough

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  • Duration: 13:50
  • Updated: 19 Oct 2016
  • views: 2252
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In this Whiteboard Walkthrough Ted Dunning, Chief Application Architect at MapR, explains in detail how to use streaming IoT sensor data from handsets and devices as well as cell tower data to detect strange anomalies. He takes us from best practices for data architecture, including the advantages of multi-master writes with MapR Streams, through analysis of the telecom data using clustering methods to discover normal and anomalous behaviors. For additional resources on anomaly detection and on streaming data: Download free pdf for the book Practical Machine Learning: A New Look at Anomaly Detection by Ted Dunning and Ellen Friedman https://www.mapr.com/practical-machine-learning-new-look-anomaly-detection Watch another of Ted’s Whiteboard Walkthrough videos “Key Requirements for Streaming Platforms: A Microservices Advantage” https://www.mapr.com/blog/key-requirements-streaming-platforms-micro-services-advantage-whiteboard-walkthrough-part-1 Read technical blog/tutorial “Getting Started with MapR Streams” sample programs by Tugdual Grall https://www.mapr.com/blog/getting-started-sample-programs-mapr-streams Download free pdf for the book Introduction to Apache Flink by Ellen Friedman and Ted Dunning https://www.mapr.com/introduction-to-apache-flink
https://wn.com/Anomaly_Detection_In_Telecommunications_Using_Complex_Streaming_Data_|_Whiteboard_Walkthrough
Uber next in series of database intrusions

Uber next in series of database intrusions

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  • Duration: 1:01
  • Updated: 19 Mar 2015
  • views: 7
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Reports indicated that Uber fell victim to a data breach. Find out how this breach affected customer data.
https://wn.com/Uber_Next_In_Series_Of_Database_Intrusions
Prevent network intrusions with Dell SonicWALL security services

Prevent network intrusions with Dell SonicWALL security services

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  • Duration: 2:45
  • Updated: 30 Jun 2014
  • views: 1125
videos
http://www.DellSoftware.com/SW-IP Learn how the Dell Intrusion Prevention System service provides dynamically updated countermeasures for complete protection from application exploits and other malicious traffic.
https://wn.com/Prevent_Network_Intrusions_With_Dell_Sonicwall_Security_Services
KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset

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  • Duration: 13:52
  • Updated: 09 Dec 2013
  • views: 2816
videos
This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
https://wn.com/Kdd_Cupset_Intrusion_Detection_Dataset_Import_To_Mysql_Database_Simpleway_How_To_Use_Kdd_Cupset
We Lost the Battle against Intrusions. Are We Left to Raise Our Hands in Defeat?

We Lost the Battle against Intrusions. Are We Left to Raise Our Hands in Defeat?

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  • Duration: 27:28
  • Updated: 25 Jul 2016
  • views: 592
videos
In this talk, we propose a new security strategy which looks at preventing the consequences of the cyber-attack. This approach takes into consideration that threat actors are already inside the organization, and so focuses on preventing the real risk to the enterprise– the actual exfiltration and hijacking of data.
https://wn.com/We_Lost_The_Battle_Against_Intrusions._Are_We_Left_To_Raise_Our_Hands_In_Defeat
Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled

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  • Duration: 3:16
  • Updated: 05 Jul 2015
  • views: 1485
videos
Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based Associative Classification (MCAC) to get better results in terms of accuracy, false alarm rate, efficiency, capability to detect new type of attacks. For more details contact: E-Mail: lightsoftomorrowtechnologies@gmail.com Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost.
https://wn.com/Analysis_Of_Intrusion_Detection_From_Kdd_Cup_99_Dataset_Both_Labelled_And_Unlabelled
"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

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  • Duration: 0:43
  • Updated: 19 Feb 2014
  • views: 54020
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http://scarfolk.blogspot.com/2014/02/we-watch-you-while-you-sleep-tv-signal.html Here is a rare video from the Scarfolk archives. In 1975 there was a series of anonymous signal intrusions on the Scarfnada TV network. Many believed that the council itself was directly responsible for the illegal broadcasts, though this was never confirmed. However, In 1976 a BBC TV documentary revealed that the council had surreptitiously introduced tranquillisers to the water supply and employed council mediums to sing lullabies outside the bedroom windows of suspect citizens. Once a suspect had fallen asleep, the medium would break into their bedroom and secrete themselves in a wardrobe or beneath the bed. From these vantage points the mediums could record the suspect's dreams and nocturnal mumblings into a specially designed device called a 'Night Mary', named after the woman who invented it. The data would then be assessed by a local judge who could meter out the appropriate punishments. Many subconscious criminals were caught this way and the numbers of dream crimes plummeted. Literally overnight.
https://wn.com/We_Watch_You_While_You_Sleep_._Tv_Signal_Intrusion_1975_(Scarfnada_Tv)
Intrusion Detection System Using Machine Learning Models

