Wireless Sensor Networks (WSN) play a crucial role in diverse data gathering applications, but face a significant challenge in the form of limited energy reserves within sensor nodes. Enhancing the network's Quality of Service, particularly its lifetime, is paramount. Prolonging the network's operational span hinges on mitigating energy consumption, with communication accounting for a substantial portion of nodal power usage. By reducing data transmission, not only can energy consumption be curtailed, but also bandwidth requirements and network congestion can be minimized. In the context of Wireless Sensor Networks, the Distributed Similarity-based Clustering and Compressed Forwarding (DSCCF) approach strives to construct data-similar iso-clusters with minimal communication overhead. This technique involves extracting trend and magnitude components from lengthy data series using an LMS filter, resulting in what is termed "data projection." Data similarity between nodes is assessed by measuring the Euclidean distance between these data projections, thereby facilitating efficient and low-overhead iso-cluster formation. To further economize intra-cluster communication, an adaptive-nLMS-based dual prediction framework is employed. During each data collection round, the cluster head holds instantaneous data for each cluster member, using either prediction or direct data communication. Furthermore, inter-cluster data is reduced via a multi-level lossless compressive forwarding technique. Impressively, this proposed approach has achieved an 80% reduction in data while maintaining optimal data accuracy for the collected information. The transmission of inter-cluster data exclusively occurs through a network backbone comprised solely of cluster heads. Initially, the cluster heads establish this network backbone. Each cluster head dispatches a link request query towards the sink through the backbone, receiving a link reply message containing path length and the weakest link of the path. The cluster head repeats this process for each available path, subsequently selecting the most optimal path based on the acquired information and its reliability in terms of link quality
Read MoreDoi: https://doi.org/10.54216/IJWAC.080201
Vol. 8 Issue. 2 PP. 08-22, (2024)
The reassessment of alarm systems’ role in this regard has led to the search for improved ways of detecting fire. In this study, sensor fusion is explored to improve the accuracy and reliability of smoke detection. Since individual sensors are limited in their capabilities, this research seeks to merge different sensor data using complex fusion techniques. This paper gives a detailed analysis of several types of sensors that are used indoors and outdoors as well as firefighter training grounds that have multiple fire sources. To work around this problem, the Adaboost algorithm was used as an ensemble learning technique where sensor data were combined iteratively to form a strong classification model. The study then goes on to meticulously plot variable distribution graphs/bar charts, carry out correlation analyses, and make comparisons with other studies done previously; these findings give insight into how effective sensor fusion methods could be when it comes to smoke detection. The research results indicate that incorporating multiple sensors can significantly enhance detection accuracy and reliability. Thus, the findings obtained from this study identify a promising path for creating more efficient smoke detection systems.
Read MoreDoi: https://doi.org/10.54216/IJWAC.080202
Vol. 8 Issue. 2 PP. 23-31, (2024)
The primary contribution of this research lies in its innovative use of artificial intelligence to automate the trust assessment process in WBANs, providing a dynamic solution to the challenge of maintaining data integrity and network reliability. The SmartTrust (SmTr) framework uses advanced machine learning techniques to accurately analyze historical and behavioral data of network nodes. Thus, computer trustworthiness scores allow one to effectively distinguish between trustworthy nodes and potentially malicious nodes. WBANs and their services are rapidly gaining popularity, but they pose unprecedented security challenges. These requirements are being met with WBAN as it evolves. In an increasingly complex, heterogeneous, and evolving mobile environment, completing these tasks can be difficult. A more secure and adaptable WBAN environment can be achieved by using trust management to meet WBAN security requirements. The reliability of a wireless sensor network is evaluated through behavioral evidence. Researchers use the results of node behavior almost directly or combine them with the results of third-party evaluation, instead of studying the original evidence of node behavior and ignoring the analysis of the history of node behavior, which leads to low confidence, rationality, and reliability. SmartTrust (SmTr) is a new approach based on artificial intelligence (AI) to improve trust and reliability over wireless body area networks (WBAN). As a modern healthcare system, this technology can be considered. Experimental results from implementing the SmTr framework demonstrate its effectiveness in improving network resilience against security threats, improving resource allocation, and thus increasing the quality and reliability of healthcare delivery.
Read MoreDoi: https://doi.org/10.54216/IJWAC.080203
Vol. 8 Issue. 2 PP. 32-39, (2024)
Healthcare 4.0, which is the integration of digital technologies in healthcare, promises to bring about revolutionary advancements but also introduces significant cybersecurity challenges. This research seeks to address the growing concerns by investigating security threats in healthcare 4.0 systems. The study uses a multifaceted methodology that includes a comprehensive literature review and empirical analysis using advanced algorithms such as Random Forest. Using visualization techniques, data distribution analysis, and intrusion detection experiments, the research identifies common vulnerabilities and patterns in healthcare 4.0 environments. The findings highlight the need for proactive measures and strong policies to protect patient data integrity, safeguard medical infrastructure, and ensure continuous provision of health care services. This study calls for a holistic approach to cyber security with an emphasis on collaborative efforts toward strengthening Healthcare 4.0 systems against emerging threats.
