• Soon, Indian Army Will Stop Intrusions Using This Technology

    This video shows you that Soon, Indian Army Will Stop Intrusions Using This Technology. Indian defence will soon use predictive analytics to stop intrusions. Over the last 6 months, a Delhi-based startup CRON Systems — an Internet of Things-based border security startup has been building technology that will help the Army in predicting intrusions and prevent them. CRON Systems, co-founded by Tushar Chhabra, Saurav Agarwala, and Tommy Katzenellenbogen— is working at the borders to build the product based on Army’s requirements. On a call from border area of high terrain with almost no cellular network zone Chhabra explained, “We have found three painpoints that they face daily - There is no communication channel and with lack of infrastructure they cannot install new-age products and mo...

    published: 12 Apr 2018
  • KDD99 - Machine Learning for Intrusion Detectors from attacking data

    Machine Learning for Intrusion Detectors from attacking data

    published: 05 May 2015
  • Soon, Indian Army will stop intrusions using this technology

    Indian defence will soon use predictive analytics to stop intrusions. Over the last 6 months, a Delhi-based startup CRON Systems — an Internet of Things-based border security startup has been building technology that will help the Army in predicting intrusions and prevent them. CRON Systems, co-founded by Tushar Chhabra, Saurav Agarwala, and Tommy Katzenellenbogen— is working at the borders to build the product based on Army’s requirements. On a call from border area of high terrain with almost no cellular network zone Chhabra explained, “We have found three painpoints that they face daily - There is no communication channel and with lack of infrastructure they cannot install new-age products and most of the time it becomes too complicated for end user that they cannot even use it.” So...

    published: 13 Apr 2018
  • Intrusion Detection based on KDD Cup Dataset

    Final Presentation for Big Data Analysis

    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
  • 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
  • chongshm Destroy All Illegal network intrusions with big data techs

    KDDCUP 99 by Chongshen Ma, Carnegie Mellon University.

    published: 05 May 2015
  • Data Science Capstone Project "Network Intrusion Detection"

    Contributed by Ho Fai Wong, Joseph Wang, Radhey Shyam, & Wanda Wang. They enrolled in the NYC Data Science Academy 12-Week Data Science Bootcamp taking place between April 11th to July 1st, 2016. This post is based on their final class project - Capstone, due on the 12th week of the program. Network intrusions have become commonplace today, with enterprises and governmental organizations fully recognizing the need for accurate and efficient network intrusion detection, while balancing network security and network reliability. Our Capstone project tackled exactly this challenge: applying machine learning models for network intrusion detection. Learn more: http://blog.nycdatascience.com/r/network-intrusion-detection/

    published: 03 Aug 2016
  • Final Year Projects | Effective Analysis of KDD data for Intrusion Detection

    Final Year Projects | Effective Analysis of KDD data for Intrusion Detection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get...

    published: 28 May 2013
  • Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong

    Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST)...

    published: 01 Dec 2016
  • DATA Ex-Filtration - NIDS Bypass - Transfering shellcode using ARP packets (Python Raw sockets demo)

    published: 28 Sep 2017
  • Security Onion Training 101: Part 2 - Intrusion Detection and Network Analysis

    Please check out my Udemy courses! Coupon code applied to following links.... Kali Linux Hands-on Penetration Testing Labs: https://www.udemy.com/kali-linux-hands-on-penetration-testing-labs/?couponCode=TENDOLLARS Network Security Analysis Using Wireshark, Snort, and SO: https://www.udemy.com/network-security-analysis-using-wireshark-snort-and-so/?couponCode=TENDOLLARS Snort Intrusion Detection, Rule Writing, and PCAP Analysis: https://www.udemy.com/snort-intrusion-detection-rule-writing-and-pcap-analysis/?couponCode=TENDOLLARS Description: This video will cover how to prompt alerts within Security Onion by performing remote attacks against a Ubuntu server (Metasploitable) using Armitage, which is a GUI front end for Metasploit within Kali Linux. Subsequently, there will be a de...

    published: 08 Aug 2016
  • RHAPIS - NIDS Simulator (Network Intrusion Detection Systems Simulator)

