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11 articles for “Malicious Network Traffic Detection”
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Malicious Network Traffic Detection Using Hybrid Feature Selection with Ensemble Neural Network
Abstract: The detection of malicious network traffic is a critical aspect of cybersecurity, aiming to protect sensitive data and maintain the integrity of network systems. This study introduces a novel approach that combines hybrid feature selection with ensemble neural networks to enhance the accuracy and efficiency of malicious network traffic detection. The dataset used in this study was obtained from Kaggle and offers a wide-ranging and varied collection of network traffic …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 3, 2025 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Intrusion Detection Using ANN Machine Learning for MIM, DOS, BO
Abstract: Intrusion detection system is a software program developed to use on computer systems so that it can identify intrusion attack with help of different techniques like the machine learning algorithms. The variety of assaults over the internet has multiplied through the years because of the development and smooth availability of computing technologies. Attackers develop new attack types, so in order to save you from those assaults, intrusion detection systems must …
Published in Journal Of Network security Read article
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Random Forrest Based Man-in-the-Middle Attack Detection in Advanced Metering Infrastructure
Abstract: Advanced metering infrastructure (AMI) plays a central role in the operation of modern smart grid (SG) systems by enabling continuous, two-way communication between utility providers and consumers. Through this communication, AMI supports real-time monitoring, dynamic pricing, and efficient energy management. However, the same connectivity that makes AMI effective also increases its exposure to cyber threats. One of the most critical threats is the man-in-the-middle (MITM) attack, in which an attacker …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 · pp. 1–8 Read article
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Assessing the Robustness of Machine Learning Models for Wireless Intrusion Detection Under Adversarial Traffic Perturbations
Abstract: As the Internet of Things (IoT) devices and wireless communication networks continue to grow rapidly, protecting systems from cyber threats has become increasingly important. Machine learning–based intrusion detection systems (IDS) have shown strong potential in detecting abnormal and malicious network activities, yet their effectiveness and resilience when facing adversarial attacks are still not sufficiently explored. This research evaluates Machine Learning (ML) models–XGBoost, random forest, and multi-layer perceptron (MLP)—in detecting attacks …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 29–34 Read article
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Exploring the Role of Advanced Shell Scripts for Malware Threat Detection
Abstract: This study investigates the application of advanced shell scripts in detecting malware threats within computer systems. As cyber-attacks become more sophisticated, traditional detection methods frequently prove inadequate, highlighting the need for innovative approaches. The research highlights the effectiveness of shell scripting in automating the monitoring and analysis of system behavior, file integrity, and network traffic. By leveraging patterns and signatures of known malware, the scripts can identify anomalies indicative of …
Published in Journal of Advances in Shell Programming · Vol. 11, Issue 3, 2024 · pp. 6–16 Read article
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The Evolution of Bio Crypt Keys: From Concept to Implementation
Abstract: With the rapid increase in data exfiltration due to cyber-attacks, Covert Timing Channels (CTCs) have emerged as a significant and sophisticated network security threat. These channels exploit inter-arrival times of data packets to exfiltrate sensitive information from targeted networks. Detecting CTCs increasingly relies on machine learning techniques, which use statistical metrics to differentiate between malicious (covert) and legitimate (overt) traffic flows. However, as cyber-attacks become more adept at evading detection …
Published in Journal of Control & Instrumentation · Vol. 15, Issue 2, 2024 · pp. 38–45 Read article
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Network Intrusion Detection System Using Decision Tree
Abstract: This paper presents a novel approach to network intrusion detection systems (NIDS) using advanced decision tree algorithms to address critical limitations in existing IDS solutions. Traditional IDSs often struggle with high false positive and negative rates, lack of scalability, and poor interpretability. Our proposed IDS leverages decision trees to enhance detection accuracy, interpretability, and scalability, thereby improving network security. Decision trees are chosen for their adaptive learning capabilities, transparent decision-making …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 22–33 Read article
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Botnet Beacon: Unveiling Covert Networks with Advanced AI Detection Strategies
Abstract: Securing information technology systems is paramount in today's interconnected world, where the reliability and security of networks and applications are of utmost importance. In this context, the development of a Botnet Detection System (BDS) that harnesses the power of AI classification algorithms becomes a critical endeavor. The primary objective of this work is to construct a comprehensive framework for a BDS that can efficiently gather network data and subject it …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 2, 2024 · pp. 26–32 Read article
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Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
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AI-Based Machine Learning Web Application Firewall (ML-WAF)
Abstract: This research investigates the use of deep learning techniques for the real-time detection of malicious activities in web traffic and proposes an intelligent, AI-driven Web Application Firewall (WAF) designed to provide automated and adaptive security. The system analyzes diverse components of HTTP requests, including request methods, URLs, headers, cookies, and payload content, to accurately identify and classify malicious behavior. The proposed model targets a wide range of common and critical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article