New Intrusion Detection Technology Driven by Artificial Intelligence

Dear Colleagues,

Intrusion detection first needs to extract important features in the computer system and network, then compare and analyze these features with normal features and known intrusion features and find in advance any potential intrusion that might cause harm to the computer system and network. Early detection of intrusion will thwart attackers by adopting appropriate security measures to eliminate the impending threat. Therefore, intrusion detection is a key research focus and an important aspect in the field of computer and network security. The recent Cyber Incident Response Reports show an increased level of intrusion during the pandemic, which further underpins the importance of this research. However, with the continuous emergence of new network architectures such as the Internet of Things (IoT) and software-defined networks (SDN), a large number of intelligent terminals and heterogeneous IoT devices are deployed in the network. The openness of many new network architectures and the limited computing resources of terminal devices will bring certain threats and challenges to the security of existing networks. The emergence of new artificial intelligence technology has brought innovative solutions to intrusion detection. Determining how to use new artificial intelligence technologies such as deep learning and pattern recognition to improve the accuracy of intrusion detection and reduce its time complexity is still an open problem.

Our Special Issue will serve as a forum to bring together active researchers all over the world to share their recent advances in intrusion detection based on artificial intelligence in different aspects. Our targets include: (1) state-of-the-art theories and novel applications in intrusion detection model based on deep learning; (2) novel intrusion detection framework based on artificial intelligence; (3) intrusion detection methods based on artificial intelligence in a specific environment (such as IoT or edge computing environment); (4) new intelligent optimization methods of artificial intelligence intrusion detection; (5) analyses and studies on the behavioral characteristics of network traffic and new traffic identification method based deep learning; and (6) survey articles reporting recent progress in intrusion detection methods based on artificial intelligence.

Potential topics include but are not limited to the following:

• Innovative intrusion detection models based on deep learning;
• Novel intrusion detection framework based on Artificial Intelligence;
• Intrusion detection method based on artificial intelligence for the Internet of Things;
• Intrusion detection method based on artificial intelligence for edge computing;
• New intelligent optimization methods for intrusion detection based on artificial intelligence;
• Novel traffic identification methods based on deep learning;
• How to mine and select more effective network behavior features to identify malicious network traffic;
• New network abnormal traffic detection models based on deep learning/reinforcement learning;
• How to build new intrusion detection benchmark data sets;
• Hybrid/integrated deep learning model for efficient intrusion detection in big data environment;
• Novel intrusion detection models based on machine learning/deep learning using blockchain;
• Encrypted traffic identification in high-speed network environment;
• How to address the data imbalance problem in building intrusion detection models;
• Comprehensive survey articles on recent intrusion detection techniques highlighting future challenges.

Prof. Dr. Shi Dong
Prof. Dr. Joarder Kamruzzaman
Dr. Guanfeng Liu
Guest Editors

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