Submission deadline: April 15, 2017
Notification of acceptance: September 9, 2017
Computer and communication systems are subject to repeated security attacks. Given the variety of new vulnerabilities discovered every day, the introduction of new attack schemes, and the ever-expanding use of the Internet, it is not surprising that the field of computer and network security has grown and evolved significantly in recent years. Attacks are so pervasive nowadays that many firms, especially large financial institutions, spend over 10% of their total information and communication technology (ICT) budget directly on computer and network security. Changes in the type of attacks, such as the use of Advanced Persistent Threat (APT) and the identification of new vulnerabilities have resulted in a highly dynamic threat landscape that is unamenable to traditional security approaches.
Data mining techniques that explore data in order to discover hidden patterns and develop predictive models, have proven to be effective in tackling the aforementioned information security challenges. In recent years classification, associations rules, and clustering mechanisms, have all been used to discover and generalize attack patterns in order to develop powerful solutions for coping with the latest threats such as: APTs, Ransomware, data leakage, and malicious code (Trojan, Worms and computer viruses).
Focusing on the theoretical and practical aspects of data mining for enhancing information security, the topics of interest include (but are not limited to) the following:
Data mining for intrusion detection and prevention
Data mining for fraud detection and prevention
Monitoring Network Security
One-class based anomaly detection
Data Stream Mining for Security
Deep Learning for cyber security
Big Data architectures for network security
Identify theft detection and prevention
Evaluating data mining approaches to security
Adversarial Machine Learning
Detecting data and information leakage using data mining techniques
Detecting malicious code using data mining techniques
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• Submission: April 15, 2017
• Notification of acceptance: September 9, 2017
Nathalie Japkowicz and Yuval Elovici
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