    RHAPIS is a simulator which imitates the actions of a network intrusion detection system. Download RHAPIS (IDS Simulator) on http://rhapis-data.appspot.com intrusion detection simulator ids simulation intrusion detection network intrusion detection ids/nids intrusion detection systems simulator ids simulator rhapis simulator intrusion detection simulation software detection system simulator rhapis intruder simulation traffic generation intrusion detection evaluation datasets ids evaluation datasets virtual attacks intrusion detection simulation fake attacks attacker simulator network intrusion detection simulator nids simulator network intrusion simulation ids simulation engine intrusion data simulator network traffic simulator intrusion simulation

    published: 16 Feb 2014
  • Catchr - Secretly Detect Intrusions

    App Store Link: http://bit.ly/GetCatchrI App Page Link: http://www.getcatchr.com ••••• Special launch price -- 33% off for a limited time ••••• Catchr provides the opportunity to subtly detect if somebody else has been going through your phone while it was out of sight. It detects this by monitoring applications that have been started or terminated while also recording the duration of the actions that took place during the owner's absence. This makes it a personal "privacy guardian", ensuring that private stuff stays private.

    published: 10 Feb 2014
  • Intrusion Detection System Using Machine Learning Models

    published: 16 Jul 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
  • 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
  • How to Improve the Performance of SVM Classifier | Data Mining

    Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimise the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depend upon data which is generated by SVM Classifier. So when performance of SVM classifier will improve then our results will be closer to the facts automatically.

    published: 05 Jul 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
  • Paper Data Mining for Network Intrusion Detection

    كةمبيني بةكوردي كردني زانست لة زانكؤي كةشةبيَداني مرؤيي

    published: 13 May 2014
  • What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning

    What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare ...

    published: 14 Feb 2017
  • 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
  • IDS - Intrusion Detection System

    IDS - Intrusion Detection System 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 syste...

    published: 23 Jan 2017
  • Real-time Alert Management and Detection of Saltwater Intrusion in Aquifers

    Our natural water resources are not infinite. Overexploitation (due to overpopulation, industrial and agricultural policies or mass tourism) is leading to a gradual and sometimes irreversible damage to our water resources. http://www.imageau.eu/en_EN/index.php This risk is particularly acute in coastal areas where excessive strain on the water supply is causing salt intrusion, thereby damaging pumping stations and threatening the quality of groundwater. Only a targeted, continuous and proactive management of these aquifers (done in-situ) can prevent this threat and avoid an expensive, lengthy and difficult remediation process. The imaGeau innovative technology can address this issue by proposing the following: - A continuous and dynamic monitoring of the quality and availability of wat...

    published: 05 Feb 2012
developed with YouTube
Soon, Indian Army Will Stop Intrusions Using This Technology
4:00

