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Telecom Network & SIEM logs analysis using machine learning

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ALTANAI BISHT
ALTANAI BISHT

This document summarizes a presentation on analyzing telecom network and SIEM logs using machine learning. The presentation discusses: 1. The growing field of telecommunications technologies and the increased demand during the COVID-19 pandemic. 2. The need for security event management in telecom networks given risks like intrusions, fraud, and ransomware attacks. 3. How to implement dynamic security event management using machine learning by gathering data from various logs and analyzing patterns in real-time to detect anomalies and intrusions.

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Telecom Network & SIEM Logs Analysis using Machine Learning - Altanai Presented at Machine Learning for Cyber Security & Digital Forensics - Digital Forensics (4N6) in 2020
HELLO! I am Altanai Specialised in CPaaS, carrier-grade WebRTC-SIP telecom platforms Author of WebRTC Integrator's Guide, https://www.packtpub.com/in/web-development/webrtc-integrators-guide. 2
3 10 yrs in Telecom + Voice Over IP + Media streaming + Communication as a Service Freelancer , Open source contributor and blogger https://telecom.altanai.com/ Author of WebRTC Integrator’s Guide Inventor of RamuDroid ( Bot to clean roads and outdoors ) currently with Airtel Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
1. Rich and growing world of Telecommunication technologies - Unified Communication and Collaboration client (UCC) - High level view of device agnostic Communication as a service provider - SIP ( Session Initiation Protocol) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
“Covid-19 impact on telecoms - Demand Skyrocketed on telcos and OTTs - VoD video on demand and Media platform - Remote office working - elearning 5 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
6 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
7 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
SIP ( Session Initiation protocol) 8
2. Security event Management and Telecom Networks - Intrusions and Hacks on Telecom and Communication platforms - Fraud and Ransomware in Communication Service Industry - Need for Security information and event management (SIEM) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Intrusions and Hacks on Telecom and Communication platforms 10 https://telecom.economictimes.indiatimes.com/news/bsnl-intranet-hacked-company-fixes-leak-after-alert-by-re searcher/63159693 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Fraud and Ransomware in Communication Service Industry 11 https://www.computerweekly.com/news/450415866/Nearly-a-third- of-malware-attacks-are-zero-day-exploits https://www.vyopta.com/blog/business -collaboration/telecommunications-sec urity-vulnerabilties/ Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
“..telecommunications industry experienced $38.1 billion in fraudulent charges in 2016. - Communication and Fraud Control Association ( https://www.cfca.org/) 12 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Hacking in to a VoIP System 13 - Vulnerabilities via network firewalls - Lack of ACL - Device / Endpoint Vulnerabilities like malicious app on phone - Server backdoor entry - Leaked passwords or Pem keys 03 01 02 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai Intrusions Classes ● denial of service(DoS) ● unauthorized access from a remote machine (R2L) ● unauthorized access to local superuser (root) privileges (U2R) ● probing (PRB)
Risk to Operation of a communication provider 14 Toll frauds on international calling Private Call Record Data leaks Ransomware DDOS Subscript ion fraud Eavesdropping Ip/PBX takeover Dealer Fraud Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
SS7 and Diameter Signalling Threats Phone endpoint Hacking SIP / H3.23 Toll Fraud One ring and Cut to generate Call back revenue Blind Call Transfers SPIT Points of Concern in CPaaS or VoIP Network User Authentication Hacking Browser based Click to Call Malicious Chrome extensions Call Cards Vishing VOMIT 15 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