Read MoreDoi: https://doi.org/10.54216/IJWAC.080204
Vol. 8 Issue. 2 PP. 40-45, (2024)
The increasing threat landscape of Distributed Denial-of-Service (DDoS) attacks makes network security a major concern. These attacks are a serious challenge to the stability and integrity of digital infrastructures. This research paper is an in-depth study on how to enhance network security through the detection and mitigation of DDoS attacks. The study reviews existing literature on DDoS attack mitigation strategies, emphasizing the evolving nature of these threats and the imperative for robust defense mechanisms. The research uses statistical analysis and logistic regression to provide a detailed methodology for distinguishing DDoS attacks from normal network activities. The results show that logistic regression is an effective classification model, providing insights into improved detection measures. Finally, the study concludes by recommending a multi-faceted approach that combines theoretical insights with empirical validation, highlighting the need for stronger network security measures against DDoS attacks and enhancing digital resilience.
Read MoreDoi: https://doi.org/10.54216/IJWAC.080205
Vol. 8 Issue. 2 PP. 46-52, (2024)
Smart grids (SGs) offer can ensure that users with a continuous power supply, decreased line losses, improved renewable output and storing, user participation in current electricity, and demand-side responsiveness. The development of cyberphysical SG (CPSG) systems has transformed the standard power grid by allowing bi-directional energy flow among utilities and users. But, because of increased data change among consumers, it is presented a major problem to the firewall systems for the transmission networks at either cyber or physical planes. Intrusion Detection Systems (IDSs) can role an essential play in maintaining SGs systems against cyber threats by generating a second wall of defense, complementing conventional preventive security procedures (for instance, authorization, encryption, and authentication). Therefore, this article concentrates on the design and development of Hybrid Metaheuristics with Deep Learning Assisted Intrusion Detection in a Cyber-Physical Smart Grid (HMDL-IDCPSG) infrastructure. The major objective of the HMDL-IDCPSG system provides the effectual recognition of the intrusions using feature selection and classification processes in the CPSG infrastructure. In the presented HMDL-IDCPSG method, a binary dragonfly algorithm with the hybrid directed differential operator (BDA‐DDO) algorithm could be implemented for the feature selection (FS) method. Besides, attention-based bi-directional long short-term memory (ABiLSTM) algorithm could be carried out for the recognition and classification of the intrusions. At last, the sparrow search algorithm (SSA) can be exploited for highest chosen the hyperparameter values of the ABiLSTM algorithm which supports in achieving a better solution. For demonstrating the greater outcome of the HMDL-IDCPSG technique, a comprehensive simulation value can be executed. The obtained results reported the supremacy of the HMDL-IDCPSG methodology with other existing approaches
Read MoreDoi: https://doi.org/10.54216/IJWAC.080206
Vol. 8 Issue. 2 PP. 53-66, (2024)
The Internet of Things (IoT) represents important security vulnerabilities, increasing difficulties in cyberattacks. Attackers employ these vulnerabilities to establish distributed denial-of-service (DDoS) attacks, compromising availability and causing financial losses to digital platforms. Newly, numerous Machine Learning (ML) and Deep Learning (DL) approaches have been presented for the identification of botnet attacks in IoT networks. By analyzing the patterns of communication and behavior of IoT devices, DL algorithms will be differentiated between malicious and normal activity, therefore supporting the earlier detection and avoidance of botnet attacks. This is essential to protect the integrity and security of IoT systems that can be increasingly vulnerable to botnet-driven attacks because of their limited security measures and often large-scale applications. In this aspect, this study designs an innovative tunicate swarm algorithm with stacked deep learning for botnet detection (TSASDL-BD) technique for IoT platforms. The purpose of the TSASDL-BD technique is to recognize the botnets and achieve maximum network security. In the TSASDL-BD technique, the TSA is applied for the effectual feature selection process, which aids in reducing the dimensionality problem. For botnet detection, the TSASDL-BD technique makes use of the stacked long short-term memory gated recurrent unit (SLSTM-GRU) model. Finally, the artificial humming algorithm (AHA) can be used for the optimal selection of the hyperparameter values of the SLSTM+GRU system. The outcome analysis of the TSASDL-BD method on the benchmark database takes place. The extensive outcomes stated that the TSASDL-BD approach gains maximum detection results over other algorithms with respect of different measures
Read MoreDoi: https://doi.org/10.54216/IJWAC.080207
Vol. 8 Issue. 2 PP. 67-80, (2024)