Soon, Indian Army Will Stop Intrusions Using This Technology

  • Order:
  • Duration: 4:00
  • Updated: 12 Apr 2018
  • views: 2978
videos
This video shows you that Soon, Indian Army Will Stop Intrusions Using This Technology. Indian defence will soon use predictive analytics to stop intrusions. Over the last 6 months, a Delhi-based startup CRON Systems — an Internet of Things-based border security startup has been building technology that will help the Army in predicting intrusions and prevent them. CRON Systems, co-founded by Tushar Chhabra, Saurav Agarwala, and Tommy Katzenellenbogen— is working at the borders to build the product based on Army’s requirements. On a call from border area of high terrain with almost no cellular network zone Chhabra explained, “We have found three painpoints that they face daily - There is no communication channel and with lack of infrastructure they cannot install new-age products and most of the time it becomes too complicated for end user that they cannot even use it.” So, CRON has installed multiple sensors used in the polls which includes Sensor Fusion of Active Infra-Red, Passive Infra Red, Microwave and IR Cameras with Day and Night Capability, thermal cameras for accurate intrusion verification. These polls have a range of 100-200 meters which is capable of recognizing friends or foe movement. As soon as an intrusion happens or a movement is detected beyond the perimeter/border an alarm is raised, detected by these sensors and an alert is sent to an analytics platform, through an encrypted wireless communication system. Simultaneously, the nearest drone and rover are sent an alert for live surveillance & visual verification. However, when someone blocks this communication, the object is differentiated if its an animal or a human. Instantly, data is transmitted to each station where the jawan then has to press a button saying either he is in trouble or secure. “Collecting such data points, the platform is creating descriptive analytics, which probably in the next 5-6 years will help you predict the threat around the perimeter before an attack likely happens. For instance, if he is carrying any we@pon, the light radar and thermal technology will detect it. He explains over the last two-three years, through their data points they found that during November, intrusions usually spike up. But also, before an intrusion happens lot of activities take place. “Such data points help in predicting these intrusions.” ====================================================================================================== DISCLAIMER: Each and every content used in this video is not imaginary. All are taken from reputed news agencies. This video doesn’t meant to hurt anybody's personal feelings,beliefs and religion. We are not responsible for any of these statements used in this video. If you have any suggestion or query regarding this video, you can contact me on YouTube personal Message and you can send me message in my Facebook page. Thank you & regards Global conflicts ====================================================================================================== Channel Link: https://www.youtube.com/c/Globalconflict7 Facebook: https://www.facebook.com/GlobalConflict7/ Fan Page: https://www.facebook.com/globalconflict/ Twitter: https://twitter.com/Gl0balC0nflict ======================================================================================================
https://wn.com/Soon,_Indian_Army_Will_Stop_Intrusions_Using_This_Technology
KDD99 - Machine Learning for Intrusion Detectors from attacking data
45:56

KDD99 - Machine Learning for Intrusion Detectors from attacking data

  • Order:
  • Duration: 45:56
  • Updated: 05 May 2015
  • views: 3069
videos https://wn.com/Kdd99_Machine_Learning_For_Intrusion_Detectors_From_Attacking_Data
Soon, Indian Army will stop intrusions using this technology
3:46

Soon, Indian Army will stop intrusions using this technology

  • Order:
  • Duration: 3:46
  • Updated: 13 Apr 2018
  • views: 544
videos
Indian defence will soon use predictive analytics to stop intrusions. Over the last 6 months, a Delhi-based startup CRON Systems — an Internet of Things-based border security startup has been building technology that will help the Army in predicting intrusions and prevent them. CRON Systems, co-founded by Tushar Chhabra, Saurav Agarwala, and Tommy Katzenellenbogen— is working at the borders to build the product based on Army’s requirements. On a call from border area of high terrain with almost no cellular network zone Chhabra explained, “We have found three painpoints that they face daily - There is no communication channel and with lack of infrastructure they cannot install new-age products and most of the time it becomes too complicated for end user that they cannot even use it.” So, CRON has installed multiple sensors used in the polls which includes Sensor Fusion of Active Infra-Red, Passive Infra Red, Microwave and IR Cameras with Day and Night Capability, thermal cameras for accurate intrusion verification. These polls have a range of 100-200 meters which is capable of recognizing friends or foe movement. As soon as an intrusion happens or a movement is detected beyond the perimeter/border an alarm is raised, detected by these sensors and an alert is sent to an analytics platform, through an encrypted wireless communication system. Simultaneously, the nearest drone and rover are sent an alert for live surveillance & visual verification. However, when someone blocks this communication, the object is differentiated if its an animal or a human. Instantly, data is transmitted to each station where the jawan then has to press a button saying either he is in trouble or secure. “Collecting such data points, the platform is creating descriptive analytics, which probably in the next 5-6 years will help you predict the threat around the perimeter before an attack likely happens. For instance, if he is carrying any weapon, the light radar and thermal technology will detect it. He explains over the last two-three years, through their data points they found that during November, intrusions usually spike up. But also, before an intrusion happens lot of activities take place. “Such data points help in predicting these intrusions.” Chabbra adds, “We also realized just checking in line will not solve the problem but we need to look at the counterpart’s territory. All perimeters have different threats so a camp in Kashmir will have altogether a different set of data point from an Indo-Nepal border and even the level of threats are different. For example in the Indo-Nepal border, a person will always try to move alone so that he isn’t caught whereas in Kashmir territory while doing areas inspection, they will move in larger groups. Proximity to the perimeter is another crucial criteria. In heavy threat areas, proximity will be very less around 10-12 ft and it will be around months before the attack whereas otherwise, it will be 3 days before and around 100-200 metres away. Source :- Tech ET Background Music :- bensound.com Disclaimer- This channel is for defence related news worldwide . We try to give you true news related to each and every aspects of defence . It is either country, defence weapon, air Force, army ,navy, military or anything we will try to fully explain . The content specially news we upload are taken from various news channels and media houses . we never claim it is 100 % on our behalf but we try to deliver you exact without rumours . our news is specially related to india . As India is a growing country specially in defence under narendra modi BJP government . Channel Link: https://www.youtube.com/DefenceTube Facebook Link: https://www.facebook.com/defencetube Twitter Link : https://twitter.com/DefenceTube Check my all playlist : https://www.youtube.com/defencetube/playlist
https://wn.com/Soon,_Indian_Army_Will_Stop_Intrusions_Using_This_Technology
Intrusion Detection based on KDD Cup Dataset
18:41

