Trust evaluation models are vital security enhancement tool for Wireless Sensor Networks (WSNs) to improve dependability (cooperation) among sensor nodes. This paper presents an Accurate and Efficient Distributed Trust Model for WSNs, which focus on fundamental requirements (resource efficiency) and security improvement. The proposed mathematical model (TSTM) computes trustworthiness of communication as well as data (transmitted) more accurately and precisely than other states of the art trust models such as LDTS, ADTC, and LWTM, etc. The proposed scheme consists of unique features like authentication based data trust, scheduler based node trust, and attack resistant by giving the high penalty and minimum reward during node misbehavior. The proposed trust model would be capable of providing security against various internal attacks due to consistency in trust values.

In has been considered that the sensor network’s safety measures have been built upon a non-realistic, trusted environment. After analyzing the trust models for wireless body sensor networks (WBSNs), it has been found that trust models should be distributed, lightweight, and robust against various vulnerabilities. They are also facing issues regarding computation overhead. Moreover, there is a threat of internal attacks over WBSNs. These attacks may be an on-off attack, data modification, message disclosure, collusion attack, bad-mouthing, brute force, and denial of service attack. Thus, this research paper considers three issues: network internal security, resource management, and spiteful node detection. The Proposed dynamic and trustworthy lightweight trust assessment scheme (RTAM) for WBSNs would play a significant role in the appropriate usage of resources and minimize the computational overhead. RTAM is a scalable trust model that incorporates logarithm based direct trust, indirect trust, and success ratio based data trust. It would be safe from a different type of attacks. To check the malicious activities that have been performed in nodes, some malicious nodes from the total nodes have been managed by the proposed network.

Wireless Sensor Networks (WSNs) face significant challenges, such as security threats, and susceptibility to various internal and external attacks, impacting data integrity and network reliability. Trust-aware machine learning (ML) based security frameworks are vital solutions to overcome these challenges by improving security, decision-making, accuracy, and attack detection. These models utilizes ML to actively monitor trust levels, enhancing accuracy of malicious behavior detection and strengthening network security and integrity. In this context, we propose a Secure Machine-learning-based Adaptive Reliable Trust (SMART) model, suitable for unattended autonomous WSN environment. The proposed SMART model provides enhanced security and accuracy through fast and trustworthy dynamic generation of trust values. The proposed SMART model uses a novel ML algorithm that derives multiple trust features such as Co-Location Relationship (CLR), Co-Work Relationship (CWR), and Cooperativeness-Frequency-Duration (CFD). These features together give rise to an proactive trust score for sensor nodes, enabling predictions of likely misbehavior and allows for informed decisions regarding the reliability of individual sensor devices. Furthermore, SMART framework approach utilizes both immediate (direct) trust and derived (indirect) trust, making use of logical time windows to systematically watch for honest and suspicious interactions. SMART utilizes an efficient K-means clustering algorithm to assign data points to the nearest K-centers, optimizing cluster identification. Furthermore, It utilizes Principal Component Analysis (PCA) for significant variance identification and makes use of an SVM-based method for dimension reduction and accurate decision-making with lower time and space complexity. Performance measures such as accuracy, F1-score, recall, FNR, malicious SD detection, and trust value change measure its performance. Simulation results confirm that the SMART model can identify malicious nodes with a detection rate of 96%, FNR of 0.7%, F1-Score of 0.75, and accuracy of 96% even when there are 50 malicious nodes. This confirms the ability of the SMART model to enhance the trustworthiness and security of WSNs considerably.

The expeditious propagation of fake news through online social media platforms has cropped up as a captious challenge, undermining the credibility of information sources and affecting public trust. Accurate detection of fake news is imperative to maintain the integrity of online content but is constrained by availability of data. This research aims to detect fake news from online articles by proposing a novel deep learning ensemble network capable of effectively discerning between genuine and fabricated news articles using limited data. We introduce ScrutNet, which leverages the synergistic capabilities of a bidirectional long short-term memory network and a convolutional neural network, which have been meticulously designed and fine-tuned for the task by us. This comprehensive ensemble classifier captures both sequential dependencies and local patterns within the textual data without requiring very large datasets like transformer based models. Through rigorous experimentation, we optimise the individual model parameters and ensemble strategy. The experimental results showcase the remarkable efficacy of ScrutNet in the detection of fake news, with an outstanding precision of 99.56%, 99.43% specificity, and an F1 score of 99.49% achieved on the partition test of the data set. Comparative analysis against state-of-the-art baselines demonstrates the superior performance of ScrutNet, establishing its prominence as a generalised and dependable fake news detection mechanism.

(2021). Guest Editors. Journal of Discrete Mathematical Sciences and Cryptography: Vol. 24, Recent Trends in Network Security and Artificial Intelligence, pp. ii-ii.

Workflow scheduling in heterogeneous multiprocessor systems becomes substantially harder when a global energy budget must be satisfied alongside performance goals. Existing energy-constrained schedulers share a common weakness: task-level energy allocation is computed independently of processor assignment, causing pre-assigned budgets to misalign with the energy-frequency characteristics of the processors eventually selected. Greedy runtime methods compound this by exhausting slack on early tasks, starving critical tasks later in the schedule. This paper presents Energy Restricted Workflow Scheduling (ERWS), a three-phase heuristic for dynamic-voltage-and-frequency-scaling-enabled heterogeneous systems that minimizes makespan under a hard global energy constraint. ERWS prioritizes tasks using upward ranking, distributes slack energy in proportion to each task’s minimum energy requirement, and selects processor-frequency pairs by earliest finish time subject to per-task energy limits. The method is evaluated on six real-world scientific workflows and compared against five state-of-the-art energy-constrained scheduling methods. Under the tightest budget tested, ERWS reduces normalized schedule length by an average of 39.3 % over the greedy baseline and 5–9 % over existing static pre-assignment methods; gains persist, though with narrower margins, as the budget relaxes. Friedman and Wilcoxon signed-rank tests confirm statistical significance across all workflows and all 30 pairwise comparisons.