Transformer-based Network Anomaly Detection System for Intelligent Cyber Security Monitoring

A Machine Learning-Based Intrusion Detection Framework for Enhanced Network Security

The rapid expansion of interconnected networks, cloud computing, Internet of Things (IoT) devices, and digital communication technologies has significantly increased the complexity of modern cyber threats, making traditional network security mechanisms increasingly inadequate. Intrusion Detection Systems (IDS) are essential components of cybersecurity infrastructures, designed to monitor network activities and identify malicious behavior before it compromises system integrity. However, conventional signature-based and rule-based IDS are primarily effective against previously known attack patterns and often fail to detect zero-day attacks, advanced persistent threats (APTs), and other evolving cyber threats. To address these limitations, machine learning (ML) has emerged as a transformative technology that enables adaptive, intelligent, and data-driven intrusion detection by learning complex patterns from network traffic and system behavior. This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models. The paper examines widely used benchmark datasets, feature selection and feature engineering methods, data preprocessing techniques, and commonly adopted performance evaluation metrics for assessing intrusion detection effectiveness. It also reviews various IDS deployment architectures, including centralized, distributed, edge-based, cloud-enabled, and hybrid frameworks, highlighting their strengths and limitations in different networking environments. To provide a clear understanding of intelligent intrusion detection mechanisms, the review introduces two conceptual frameworks: a machine learning-based intrusion detection pipeline that illustrates the end-to-end process from data acquisition to threat classification, and a layered network security architecture demonstrating the integration of ML techniques into modern cybersecurity infrastructures. Furthermore, the paper discusses critical challenges affecting the deployment of ML-based IDS, including data imbalance, scalability, computational complexity, model interpretability, adversarial machine learning attacks, privacy preservation, and real-time processing constraints.

Enhancing Network Security with a Hybrid Intrusion Detection System Using SVM

Networks are rapidly and inevitably becoming complex, also the number of cyber threats is rising. Technology that uses intrusion quote elimination methods is essential to most networked forms for intentional analysis. The paper provides a thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles. The method uses both abnormality detection, which is extremely useful for identifying novel and 0-day attacks, and misuse detection, which is more effective in the detection of known attack patterns. Support Vector Machines is applied in building the system that achieves excellent accuracy in classification of network traffic, and at the same time reducing false negative and false positive rates. The hybrid model is the approach through which data-driven analytics is performed on raw network traffic data to extract relevant features, which are then used at run-time for classification by use of SVM. Anomaly detection aims at discovering deviations originating from standard network behavior, whereas misuse detection employs aspects of known signatures attack. This anomaly or misuse based systems outperform state-of-the-art models with respect to accuracy 97.1 in all other parameters such as precision, recall, as well as the f1 score. Among the other positive aspects of this work is the possible use of SVM in hybrid intrusion detection frameworks foreseeing an all-encompassing solution to modern network security issues. The research will further consider utilization of adaptive learning in order to improve persistency of the system against learning to new threats. Keywords:- Behavioral Anomaly Detection, Explainable Artificial Intelligence, Ensemble Learning, Anomaly Detection, Attack Classification

Intelligent Intrusion Detection System Using Hybrid Swin Transformer-RNN for Efficient Cyber Threat Mitigation

An Intelligent Intrusion Detection Framework for Cyber Security in Hybrid Network Environments