Intrusion Detection based on KDD Cup Dataset

  • Order:
  • Duration: 18:41
  • Updated: 05 May 2015
  • views: 5430
videos https://wn.com/Intrusion_Detection_Based_On_Kdd_Cup_Dataset
Intrusion Detection System Introduction, Types of Intruders in Hindi with Example
9:07

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

  • Order:
  • Duration: 9:07
  • Updated: 06 Dec 2016
  • views: 30538
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
Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques
49:38

Detecting Network Intrusions With Machine Learning Based Anomaly Detection Techniques

  • Order:
  • Duration: 49:38
  • Updated: 27 Jul 2015
  • views: 7435
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
chongshm Destroy All Illegal network intrusions with big data techs
26:50

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
Data Science Capstone Project "Network Intrusion Detection"
29:30

Data Science Capstone Project "Network Intrusion Detection"

  • Order:
  • Duration: 29:30
  • Updated: 03 Aug 2016
  • views: 256
videos
Contributed by Ho Fai Wong, Joseph Wang, Radhey Shyam, & Wanda Wang. They enrolled in the NYC Data Science Academy 12-Week Data Science Bootcamp taking place between April 11th to July 1st, 2016. This post is based on their final class project - Capstone, due on the 12th week of the program. Network intrusions have become commonplace today, with enterprises and governmental organizations fully recognizing the need for accurate and efficient network intrusion detection, while balancing network security and network reliability. Our Capstone project tackled exactly this challenge: applying machine learning models for network intrusion detection. Learn more: http://blog.nycdatascience.com/r/network-intrusion-detection/
https://wn.com/Data_Science_Capstone_Project_Network_Intrusion_Detection
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection
9:16

Final Year Projects | Effective Analysis of KDD data for Intrusion Detection

  • Order:
  • Duration: 9:16
  • Updated: 28 May 2013
  • views: 4083
videos
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: info@clickmyproject.com
https://wn.com/Final_Year_Projects_|_Effective_Analysis_Of_Kdd_Data_For_Intrusion_Detection
Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong
17:45

Machine Learning for Real-Time Anomaly Detection in Network Time-Series Data - Jaeseong Jeong

  • Order:
  • Duration: 17:45
  • Updated: 01 Dec 2016
  • views: 7523
videos
Real-time anomaly detection plays a key role in ensuring that the network operation is under control, by taking actions on detected anomalies. In this talk, we discuss a problem of the real-time anomaly detection on a non-stationary (i.e., seasonal) time-series data of several network KPIs. We present two anomaly detection algorithms leveraging machine learning techniques, both of which are able to adaptively learn the underlying seasonal patterns in the data. Jaeseong Jeong is a researcher at Ericsson Research, Machine Learning team. His research interests include large-scale machine learning, telecom data analytics, human behavior predictions, and algorithms for mobile networks. He received the B.S., M.S., and Ph.D. degrees from Korea Advanced Institute of Science and Technology (KAIST) in 2008, 2010, and 2014, respectively.
https://wn.com/Machine_Learning_For_Real_Time_Anomaly_Detection_In_Network_Time_Series_Data_Jaeseong_Jeong
DATA Ex-Filtration - NIDS Bypass - Transfering shellcode using ARP packets (Python Raw sockets demo)
13:47

