8,454 research outputs found
Log-based Anomaly Detection of CPS Using a Statistical Method
Detecting anomalies of a cyber physical system (CPS), which is a complex
system consisting of both physical and software parts, is important because a
CPS often operates autonomously in an unpredictable environment. However,
because of the ever-changing nature and lack of a precise model for a CPS,
detecting anomalies is still a challenging task. To address this problem, we
propose applying an outlier detection method to a CPS log. By using a log
obtained from an actual aquarium management system, we evaluated the
effectiveness of our proposed method by analyzing outliers that it detected. By
investigating the outliers with the developer of the system, we confirmed that
some outliers indicate actual faults in the system. For example, our method
detected failures of mutual exclusion in the control system that were unknown
to the developer. Our method also detected transient losses of functionalities
and unexpected reboots. On the other hand, our method did not detect anomalies
that were too many and similar. In addition, our method reported rare but
unproblematic concurrent combinations of operations as anomalies. Thus, our
approach is effective at finding anomalies, but there is still room for
improvement
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
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