This study delves into advanced feature selection methodologies for enhancing Android malware classification. GHR-Optimizer is introduced as an innovative feature selection approach combining Grey Wolf Optimization, Hill Climbing, and Random Forest Classifier method. The approach selects features from a hybrid dataset and is evaluated across machine learning, deep learning, and ensemble frameworks. A detailed comparative analysis is conducted, contrasting GHR-Optimizer with static and dynamic feature sets as well as traditional filter and wrapper-based methods. The implementation of the GHR method demonstrated superior performance, particularly when evaluated with diverse datasets such as KronoDroid, which achieved exceptional accuracy and balance in classification metrics. When integrated with the Random Forest classifier, the GHR-Optimizer achieves an accuracy of 98.40%. These findings underscore GHR-Optimizer’s superior performance in boosting classification accuracy and robustness, highlighting its pivotal role in advancing feature selection strategies within the domain.

Recent advancements in the field of the Internet of Medical Things (IoMT) have enabled the real-time monitoring and treatment of patients with communicable infectious diseases while minimizing human intervention. However, IoMT devices face challenges, such as unbalanced energy consumption, memory constraints, computation power, and low latency, which can deter the efficient transfer of patient monitoring data. Thus, there is an urgent need to establish an energy-efficient infrastructure for IoMT devices to remotely monitor and collect data on communicable diseases. For this, a genetic algorithm (GA)-based dynamic transmission of data for communicable diseases in the IoMT environment is proposed in this article. The energy utilization of the IoMT is enhanced by considering the GA evolutionary processing based on the dynamic sensor range. The proposed work incorporates a periphery of the fixed area for deploying the IoMT devices to settle the energy hole problem. Multiple sinks and direct information collection concepts are also introduced which further improve the performance and reduce the movement of data packets. The proposed protocols not only optimize energy usage but also provide a robust approach for massive data collection and communication.

The significant expansion in network size to support new paradigms such as cloud computing, IoT (Internet of Things), etc. together with the exponential increase in vulnerabilities has challenged the existing security mechanisms greatly. These challenges have opened many avenues for research in network security. However, while attack graphs play an important role in analyzing vulnerabilities, analyzing large attack graphs itself is a major issue. Therefore, it is necessary to extract only the critical part of the attack graph. Although technologies have been developed for attack path characterization, there is a lack of hybrid technology that can differentiate between similar behavior attack paths. We have proposed a cost-based path characterization technique that takes the attack node's vulnerability complexity into account and significantly reduces the number of vulnerabilities that need to be patched to avoid the major segment of attack graph. Moreover, we have used a real network prototype to validate the performance of the proposed scheme. The proposed scheme works well in cases where some vulnerabilities have similar risk scores. To the best of our knowledge, this is the first time that a cost-effective approach for attack path analysis has been proposed.

Summary Self‐healing is an approach that maintains the health of the system with proper supervision of its functioning and emerges from any unacceptable state during the execution. The complexity of modern distributed systems and dynamic hike in terms of users' access has led to an increase in maintenance. In this paper, a self‐healing architecture for services that exploit the autonomous capability of agent technology is proposed. The proposed mechanism is a multi‐agent system that comprises different agents with different capabilities and roles. The planning agent, responsible for taking the right decision to revive the system from an unhealthy state to a healthy state, uses a case‐based fault recovery mechanism at runtime. The architecture contains a persistent layer that maintains previously occurred failed cases. To determine the best suitable solution, the similarity between the detected fault and recorded failed cases is calculated. The case, having a maximum similarity index value is considered closest to the failure. Multiple recovery strategies like a replacement, restart, alternative resources are been utilized. Also, to validate the proposed architecture, an SOA‐based application is used and performance‐based evaluation metrics are analyzed.

Dynamic metrics are known to assess the actual behavior of software systems as they are extracted from runtime data obtained during program execution. However, recent literature indicates that dealing with dynamic information remains a formidable challenge due to the huge size of execution data at hand, resulting in long processing delays. We present an efficient and scalable technique to extract design level dynamic metrics from Calling Context Tree (CCT) using cloud based MapReduce paradigm. CCT profiles having node count up to 40 million are used to extract a number of dynamic coupling metrics. On an average, 73% increase in performance is observed as compared to sequential analysis. Also other performance characteristics like speed-up and scale-up are analyzed to strengthen the applicability of our parallel computation approach.