DATA Ex-Filtration - NIDS Bypass - Transfering shellcode using ARP packets (Python Raw sockets demo)

  • Order:
  • Duration: 13:47
  • Updated: 28 Sep 2017
  • views: 443
videos
https://wn.com/Data_Ex_Filtration_Nids_Bypass_Transfering_Shellcode_Using_Arp_Packets_(Python_Raw_Sockets_Demo)
Security Onion Training 101: Part 2 - Intrusion Detection and Network Analysis
5:59

Security Onion Training 101: Part 2 - Intrusion Detection and Network Analysis

  • Order:
  • Duration: 5:59
  • Updated: 08 Aug 2016
  • views: 9238
videos
Please check out my Udemy courses! Coupon code applied to following links.... Kali Linux Hands-on Penetration Testing Labs: https://www.udemy.com/kali-linux-hands-on-penetration-testing-labs/?couponCode=TENDOLLARS Network Security Analysis Using Wireshark, Snort, and SO: https://www.udemy.com/network-security-analysis-using-wireshark-snort-and-so/?couponCode=TENDOLLARS Snort Intrusion Detection, Rule Writing, and PCAP Analysis: https://www.udemy.com/snort-intrusion-detection-rule-writing-and-pcap-analysis/?couponCode=TENDOLLARS Description: This video will cover how to prompt alerts within Security Onion by performing remote attacks against a Ubuntu server (Metasploitable) using Armitage, which is a GUI front end for Metasploit within Kali Linux. Subsequently, there will be a demonstration of how to interpret alerts and analyze the underlying network traffic using Wireshark. It's notable to mention that no real Trojan infection occurred. This was merely a demonstration of how analysis and reporting of potentially malicious traffic is performed. The following are websites which are pertinent to this video: https://sourceforge.net/projects/metasploitable/ https://github.com/Security-Onion-Solutions/security-onion/blob/master/Verify_ISO.md https://www.kali.org/downloads/
https://wn.com/Security_Onion_Training_101_Part_2_Intrusion_Detection_And_Network_Analysis
RHAPIS - NIDS Simulator (Network Intrusion Detection Systems Simulator)
19:38

RHAPIS - NIDS Simulator (Network Intrusion Detection Systems Simulator)

  • Order:
  • Duration: 19:38
  • Updated: 16 Feb 2014
  • views: 1652
videos
RHAPIS is a simulator which imitates the actions of a network intrusion detection system. Download RHAPIS (IDS Simulator) on http://rhapis-data.appspot.com intrusion detection simulator ids simulation intrusion detection network intrusion detection ids/nids intrusion detection systems simulator ids simulator rhapis simulator intrusion detection simulation software detection system simulator rhapis intruder simulation traffic generation intrusion detection evaluation datasets ids evaluation datasets virtual attacks intrusion detection simulation fake attacks attacker simulator network intrusion detection simulator nids simulator network intrusion simulation ids simulation engine intrusion data simulator network traffic simulator intrusion simulation
https://wn.com/Rhapis_Nids_Simulator_(Network_Intrusion_Detection_Systems_Simulator)
Catchr - Secretly Detect Intrusions
1:07

Catchr - Secretly Detect Intrusions

  • Order:
  • Duration: 1:07
  • Updated: 10 Feb 2014
  • views: 37431
videos
App Store Link: http://bit.ly/GetCatchrI App Page Link: http://www.getcatchr.com ••••• Special launch price -- 33% off for a limited time ••••• Catchr provides the opportunity to subtly detect if somebody else has been going through your phone while it was out of sight. It detects this by monitoring applications that have been started or terminated while also recording the duration of the actions that took place during the owner's absence. This makes it a personal "privacy guardian", ensuring that private stuff stays private.
https://wn.com/Catchr_Secretly_Detect_Intrusions
Intrusion Detection System Using Machine Learning Models
19:13

Intrusion Detection System Using Machine Learning Models

  • Order:
  • Duration: 19:13
  • Updated: 16 Jul 2015
  • views: 5464
videos
https://wn.com/Intrusion_Detection_System_Using_Machine_Learning_Models
Wireshark and Recognizing Exploits, HakTip 138
6:07