2. Dynamic Security event Management using Machine Learning - Types of Security Managements - HOMER and heplify voip monitoring - Log alerts using elastic Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Types of Security Managements - Statistical correlation engine like SIEM - User and entity behavior analytics (UEBA) - Security orchestration, automation and response (SOAR) - Dynamic/Active ML based Protection - Adaptive / Proactive security - Continuous Risk scoring - Updated blocklist for alerted accounts - Outsourcing to MSSP (managed security service providers) 17 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
“ 18 Gathering Data and Logs - VoIP /SIP traces , protocol sniffer - Logs ( audit , system , services , networks ) - RTP / RTCP media traces - Call Data records Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
19 HEP / EEP encapsulation protocol of HOMER Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
HOMER in operation 20 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
21 Real Time Monitoring - SIPCAPTURE Credits : https://sipcapture.org/ Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
“Device Authentication and Call Pattern - Static / Rule based engine - SIP trunks interaction with VoIP PBX gateways based on thresholds - Registration flooding - Unknown protocol packets - Detect attacks signature based on historical analysis - Detect abrupt change in routing plans 22 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
“ 23 Real Time VoIP Traffic Analysis - Supervised learning on labelled use cases via training dataset - source /destination ip address geolocations so on - Unsupervised Machine learning for non Linear Classification of Calls - K-means clustering ( others include Bayes Network, Random Forest, Random Tree, MLP, Decision Table ) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Intrusion Detection System (IDS) for VoIP Accounts 24 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Custom IDS ( Intrusion Detection System ) 25 - Signature-based intrusion detection - Anomaly-based intrusion detection Usecase Driven - Call Centre - Remote office collaboration - Sales Calls - OTP calls - Anonymous calling - Secure / financial / bank calls - TeleMedicine - Video KYC
3. Solutions 26 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
Demo 27 https://www.kaggle.com/altanai/network-intrusion-detection-using-python/ Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
28
29
Applications of ANN Aggregate data from honeypot application and traffic monitoring to neural network Extensive testbed experimentation for Responding to Attacks Back propagation Neural Network to detect SPAM calls 30 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
4. Key Takeaways - High availability is critical - Honeypots to observe behaviour of crawlers and hackers - Traffic Pattern and Behavior analysis - Hacks are more common in off hours like early morning or holiday season 31 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
CREDITS Special thanks to all the people who made and released these awesome resources for free: ▹ Presentation template by SlidesCarnival ▹ Photographs by Unsplash 32
References This presentation uses the following references: ▹ Communication And Fraud Prevention https://www.cfca.org/ ▹ ForcePoint What is SIEM? Defined, Explained, and Explored ▹ Techtarget is SIEM and Why is it Important? ▹ Medium - Evaluation of Machine Learning Algorithms for Intrusion Detection System by Cuelogic Technologies ▹ Zamani, Mahdi. (2013). Machine Learning Techniques for Intrusion Detection. ▹ QoS Evaluation Based on Extend E-Model in VoIP Hongli Zhangab, Zhimin Gua , Zhenqing Tianb a School of Computer Science and Technology, Beijing Institute of Technology b Media College, Inner Mongolia Normal University ,China ▹ Voice quality prediction models and their application in VoIP networks - September 2006 IEEE Transactions on Multimedia DOI: 10.1109/TMM.2006.876279 , Lingfen Sun, Emmanuel Ifeachor assar, Mohamed & State, Radu & Festor, Olivier. (2007). VoIP Honeypot Architecture. 109 - 118. 10.1109/INM.2007.374775. ▹ Telecom RnD https://telecom.altanai.com/ ▸ VOIP Call Metric Monitoring and MOS ( Mean Opinion Score) ▸ OTT ( Over the Top ) Communication applications ▸ CLI/NCLI, Robocalls and STIR/SHAKEN 33
THANKS! Any questions? You can find me at @altanai https://telecom.altanai.com/ 34

