Artificial Intelligence (AI) has started to deeply influence people’s daily lives. AI-driven security solutions have already found applications in next generation firewalls, automatic intrusion detection systems, encrypted traffic identification, malware detection, and so on. Researchers are now assisted by AI-driven solutions to optimize algorithm design and release cryptanalysis efforts. Also, automatic data protection solutions based on deep learning technology have recently started appearing in academia. Conversely, an individual’s privacy is becoming under great threat given AI-based cyberattacks. The rise of AI-enabled cyberattacks could cause an explosion of network penetration, personal data theft, and epidemic spread of intelligent computer viruses. This raises the concern to defend against AI-driven attacks. Subsequently, AI-driven technologies require a training process, introducing additional problems of protecting training data and algorithms. Many machine learning and deep learning models have shown to be vulnerable against well-designed adversarial input samples. Outsourcing data and algorithms for training requires integrity in the training stage. Furthermore, end user data privacy and learning models must be protected. Thus, privacy preservation of big data in AI is becoming a key issue in the field of cyber security.
Topics of interest include (but are not limited to):
•Applications of AI technologies in cyber security and privacy
•Security-related big data capture, classification and analytics
•Data mining and knowledge discovery for security
•Intelligent systems for security and privacy
•Theoretical studies on big data privacy and security
•Privacy-preserving machine/deep learning and data mining