Many static and behavior-based malware detection methods have been developed to address malware and other cyber threats. Even though these cybersecurity systems offer good outcomes in a large dataset, they lack reliability and robustness in terms of detection. There is a critical need for relevant research on enhancing AI-based cybersecurity solutions such as malware detection and malicious behavior identification. Malware Analysis and Intrusion Detection in Cyber-Physical Systems focuses on dynamic malware analysis and its time sequence output of observed activity, including advanced machine learning and AI-based malware detection and categorization tasks in real time. Covering topics such as intrusion detection systems, low-cost manufacturing, and surveillance robots, this premier reference source is essential for cyber security professionals, computer scientists, students and educators of higher education, researchers, and academicians.

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Manoj Kumar M V completed his Ph.D. from the department of computer science at National Institute of Technology Karnataka, Surathkal, Mangalore, India. He did his masters from Bapuji Institute of Engineering and Technology, Davanagere, India and is graduated from Vishweshwaraiah Technology University, Belgaum, India. His research interest is in data and process mining, mobile application, and statistical data analysis. R-programming is one of his key interest and building mobile apps is his hobby. He has been a resource person in various workshops on mobile application development and data analysis. He is currently working on a problem related to concept drift, in the context of process mining. He has a good number of publications in international journals and conferences. He has worked as a research scholar under Dr. Annappa, associate professor, NITK.

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