Industrial Internet of things (IIoT) environments need to implement reliable security measures because of the growth in network traffic and overall connectivity. Accordingly, this work provides the architecture of network traffic analysis and the detection of intrusions in a network with the help of edge computing and using machine-learning methods. The study uses k-means and DBSCAN techniques to examine the flow of traffic in a network and to discover several groups of behavior and possible anomalies. An assessment of the two clustering methods shows that K-means achieves a silhouette score of 0.612, while DBSCAN achieves 0.473. For intrusion detection, k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) were used and evaluated. The analysis revealed that both KNN and RF yielded seamless results in terms of precision, recall, and F1 score, close to the maximum possible value of 1.00, as demonstrated by both ROC and precision–recall curves. Accuracy matrices show that RF had better precision and recall for both benign and attacks, while KNN and LR had good detection with slight fluctuations. With the integration of edge computing, the framework is improved by real-time data processing, which means a lower latency of the security system. This work enriches the knowledge of the IIOT by offering a detailed solution to the issue of cybersecurity in IoT systems, based on well-grounded performance assessments and the right implementation of current technologies. The results thus support the effectiveness of the proposed framework to improve security and provide tangible improvements over current approaches by identifying potential threats within a network.

Machine Learning (ML) and Deep Learning (DL) models are increasingly deployed in critical domains such as computer vision, natural language processing (NLP), automatic speech recognition (ASR), time-series analysis, and cybersecurity. Despite their success, these models remain vulnerable to adversarial attacks that can mislead predictions and undermine system reliability. To address this challenge, we conduct a Systematic Literature Review (SLR) of adversarial attacks and defenses in ML/DL, following PRISMA guidelines. Our review analyzes 132 peer-reviewed studies published between 2017 and 2025, identified from leading digital libraries including IEEE, ACM, ScienceDirect, and SpringerLink. The objectives of this study are to: (i) identify state-of-the-art adversarial attack and defense techniques and assess their strengths and limitations; (ii) evaluate how novel threats and countermeasures are addressed across domains; and (iii) propose a unified taxonomy that integrates adversarial attacks, defenses, evaluation metrics, and application domains. Our findings show that adversarial training and certified robustness dominate defense research, but both face trade-offs in scalability and generalizability. Computer vision remains the most studied domain, while NLP, ASR, and time-series research is growing but lacks standardized benchmarks. This review contributes a taxonomy and cross-domain synthesis of adversarial ML research, highlights research gaps, and outlines future directions to advance robustness and support the development of trustworthy AI systems.

The Wireless Sensor Network (WSN) consists of many sensors that are distributed in a specific area for the purpose of monitoring physical conditions. Factors such as hardware limitations, limited resources, unfavourable WSN deployment environment, and the presence of various attacks on nodes can lead to the presence of Faulty Nodes in a WSN. This raises the problem of detecting Faulty Nodes and avoiding Data loss. Detecting Faulty Nodes in real-world scenarios will improve the quality of a WSN. The research was aimed at developing an algorithm to determine the location of Faulty Nodes in a WSN. The algorithm uses characteristics of Machine Learning and Support Vector Machines (SVM), which use the classification of Data into true and false. A Mathematical Model for determining Faulty Nodes using the SVM is considered. A methodology for detecting a Faulty Node is demonstrated, which consists of Data Collection, Feature Extraction, Training, and Testing the Model. The Results of simulated experiments that were conducted with different numbers of nodes from 50 to 320 are shown. The Model is tested on Data very similar to real-world sensing Data to evaluate the ability of the Model to detect failed nodes and calculate training and testing errors. As a result, the training error is 4.6667%, the accuracy of detecting faulty nodes was 97%. The simulation results demonstrate the high stability of the proposed algorithm and are suitable for network environments with irregular node distribution or coverage gaps. In real scenarios, it can provide a high level of uninterrupted operation of the WSN and lossless data transmission. Shortcomings and prospects in research on fault detection in WSN, such as studying real-world hardware issues and its security, were presented.

The internet of things (IoT) encompasses various devices for monitoring, data collection, tracking people and assets, and interacting with other gadgets without human intervention. Implementing a system for predicting the development and assessing the criticality of detected attacks is essential for ensuring security in IoT interactions. This work analyses existing methods for detecting attacks, including machine learning, deep learning, and ensemble methods, and explores the federated learning (FL) method. The aim is to study FL to enhance security, develop a methodology for predicting the development of attacks, and assess their criticality in real-time. FL enables devices and the aggregation server to jointly train a common global model while keeping the original data locally on each client. We demonstrate the performance of the proposed methodology against structured query language (SQL) injection and brute force attacks using the CICIOT2023 dataset. We used accuracy and F1 score metrics to evaluate the effectiveness of our proposed methodology. As a result, the accuracy in predicting SQL injection reached 100%, and for brute force attacks, it reached 98.25%. The high rates of experimental results clearly show that the proposed FL-based attack prediction methodology can be used to ensure security in IoT interactions.

One of the most important aspects of the effective functioning of wireless sensor network (WSN) is their security. Despite significant progress in WSN security, there are still several unresolved issues. Many review studies have been published on the problems of possible attacks on WSN and their identification. However, due to the lack of their systematic analysis, it is not possible to fully substantiate practical recommendations for the effective application of the proposed solutions in the field of WSN security. In particular, the creation of methods that provide a high degree of security while minimizing computational effort and costs, and the development of effective methods for detecting and preventing attacks on WSN. The purpose of this document is to fill this gap. The article presents the results of the study in the form of a systematic analysis of the literature with a targeted selection of sources to identify the most effective methods for detecting and preventing attacks on WSN. By identifying the security of WSN, which has not yet been addressed in research works, the review aims to reduce its impact. As a result, our extended taxonomy is presented, including attack types, datasets, effective WSN attack detection methods, countermeasures, and intrusion detection systems (IDS).

The proliferation of IoT devices has heightened their susceptibility to cyberattacks, particularly botnets. Conventional security methods frequently prove inadequate because of the restricted processing capabilities of IoT devices. This paper suggests utilizing machine learning methods to enhance the detection of attacks in Internet of Things (IoT) environments. The paper presents a novel approach to detect different botnet assaults on IoT devices by utilizing ML methods such as XGBoost, Random Forest, LightGBM, and Decision Tree. These algorithms were examined using the N-BaIoT dataset to classify multi-class botnet attacks and were specifically designed to accommodate the limitations of IoT devices. The technique comprises the steps of data preparation, preprocessing, classifier training, and decision-making. The algorithms achieved high detection accuracy rates: XGBoost (99.18%), Random Forest (99.20%), LGBM (99.85%), and Decision Tree (99.17%). The LGBM model demonstrated exceptional performance. The incorporation of the attack evaluation model greatly enhanced the identification of botnets in IoT networks. The paper displays the efficacy of machine learning techniques in identifying botnet assaults in IoT networks. The models generated exhibit exceptional accuracy and can be seamlessly integrated into existing cybersecurity systems.