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32 articles for “intrusion detection system (IDS)”
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Animal Detection in Farms Using Opencv
Abstract: Agriculture plays a fundamental role in sustaining the Indian economy, providing employment and livelihood to a large portion of the population. Despite advancements in farming techniques, one of the persistent challenges faced by farmers is the intrusion of wild animals into agricultural fields. Such intrusions often lead to large-scale crop damage, financial loss, and emotional distress for farmers. Traditional animal deterrent methods, such as manual patrolling, fences, or scarecrows, have …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 Read article
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Transforming Hospital Environments: The Role of Sound Detection
Abstract: Hospitals are sanctuaries of healing, yet they are often paradoxically exposed to a pervasive, often underestimated threat: excessive noise. While internal hospital sounds pose their own challenges, the impact of external sounds – traffic, construction, sirens, and urban clamor – can profoundly compromise patient safety, recovery, and overall well-being. This article explores the critical role of advanced sound detection systems in mitigating this external acoustic intrusion, leveraging technology to create …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 4, Issue 1, 2026 · pp. 28–40 Read article
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A Review on the Impact of Artificial Intelligence on Cybersecurity
Abstract: When it comes to protecting against cyber threats, the use of AI is changing everything. Thanks to AI-powered technologies, organizations can now better foresee and handle potential intrusions. These solutions provide exceptional capabilities in identifying threats, monitoring in real time, and delivering predictive insights. But, with these innovations come significant hazards and difficulties, necessitating thoughtful deliberation and preventative measures. Artificial intelligence's impact on cybersecurity is explored in this article, looking …
Published in Journal of Artificial Intelligence Research & Advances Read article
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AI-Based Cybersecurity Framework for Protecting Smart Surveillance Infrastructure in Mumbai
Abstract: Smart city infrastructures increasingly rely on interconnected surveillance systems to ensure safety, operational efficiency, and public trust. However, the rapid expansion of IoT-based monitoring technologies has introduced new cyber risks, especially in high-density metropolitan areas. This paper proposes an AI-driven cyber resilience framework targeting smart surveillance infrastructure as a critical smart-living domain, focusing on Mumbai as a case study. Using the CIC-IDS2017 dataset, a machine learning-based intrusion detection model is …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 Read article
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Cybersecurity in Web Automation: A Machine Learning Approach to Lightweight Intrusion Detection
Abstract: Launch-Attack is a lightweight and practical threat-detection framework designed specifically for smaller web-automation environments, including setups that rely on tools such as Selenium. Rather than aiming to replace large enterprise-grade security platforms, the framework focuses on offering an accessible option for developers, testers, and researchers who need real-time monitoring without the heavy resource demands of traditional systems. The model relies on machine-learning techniques implemented through Scikit-learn, enabling it to detect …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 34–40 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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DRIVE-DEFENDER: A Driver Safety-oriented Alcohol and Drowsiness Detection System
Abstract: DRIVE-DEFENDER presents a pioneering approach in driver safety through the development of a real-time machine learning system for alcohol detection. The detrimental impact of alcohol-impaired driving on road safety necessitates efficient detection mechanisms. Current methodologies are often hindered by their cost, invasiveness, and reliance on specialized sensors. DRIVE-DEFENDER utilizes a camera for recording the face of the driver in real time, employing image processing techniques to identify facial landmarks. These …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 15–26 Read article
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A Study on AI-Driven Multi-Layered Defense in 6G Ecosystems
Abstract: The 6G networks bring about new degrees of possible functions related to connectivity, latency, data throughput, and integration with artificial intelligence (AI). This enables advances within healthcare, autonomous systems, and smart cities. The positive impact of rapid advancements must also be balanced with heightened risks due to the sheer volume of gaps that can be exploited, and the complex nature of the alignments and breaches. This results in the breaches …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
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Advancing IoT Security through Blockchain-based Approaches
Abstract: The emergence of blockchain technology has revolutionized various industries, including the Internet of Things (IoT), by providing a decentralized and secure platform for data management and transaction processing. However, securing IoT devices and networks remains a significant challenge due to inherent vulnerabilities and the increasing sophistication of cyberattacks. Blockchain-based security approaches have shown promise in addressing these challenges, yet their adoption is hindered by a lack of comprehensive taxonomy and …
Published in Trends in Electrical Engineering · Vol. 15, Issue 1, 2025 · pp. 1–34 Read article
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A Review of Machine and Deep Learning Techniques for Cyber Security
Abstract: Nowadays in the digital landscape, cyber threats and attacks are increasing in an exponential manner, posing server risks to organizations and critical infrastructures. Data breaches often result from sophisticated threat models that exploit vulnerabilities in networks, systems and user behaviors. Cyber solutions are increasingly incorporating machine learning and deep learning to prevent and mitigate these security issues. These technologies have the potential to detect anomalies, classify threats and predict potential …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 01–07 Read article
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Fortifying the Cloud: AI-Driven Security Paradigms and Evolving Threat Defenses in Modern Cloud Computing
Abstract: Organizations worldwide are raising their concerns about security maintenance while cloud computing expands rapidly to serve as a digital transformation foundation. The study explores modern cloud security patterns while also evaluating how artificial intelligence modifies the identification and evaluation of complex cyber threats along with their prevention methods. New security threats such as insider operations and DDoS attacks and data breaches alongside insecure APIs can be detected through machine learning …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 34–40 Read article
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Leak Location Detection in Underground Pipeline Using Transient Pollutant Propagation Concentration Signature Analysis with Theory of Hypernumbers
Abstract: The paper introduces a new analytical method of detecting leakage locations in underground pipe systems. For the first time, the phenomenon of pollutant backflush through leaks in liquid transport systems is used for algorithmic leak location identification. The paper compares the proposed concept with known monitoring methods. The theoretical analysis of the method's capability to increase leak localization distance and detect the location of tiny holes in pipelines is provided. …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 13–22 Read article