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Telecom Network & SIEM logs analysis using machine learning

  • 1. Telecom Network & SIEM Logs Analysis using Machine Learning - Altanai Presented at Machine Learning for Cyber Security & Digital Forensics - Digital Forensics (4N6) in 2020
  • 2. HELLO! I am Altanai Specialised in CPaaS, carrier-grade WebRTC-SIP telecom platforms Author of WebRTC Integrator's Guide, https://www.packtpub.com/in/web-development/webrtc-integrators-guide. 2
  • 3. 3 10 yrs in Telecom + Voice Over IP + Media streaming + Communication as a Service Freelancer , Open source contributor and blogger https://telecom.altanai.com/ Author of WebRTC Integrator’s Guide Inventor of RamuDroid ( Bot to clean roads and outdoors ) currently with Airtel Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 4. 1. Rich and growing world of Telecommunication technologies - Unified Communication and Collaboration client (UCC) - High level view of device agnostic Communication as a service provider - SIP ( Session Initiation Protocol) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 5. “Covid-19 impact on telecoms - Demand Skyrocketed on telcos and OTTs - VoD video on demand and Media platform - Remote office working - elearning 5 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 6. 6 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 7. 7 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 8. SIP ( Session Initiation protocol) 8
  • 9. 2. Security event Management and Telecom Networks - Intrusions and Hacks on Telecom and Communication platforms - Fraud and Ransomware in Communication Service Industry - Need for Security information and event management (SIEM) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 10. Intrusions and Hacks on Telecom and Communication platforms 10 https://telecom.economictimes.indiatimes.com/news/bsnl-intranet-hacked-company-fixes-leak-after-alert-by-re searcher/63159693 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 11. Fraud and Ransomware in Communication Service Industry 11 https://www.computerweekly.com/news/450415866/Nearly-a-third- of-malware-attacks-are-zero-day-exploits https://www.vyopta.com/blog/business -collaboration/telecommunications-sec urity-vulnerabilties/ Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 12. “..telecommunications industry experienced $38.1 billion in fraudulent charges in 2016. - Communication and Fraud Control Association ( https://www.cfca.org/) 12 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 13. Hacking in to a VoIP System 13 - Vulnerabilities via network firewalls - Lack of ACL - Device / Endpoint Vulnerabilities like malicious app on phone - Server backdoor entry - Leaked passwords or Pem keys 03 01 02 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai Intrusions Classes ● denial of service(DoS) ● unauthorized access from a remote machine (R2L) ● unauthorized access to local superuser (root) privileges (U2R) ● probing (PRB)
  • 14. Risk to Operation of a communication provider 14 Toll frauds on international calling Private Call Record Data leaks Ransomware DDOS Subscript ion fraud Eavesdropping Ip/PBX takeover Dealer Fraud Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 15. SS7 and Diameter Signalling Threats Phone endpoint Hacking SIP / H3.23 Toll Fraud One ring and Cut to generate Call back revenue Blind Call Transfers SPIT Points of Concern in CPaaS or VoIP Network User Authentication Hacking Browser based Click to Call Malicious Chrome extensions Call Cards Vishing VOMIT 15 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 16. 2. Dynamic Security event Management using Machine Learning - Types of Security Managements - HOMER and heplify voip monitoring - Log alerts using elastic Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 17. Types of Security Managements - Statistical correlation engine like SIEM - User and entity behavior analytics (UEBA) - Security orchestration, automation and response (SOAR) - Dynamic/Active ML based Protection - Adaptive / Proactive security - Continuous Risk scoring - Updated blocklist for alerted accounts - Outsourcing to MSSP (managed security service providers) 17 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 18. “ 18 Gathering Data and Logs - VoIP /SIP traces , protocol sniffer - Logs ( audit , system , services , networks ) - RTP / RTCP media traces - Call Data records Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 19. 19 HEP / EEP encapsulation protocol of HOMER Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 20. HOMER in operation 20 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 21. 21 Real Time Monitoring - SIPCAPTURE Credits : https://sipcapture.org/ Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 22. “Device Authentication and Call Pattern - Static / Rule based engine - SIP trunks interaction with VoIP PBX gateways based on thresholds - Registration flooding - Unknown protocol packets - Detect attacks signature based on historical analysis - Detect abrupt change in routing plans 22 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 23. “ 23 Real Time VoIP Traffic Analysis - Supervised learning on labelled use cases via training dataset - source /destination ip address geolocations so on - Unsupervised Machine learning for non Linear Classification of Calls - K-means clustering ( others include Bayes Network, Random Forest, Random Tree, MLP, Decision Table ) Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 24. Intrusion Detection System (IDS) for VoIP Accounts 24 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 25. Custom IDS ( Intrusion Detection System ) 25 - Signature-based intrusion detection - Anomaly-based intrusion detection Usecase Driven - Call Centre - Remote office collaboration - Sales Calls - OTP calls - Anonymous calling - Secure / financial / bank calls - TeleMedicine - Video KYC
  • 26. 3. Solutions 26 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 28. 28
  • 29. 29
  • 30. Applications of ANN Aggregate data from honeypot application and traffic monitoring to neural network Extensive testbed experimentation for Responding to Attacks Back propagation Neural Network to detect SPAM calls 30 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 31. 4. Key Takeaways - High availability is critical - Honeypots to observe behaviour of crawlers and hackers - Traffic Pattern and Behavior analysis - Hacks are more common in off hours like early morning or holiday season 31 Machine Learning for Cyber Security & Digital Forensics - talk by @Altanai
  • 32. CREDITS Special thanks to all the people who made and released these awesome resources for free: ▹ Presentation template by SlidesCarnival ▹ Photographs by Unsplash 32
  • 33. References This presentation uses the following references: ▹ Communication And Fraud Prevention https://www.cfca.org/ ▹ ForcePoint What is SIEM? Defined, Explained, and Explored ▹ Techtarget is SIEM and Why is it Important? ▹ Medium - Evaluation of Machine Learning Algorithms for Intrusion Detection System by Cuelogic Technologies ▹ Zamani, Mahdi. (2013). Machine Learning Techniques for Intrusion Detection. ▹ QoS Evaluation Based on Extend E-Model in VoIP Hongli Zhangab, Zhimin Gua , Zhenqing Tianb a School of Computer Science and Technology, Beijing Institute of Technology b Media College, Inner Mongolia Normal University ,China ▹ Voice quality prediction models and their application in VoIP networks - September 2006 IEEE Transactions on Multimedia DOI: 10.1109/TMM.2006.876279 , Lingfen Sun, Emmanuel Ifeachor assar, Mohamed & State, Radu & Festor, Olivier. (2007). VoIP Honeypot Architecture. 109 - 118. 10.1109/INM.2007.374775. ▹ Telecom RnD https://telecom.altanai.com/ ▸ VOIP Call Metric Monitoring and MOS ( Mean Opinion Score) ▸ OTT ( Over the Top ) Communication applications ▸ CLI/NCLI, Robocalls and STIR/SHAKEN 33
  • 34. THANKS! Any questions? You can find me at @altanai https://telecom.altanai.com/ 34