DMCS 2017 - First workshop on Data Mining for Cyber Security

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, this workshop is a continuation of last year’s DMCS workshop held at ICDM’2016 in Barcelona which paved the way to a special issue on Data Mining for Cyber Security at IEEE’s Intelligent Systems Journal (in preparation). The workshop provides an opportunity to present and discuss the latest theoretical advances and real-world applications in this research field. Manuscripts are solicited to address a wide range of topics in this area, including but not limited to:
- 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
- Detecting compromised IoT devices
- Detecting malicious documents
- Detecting security threats in social networks