In the era of contemporary data traffic routing, the concept of Intrusion Detection Systems (IDS) is substantially utilized. However, the efficacy of IDS is often decreased because of the reality that high-concentration traffic postfixes, sophisticated cyber criminals, and more and more stringent demands are tending to decrease in resource-limited environments. The paper presents the enhanced intrusion detection system based on deep learning architecture, which can be flexible, adaptive and as well maintain the high detection capability with confidence under changing or to-be changed network settings. The objectives of this and aforementioned also address the issue of avoiding strong overtting behavior by models during the transfer learning and even rich feature representation through the first-stage operation: moving to address and ideally preventing attacks rather than supporting other attacks. The work is tailored to the deployment of the light-weight and adaptive IDS design which is supposed to be large enough to work in real time on low-powered devices such as IoTs and edge devices that are nondominated in energy and computationally less demanding. Real-time adaptability of the model will be examined through operational deployment simulations. It is also expected that such simulations would take into account latency, throughput, and energy consumption of the IDS model. On the one hand, In a stage nested within the very last period of this research, the IDS model has been merged with Explainable AI technologies; now LIME and SHAP are also preserved to improve the interpretability of the model decisions and the level of decision-making. What kind of feature attributions are made with the intrusion data? How is Interpretability of the model evaluated in terms of fidelity, comprehensibility, and expert belief? Therefore, all the above-mentioned events will be a perfect example of how the technologically ingrained tasks, particularly in the technical discipline of security studies, can be wrapped into the very cognitive resource of human beings.

From Signature to Attention: Transformer-Powered Intrusion Detection Systems for Cybersecurity

Intrusion detection systems (IDSs) play a vital role in safeguarding modern computer networks against increasingly sophisticated and high-volume cyber threats. Recent progress in artificial intelligence, especially deep learning, has allowed IDSs to go from static rule-based systems to adaptive and data-driven security solutions. But traditional machine learning- and convolution-based IDSs often have trouble finding long-range dependencies and temporal correlations in large-scale network traffic. This makes detection less accurate and increases the number of false alarms. This challenge becomes more pronounced in heterogeneous and evolving network environments. To address this, experiments are conducted on two widely used benchmark datasets: CIC-IDS2017 for binary intrusion detection and CICIDS2018 for multiclass attack classification. These datasets represent realistic network traffic with diverse attack categories and severe class imbalance. The proposed methodology employs a Transformer-based intrusion detection framework incorporating sequence windowing, positional encoding, and multi-head self-attention to learn contextual traffic representations. The primary contribution of this study lies in systematically integrating sliding temporal windowing, positional encoding, and multi-head self-attention into flow-level intrusion modeling, accompanied by empirical ablation analysis and statistical validation across two large-scale CIC benchmark datasets. Performance is evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and false alarm rate. Experimental results demonstrate that the proposed model achieves high detection accuracy, strong discriminative capability, and low false alarm rates across both datasets, confirming its effectiveness and scalability for next-generation cybersecurity applications.

Design of an intelligent security system for cyber-attack detection in petrochemical SCADA networks based on a hybrid artificial neural network and Krill-Herd algorithm.

In the past few years, with the increasing severity of cyber-attacks, network security has become a burning topic in safety-critical industries such as petrochemicals. SCADA systems, as the central hub of industrial process control, are under constant attack by reconnaissance and intrusion attempts. To secure these environments, this study proposes a comprehensive two-tier intelligent security system. Initially, industrial network traffic was captured using Wireshark to distinguish reconnaissance attack patterns from normal operations. For the first tier, a monitoring script was developed to extract behavior-based scores, which are then analyzed by an Artificial Neural Network (ANN) optimized via the Krill-Herd (KH) algorithm to effectively detect early-stage attacks (ARP, Port, and ICMP scans). Furthermore, to address sophisticated application-layer threats, the system incorporates a second tier: a Bottleneck Deep Neural Network (BDNN) optimized by the KH algorithm. This module analyzes advanced statistical features to successfully detect stealthy manipulations within the Modbus protocol. The results confirm that the proposed two-tier system detects reconnaissance and stealthy Modbus attacks with high accuracy and very low inference latency. The Krill-Herd (KH) algorithm contributes automated hyperparameter selection and faster convergence rather than a statistically significant accuracy gain over a well-tuned Adam baseline. Ultimately, this framework can serve as a highly effective, intelligent tool in cyber defense against evolving threats in petrochemical industries.