Intrusion Detection System Using Machine Learning Models

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  • Duration: 19:13
  • Updated: 16 Jul 2015
  • views: 2195
videos
https://wn.com/Intrusion_Detection_System_Using_Machine_Learning_Models
Optical Encryption: Is your data fully protected?

Optical Encryption: Is your data fully protected?

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  • Duration: 2:02
  • Updated: 20 Jan 2016
  • views: 1183
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Protecting company and customer data is a core concern of every organization today. Ciena’s WaveLogic Encryption solution provides wire-speed transport-layer optical encryption that is always-on, enabling a highly secure fiber network infrastructure that safeguards all of your in-flight data from illicit intrusions, all of the time. With our industry-leading coherent optics and dedicated end-user key management tool, encryption is made simple. Is your data fully protected? Learn more at: http://www.ciena.com/solutions/wavelogic-encryption/
https://wn.com/Optical_Encryption_Is_Your_Data_Fully_Protected
HIP16-TALK Machine learning based ML techniques

HIP16-TALK Machine learning based ML techniques

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  • Duration: 46:00
  • Updated: 07 Sep 2016
  • views: 319
videos
DAY 1 TALK 3 / Clarence Chio Machine learning-based (ML) techniques for network intrusion detection Machine learning-based (ML) techniques for network intrusion detection have gained notable traction in the web security industry over the past decade. Some Intrusion Detection Systems (IDS) successfully used these techniques to detect and deflect network intrusions before they could cause significant harm to network services. Simply put, IDS systems construct a signature model of how normal traffic looks, using data retrieved from web access logs as input. Then, an online processing system is put in place to maintain a model of how expected network traffic looks like, and/or how malicious traffic looks like. When traffic that is deviant from the expected model exceeds the defined threshold, the IDS flags it as malicious. The theory behind it was that the more data the system sees, the more accurate the model would become. This provides a flexible system for traffic analysis, seemingly perfect for the constantly evolving and growing web traffic patterns. However, this fairytale did not last for long. It was soon found that the attackers had been avoiding detection by ‘poisoning’ the classifier models used by these PCA systems. [1] The adversaries slowly train the detection model by sending large volumes of seemingly benign web traffic to make the classification model more tolerant to outliers and actual malicious attempts. They succeeded. In this talk, we will do a demo of this 'model-poisoning' attack and analyze methods that have been proposed to decrease the susceptibility of ML-based network anomaly detection systems from being manipulated by attackers. [2] Instead of diving into the ML theory behind this, we will emphasize on examples of these systems working in the real world, the attacks that render them impotent, and how it affects developers looking to protect themselves from network intrusion. Most importantly, we will look towards the future of ML-based network intrusion detection. [1] Benjamin I. P. Rubinstein, Blaine Nelson, Ling Huang, Anthony D. Joseph. “Stealthy poisoning attacks on PCA-based anomaly detectors”. In Proc. ACM SIGMETRICS (2009) [2] M. Kloft and P. Laskov. "Online anomaly detection under adversarial impact". In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)
https://wn.com/Hip16_Talk_Machine_Learning_Based_Ml_Techniques
Spearphishing data intrusion

Spearphishing data intrusion

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  • Duration: 2:18
  • Updated: 30 Sep 2013
  • views: 49
videos
Virus prevention - http://www.afxsearch.com/
https://wn.com/Spearphishing_Data_Intrusion
Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection

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  • Duration: 7:47
  • Updated: 05 May 2015
  • views: 498
videos https://wn.com/Data_Mining_For_Network_Intrusion_Detection
KDD99 - Machine Learning for Intrusion Detectors from attacking data

KDD99 - Machine Learning for Intrusion Detectors from attacking data

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  • Duration: 45:56
  • Updated: 05 May 2015
  • views: 1405
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
HP LaserJets - The World's Most Secure Printers

