Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

Information Fusion for Anomaly Detection with the Dendritic Cell Algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a Dendritic Cell Algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the Dendritic Cell Algorithm is sucessful at detecting port scans.Comment: 21 pages, 17 figures, Information Fusio

Environment classification in multiagent systems inspired by the adaptive immune system

The adaptive immune system in vertebrates is a complex, distributed, adaptive system capable of effecting collective mul-ticellular responses. Our study introduces many of the desirable properties of this biological system to decentralized multiagent systems. We adopt the crossregulation model of the adaptive immune system involving interactions between effector and regulatory cells. Effector cells can mount beneficial immune responses to microbial antigens as well as pathologic autoimmune responses to self-antigens. Deleterious autoimmunity is prevented by regulatory cells that suppress the effectors to tolerate the self-antigens. We redeploy the crossregulation model within a multiagent system by letting each agent run an ODE-based instance of the model. Results of extensive simulation-based experiments demonstrate that a distributed multiagent system can mount different responses to distinct objects in their environment. These responses are solely a result of the dynamics between virtual cells in each agent and interactions between neighboring agents. The collective dynamics gives rise to a meaningful "self"- "nonself" classification of the environment by individual agent, even if these categories were not prescribed a priori in the agents.info:eu-repo/semantics/publishedVersio