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The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients' sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

Evaluating the resiliency of power systems against abnormal operational conditions is crucial for adapting effective actions in planning and operation. This paper introduces the level-of-resilience (LoR) measure to assess power system resiliency in terms of the minimum number of faults needed to produce a system outage (blackout) under sequential topology attacks. Four deep reinforcement learning (DRL)-based agents: deep Q -network (DQN), double DQN, the REINFORCE (Monte-Carlo policy gradient), and REINFORCE with baseline are used to determine the LoR. In this paper, three case studies based on IEEE 6-bus test system are investigated. The results demonstrate that the double DQN network agent achieved the highest success rate, and it was the fastest among the other agents. Thus, it can be an efficient agent for resiliency evaluation., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Mariam Ibrahim et al.)

The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network ( ANN ), convolutional neural network ( CNN ), long short-term memory ( LSTM ), and a hybrid model convolutional LSTM ( ConvLSTM ) that combines LSTM with CNN , and one support vector machine ( SVM ) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2020 Mariam Ibrahim et al.)

Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks. [ABSTRACT FROM AUTHOR]