HP LaserJets - The World's Most Secure Printers

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  • Duration: 1:11
  • Updated: 28 Sep 2015
  • views: 2958709
videos
http://www.hp.com/go/PrintersThatProtect New HP enterprise-class LaserJets, the world’s most secure printers, come with built-in self-healing security features including: HP Sure Start, Whitelisting and Run-time intrusion detection. Defend your network with the deepest device, data, and document security.
https://wn.com/Hp_Laserjets_The_World's_Most_Secure_Printers
Do more with Opera Mini mobile browser

Do more with Opera Mini mobile browser

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  • Duration: 0:31
  • Updated: 27 Jan 2017
  • views: 1266828
videos
Opera Mini – A mobile browser with built-in newsfeed and video downloader! Opera Mini is a so much more than just a browser – get ready to read news, download video and more – all the while saving data! Get it for free: http://opr.as/2kszRcG Got the new Opera Mini browser? Opera Mini is one of the world’s most popular mobile browsers. This fast mobile browser blocks ads and saves you data. The unique compression technology lets you load pages faster and download more video – for free. Browse faster! With Opera Mini you get one of the fastest mobile browsers on the market, developed to run at high speed on any internet connection. So, whether you’re on a secluded island in the Philippines or in urban London, you’ll be browsing at top speed, much thanks to the unique compression technology built into the browser. Our speed tests show that Opera Mini is much faster than other mobile browsers like Chrome and the UC browser. Opera Mini in extreme savings mode loads web pages 72% faster than Chrome, and 64% faster than the UC browser. Are you wondering how we made the browser this fast? In addition to the unique compression technology, we built an ad blocker into the browser. Without ads, web pages are so much lighter – it takes no time to load them. It is all win, win, right? You get the content you want – just faster! If you want a speedy mobile browser for Android, it’s time to give Opera Mini a try. Read more about it on http://www.opera.com/mobile/mini/android. Get the news Opera Mini brings the news that’s important to you directly to the browser. The news feed notices what kind of content you like and gives you more of it. Swipe through a range of news channels within the browser, subscribe to your favorite channels, and save stories to read later. You’ll find this mobile browser makes catching up with the world an exciting journey – no more shuffling past the the boring stuff. Download videos Opera Mini’s download manager provides our mobile users with more power and control while downloading web content, including pictures and videos. Also, people are increasingly downloading more video to their phones, as the average phone now has a higher memory. Mobile users want a browser that handles video well, putting mobile browsers that do one step ahead of the crowd. With Opera Mini’s download manager you can: - Control the number of files you download simultaneously. - Get alerts when downloading large files, over 15MB. - Select the download location. Block ads Online ads take up precious screen space, slow down the browsing and adds to the user’s data bill. We added an ad blocker to Opera Mini because it offers our mobile users a better browsing experience. Browsing with Opera Mini now means users can surf at a higher speed, skip extra data charges and stretch their internet packages even further by blocking in the browser intrusive and data-wasting ads and heavy tracking. Opera Mini now loads web pages 40% faster than with the ad blocker disabled. Cost-conscious users will also be pleased to hear that removing online ads has a positive effect on the data bill, saving users lots of data. By blocking ads, Opera Mini users can achieve up to an additional 14% in data savings on top of the default data savings compression mode, so that less is deducted from the user's mobile data plan. Get a mobile browser with ad blocker to browse faster and save data, for free: http://opr.as/2kszRcG Extend your data Opera Mini’s unique compression technology saves you large amounts of data everyday by shrinking web page content data to 10% of the original size, for any web page you request. Choosing one of two modes, users can optimize their data compression for different network conditions. Our data savings mode compresses web pages without affecting the page display, making it the perfect mode for surfing the web on 3G or Wi-Fi networks. The extreme mode compresses web pages extensively, giving users a very high-speed internet experience while using very little mobile data. This mode is ideal for when users are experiencing slow network conditions, or just want to make their data plans last longer. There’s more in Opera Mini – get it for free on Google Play. Video script: Got the new Opera Mini browser? Browse faster! Get the news Download videos Block ads Extend your data There’s more in Opera Mini Download now for free Get in on Google Play Download on the App Store
https://wn.com/Do_More_With_Opera_Mini_Mobile_Browser