• Identify threats and intrusions wrangling logs data with Trifacta

    In this demo, we will see how Trifacta can prepare web logs such as firewall and intrusion logs combined to corporate information to get visibility into possible systems at risk and ensure correct regulatory reporting. Sometimes attacks may mature over months even years; using a Hadoop data lake as part of an information security strategy enables the exploration and reporting of threats over time for better detection of cyber attacks.

    published: 29 Sep 2015
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Computer and Network Security - Intrusion Detection Systems

    Computer and Network Security - Intrusion Detection Systems

    published: 16 Nov 2013
  • 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
  • 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 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
  • Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    published: 05 May 2015
  • #HITB2016AMS D2T1 - CANsee: An Automobile Intrusion Detection System - Jun Li

    Automotive security is a hot research area but up till now, research has only centered around how to attack with no single complete solution for defense. After 2 years of research, I have developed a machine learning based IDS for automobiles to detect abnormal traffic on the CANBUS and built a very low-cost device that can be used to capture raw CAN traffic and wirelessly transmit the data to a computer, mobile phone, or a central server for further analysis. I call this device CANsee – an IDS designed to be deployed as an accessory to detect abnormal behavior of any node on a vehicles internal network. In this session, I will briefly introduce the inner workings of a modern automobile and summarize the attack vectors available and will then explain how we can use CANsee to detect anoma...

    published: 30 Jun 2016
  • "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
  • chongshm Destroy All Illegal network intrusions with big data techs

    KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.

    published: 05 May 2015
Identify threats and intrusions wrangling logs data with Trifacta

Identify threats and intrusions wrangling logs data with Trifacta

  • Order:
  • Duration: 33:03
  • Updated: 29 Sep 2015
  • views: 317
videos
In this demo, we will see how Trifacta can prepare web logs such as firewall and intrusion logs combined to corporate information to get visibility into possible systems at risk and ensure correct regulatory reporting. Sometimes attacks may mature over months even years; using a Hadoop data lake as part of an information security strategy enables the exploration and reporting of threats over time for better detection of cyber attacks.
https://wn.com/Identify_Threats_And_Intrusions_Wrangling_Logs_Data_With_Trifacta
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
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)
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
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
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
Intrusion Detection System Tutorial: Setup Security Onion

Intrusion Detection System Tutorial: Setup Security Onion

  • Order:
  • Duration: 9:53
  • Updated: 09 Aug 2016
  • views: 11692
videos
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
Computer and Network Security - Intrusion Detection Systems

Computer and Network Security - Intrusion Detection Systems

  • Order:
  • Duration: 25:20
  • Updated: 16 Nov 2013
  • views: 11820
videos https://wn.com/Computer_And_Network_Security_Intrusion_Detection_Systems
Uber next in series of database intrusions

Uber next in series of database intrusions

  • Order:
  • Duration: 1:01
  • Updated: 19 Mar 2015
  • views: 7
videos
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
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 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: 11137
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
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
#HITB2016AMS D2T1 - CANsee: An Automobile Intrusion Detection System - Jun Li

#HITB2016AMS D2T1 - CANsee: An Automobile Intrusion Detection System - Jun Li

  • Order:
  • Duration: 58:43
  • Updated: 30 Jun 2016
  • views: 753
videos
Automotive security is a hot research area but up till now, research has only centered around how to attack with no single complete solution for defense. After 2 years of research, I have developed a machine learning based IDS for automobiles to detect abnormal traffic on the CANBUS and built a very low-cost device that can be used to capture raw CAN traffic and wirelessly transmit the data to a computer, mobile phone, or a central server for further analysis. I call this device CANsee – an IDS designed to be deployed as an accessory to detect abnormal behavior of any node on a vehicles internal network. In this session, I will briefly introduce the inner workings of a modern automobile and summarize the attack vectors available and will then explain how we can use CANsee to detect anomalous behavior. In addition, I will fully open source CANsee at HITBSecConf2016 – Amsterdam including schematics, bill of materials and source code. We may also give out some free samples :) ====== Jun Li is a security researcher at Unicorn Team in the largest Chinese security company Qihoo360. He is currently pursuing his masters degree from Chengdu University of Information Technology with a major in IoT security. During his undergraduate studies, he switched between different majors – 2 years in Automobile Electronics and 2 years in Electronic and Electric Engineering. His focus area is in the security of the Internet of Things and the security of automobile electronics. Jun Li along with his colleagues Lin Huang, Yingtao Zeng and Qing Yang at UnicornTeam have previously found several automobile vulnerabilities in Volvo, BMW, Audi, Mercedes Benz and BYD’s GPS.
https://wn.com/Hitb2016Ams_D2T1_Cansee_An_Automobile_Intrusion_Detection_System_Jun_Li
"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)

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

  • Order:
  • Duration: 0:43
  • Updated: 19 Feb 2014
  • views: 54025
videos
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)
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
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