The era of the Internet of Things with billions of connected devices has created an ever larger surface for cyber attackers to exploit, which has resulted in the need for fast and accurate detection of those attacks. Information and communication technology (ICT) operations evolve from operator-based management to autonomous and cognitive control. Security concerns are highlighted as an important obstacle in this transformation. While human factors lead to unnecessary flaws, autonomous management through softwarization and virtualization introduce new threats. The developments in mobile computing, communications, and mass storage architectures in the past decade have brought about the phenomenon of big data, which involves unprecedented amounts of valuable data generated in various forms at a high speed. The ability to process these massive amounts of data in real time using machine learning brings along many benefits that could be utilized in cyber threat analysis systems. By making use of big data collected from networks, computers, sensors, and cloud systems, cyber threat analysts and intrusion detection/prevention systems can discover useful information in real time. This information can help detect system vulnerabilities and attacks that are becoming prevalent and develop security solutions accordingly.
This workshop will focus on machine learning solutions for the security problems in a wide array of computerized systems and networks. Original research papers are welcome on topics including but not limited to:
- Distributed learning approaches for security
- AI-assisted security for future networks
- Internet of Things (IoT) security
- ML for detecting covert channels
- Open source security datasets for ML
- Big data for security and network management
- Cognitive network management using ML
- ML for cryptography
- Forensic analysis using ML
- Security data science
- Security analytics
- Data-driven threat intelligence
- ML and data mining for network, end-point and application protection
- Anomaly detection
- Malicious use of ML and AI
- Stream data processing and analytics for security
- Privacy-preserving ML
- ML approaches for SDN security
Important Dates:
Deadline for paper submission: 30 May 2019 (Final extension)
Notification of acceptance: 21 June 2019
Final manuscripts due: 5 July 2019
Workshop date: Sunday, 8 September 2019
Submission Site: https://edas.info/newPaper.php?c=26020&track=96361