Wireshark and Recognizing Exploits, HakTip 138

  • Order:
  • Duration: 6:07
  • Updated: 12 Mar 2015
  • views: 33598
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. -~-~~-~~~-~~-~- Please watch: "Bash Bunny Primer - Hak5 2225" https://www.youtube.com/watch?v=8j6hrjSrJaM -~-~~-~~~-~~-~-
https://wn.com/Wireshark_And_Recognizing_Exploits,_Haktip_138
Intrusion Detection System Tutorial: Setup Security Onion
9:53

Intrusion Detection System Tutorial: Setup Security Onion

  • Order:
  • Duration: 9:53
  • Updated: 09 Aug 2016
  • views: 23341
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
How to Improve the Performance of SVM Classifier | Data Mining
14:28

How to Improve the Performance of SVM Classifier | Data Mining

  • Order:
  • Duration: 14:28
  • Updated: 05 Jul 2016
  • views: 4357
videos
Data Mining Algorithm and intrusion detection system IDS algorithm is being tested in NSL-KDD data-set. I have applied an adaptive learning technique to optimise the output this time... For making our system more efficient and able to generate more accurate result, it is necessary to improve the performance of SVM classifier. Because all the result’s accuracy depend upon data which is generated by SVM Classifier. So when performance of SVM classifier will improve then our results will be closer to the facts automatically.
https://wn.com/How_To_Improve_The_Performance_Of_Svm_Classifier_|_Data_Mining
"We Watch You While You Sleep". TV signal intrusion 1975 (Scarfnada TV)
0:43

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

  • Order:
  • Duration: 0:43
  • Updated: 19 Feb 2014
  • views: 63887
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)
Paper Data Mining for Network Intrusion Detection
8:08

Paper Data Mining for Network Intrusion Detection

  • Order:
  • Duration: 8:08
  • Updated: 13 May 2014
  • views: 161
videos
كةمبيني بةكوردي كردني زانست لة زانكؤي كةشةبيَداني مرؤيي
https://wn.com/Paper_Data_Mining_For_Network_Intrusion_Detection
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
2:18

What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning

  • Order:
  • Duration: 2:18
  • Updated: 14 Feb 2017
  • views: 2517
videos
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
https://wn.com/What_Is_Anomaly_Detection_What_Does_Anomaly_Detection_Mean_Anomaly_Detection_Meaning
Optical Encryption: Is your data fully protected?
2:02

Optical Encryption: Is your data fully protected?

  • Order:
  • Duration: 2:02
  • Updated: 20 Jan 2016
  • views: 1354
videos
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
IDS - Intrusion Detection System
5:51

IDS - Intrusion Detection System

  • Order:
  • Duration: 5:51
  • Updated: 23 Jan 2017
  • views: 2328
videos
IDS - Intrusion Detection System 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
https://wn.com/Ids_Intrusion_Detection_System
Real-time Alert Management and Detection of Saltwater Intrusion in Aquifers
3:12

Real-time Alert Management and Detection of Saltwater Intrusion in Aquifers

  • Order:
  • Duration: 3:12
  • Updated: 05 Feb 2012
  • views: 7346
videos
Our natural water resources are not infinite. Overexploitation (due to overpopulation, industrial and agricultural policies or mass tourism) is leading to a gradual and sometimes irreversible damage to our water resources. http://www.imageau.eu/en_EN/index.php This risk is particularly acute in coastal areas where excessive strain on the water supply is causing salt intrusion, thereby damaging pumping stations and threatening the quality of groundwater. Only a targeted, continuous and proactive management of these aquifers (done in-situ) can prevent this threat and avoid an expensive, lengthy and difficult remediation process. The imaGeau innovative technology can address this issue by proposing the following: - A continuous and dynamic monitoring of the quality and availability of water resources - A high frequency time series and historical data log over several months or years - Online access to groundwater data for monitoring and sustainable management.
https://wn.com/Real_Time_Alert_Management_And_Detection_Of_Saltwater_Intrusion_In_Aquifers
×