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Ransomware is an emerging category of malware that locks computer data via powerful cryptographic algorithms. The global propagation of ransomware is a serious threat for individuals and organizations. The banking sector and financial institutions are the prime targets of such ransomware attacks. In case of such an attack, the field of digital forensics helps in estimation of the severity and data loss caused by the attack. Traditional digital forensics investigations make use of static or behavioral analysis to detect malware in infected systems. However, these procedures are challenged by malware obfuscation techniques. Malicious processes can stay inactive and undetected if only a single memory dump is analyzed. Thus, there is a need to collect numerous memory dumps of an individual program that can help with comprehensive and accurate analysis. In this article, we have developed a framework for volatile memory acquisition at regular time intervals to analyze the behavior of individual processes in memory. Through memory forensics, salient features are extracted from the infected memory dumps. These features can be utilized to classify malicious and benign processes efficiently through machine learning as compared to conventional techniques.

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This study focuses on position tracking control for the networked predictive motion control system with random communication delay. First, the output feedback controller is designed by networked predictive control law to actively compensate the time delay induced by the random channels of the motion control system. A closed‐loop model is established for the networked predictive motion control system with random bounded communication delay, modelled by a Markov chain. Then, the sufficient conditions of stability for the networked predictive motion control system are provided, by constructing the Lyapunov–Krasovskii functional, followed by the theoretical proof. Furthermore, the output feedback controller is constructed and the linear matrix inequality method is applied to obtain the designed controller gain. Last, the simulation and experimental results are presented to prove the effectiveness of the proposed method.