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96 articles for “anomaly detection”
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AI for Cybersecurity: Deploying Machine Learning for Network Traffic Anomaly Detection
Abstract: The growing sophistication of cyberattacks and the growth of network traffic necessitate sophisticated anomaly detection methods. This study overviews the use of artificial intelligence (AI) and machine learning (ML) to counter these challenges, as noted in current studies. It analyses supervised learning (SVM, Decision Trees), unsupervised learning (K-means, DBSCAN), and deep learning (CNNs, RNNs, Auto-encoders) approaches, considering their strengths and weaknesses. The research integrates current developments in AI/ML-based network anomaly …
Published in International Journal of Computer Science Languages · Vol. 3, Issue 2, 2025 · pp. 1–10 Read article
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Advanced Anomaly Detection in Cloud Infrastructures Using Deep Learning Algorithms
Abstract: It is critical to guarantee the stability and security of cloud environments as cloud computing is becoming the backbone of contemporary IT infrastructures. Neglecting to quickly identify and resolve anomalies, which might point to security breaches, performance problems, or system breakdowns, can lead to disastrous outcomes. The increasing size and complexity of cloud infrastructures are challenging the effectiveness of traditional anomaly detection methods. These approaches often depend on rule-based systems …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 1–11 Read article
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Enhancing Smart Grid Security: Machine Learning Approaches for Detecting Anomalies
Abstract: The integration of Information and Communication Technology (ICT) with traditional electric grids has led to the development of smart grids. However, this integration has also increased the risk of anomalies, such as cyber-attacks, metering fraud, electricity theft etc. False Data Injection Attacks are a class of cyber-attacks against power grid monitoring systems, where adversaries can inject false data to manipulate the grid’s operation. Metering frauds pertain to malicious customers com- …
Published in Trends in Electrical Engineering · Vol. 14, Issue 2, 2024 · pp. 10–19 Read article
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Timestamp Extraction and Log Classification Using Supervised Machine Learning: A Comparative Study
Abstract: In modern software systems, logs are vital for monitoring application behavior, diagnosing issues, and analyzing performance. Timestamps are especially important for sequencing events, identifying anomalies, and understanding system failures. However, detecting timestamps in logs is challenging due to inconsistent formatting across systems and the presence of timestamp-like strings in non-timestamp fields. Traditional rule-based methods often fail in such cases. This study proposes a supervised machine learning approach to accurately classify …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 · pp. 26–38 Read article
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Intensifying Security with Smart Video Surveillance
Abstract: The smart video surveillance system is designed to observe the activities of people nearby for safety concerns. This smart video surveillance can be installed at malls, public areas, banks, businesses, and ATM machines. The field of network surveillance research is rapidly expanding. The cause for this is the global insecurity that is occurring in great extent. As a result, an intelligent monitoring system that gathers data in real time, communicates, …
Published in Recent Trends in Programming languages 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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Comprehensive Comparative Analysis of Intrusion Detection Systems: Evaluating Signature Based, Anomaly Based, and Hybrid Approaches
Abstract: In the fast-changing world of cybersecurity, Intrusion Detection Systems (IDS) play a vital role in protecting digital resources. This study offers an in-depth comparative analysis to evaluate the efficiency and performance of different IDS solutions. It examines a variety of both commercial and opensource platforms, encompassing signature based, anomaly based, and hybrid models, to assess their effectiveness in identifying and responding to a wide range of cyber threats. Methodologies for …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 2, 2025 · pp. 39–44 Read article
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Cyber-Secure IoT Framework for Monitoring Fiber-Reinforced Polymer Composites Using Embedded Sensors
Abstract: The present research paper suggests a cyber-safe Internet of Things system in real-time monitoring of fiber-reinforced polymer composites with inbuilt sensors. It is aimed at enhancing structural health maintenance, using sensual, intelligent analysis, and data protection in the same platform. Multi-layer architecture An embedded sensor, signal processing, anomaly detection and lightweight layer of cyber-security are developed. Experimental validation is done under controlled conditions and the performance is measured by these …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 434–458 Read article
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Log Identification and Monitoring System Using Generative AI
Abstract: In contemporary software ecosystems, application and infrastructure logs play a vital role in ensuring system reliability, performance optimization, fault diagnosis, and security compliance. As applications become increasingly distributed and cloud native, the volume, velocity, and variety of generated log data have grown dramatically. This rapid expansion makes traditional manual log inspection inefficient, error-prone, and largely impractical. To address these challenges, this paper proposes an artificial intelligence (AI) driven log monitoring …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 08–16 Read article
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Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems
Abstract: Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning. As a first step, we employ quantum annealing for conventional optimization to adjust multiple Gaussian …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 18–27 Read article
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AI-Based Threat Detection in Cloud Platforms
Abstract: This research work delves into the transformative role AI has come to assume for enhanced threat detection in the cloud ecosystem. The conventional security frameworks, which form the basis for many architectures, are several steps behind actualizing the rapidly evolving cyber threat landscape, exposing critical weaknesses in the areas of accuracy, adaptability, and speed of response. Initially, the study sets forth the problems with the old-school approaches to threat detection …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 01–10 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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IoT-Enabled Self-Monitoring Polymer Composites for Solar Facade Systems with Cloud-Based Thermal Analytics
Abstract: The increasing demand for intelligent and energy-efficient building systems has hastened the production of smart solar facade systems. This paper presents an IoT-based self-monitoring polymer composite system, which includes thermal analytics running on the cloud to provide real-time assessments of the performance of the facade. The artificial composite panel has sensing capability built in which the material is able to measure both surface and internal thermal behavior simultaneously in dynamic …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 657–681 Read article
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Monitoring of Unauthorized Identity and Access Behaviour for Outsourced Data in Cloud Environment
Abstract: The outsourcing of data is a significant challenge in the modern cloud computing ecosystem when it comes to tracking unauthorized identification and access behaviour. In order to overcome this issue, this research suggests a thorough method for reliable anomaly detection in cloud systems. Improving data security and offering a trustworthy monitoring system are the two main goals. The suggested approach proceeds methodically, gathering information from several sources such as user …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 9–19 Read article
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The Integration of AI Technologies in Automating Cyber Defense Mechanisms for Cloud Services
Abstract: The swift growth of cloud computing has transformed how organizations handle and store data, providing greater scalability and adaptability. However, the transition to cloud-based environments has heightened the complexity of cybersecurity challenges, especially in detecting and responding to security incidents. Conventional methods of incident response, which heavily depend on manual efforts, are no longer adequate to address the rapidly evolving and complex nature of modern cyber threats. This study explores …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 1–14 Read article
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Blockchain Enabled IoT System for Tamper Proof Monitoring of Polymer Composite Manufacturing Quality
Abstract: Ensuring real-time process compliance in resin-based polymer composite manufacturing remains a persistent challenge due to non-linear material behaviors, unpredictable curing dynamics, and fragmented sensor data pipelines. Traditional centralized monitoring architectures struggle to guarantee data integrity, auditability, and adaptive response under high-frequency environmental fluctuations. Most existing frameworks fall short in unifying trust, traceability, and time-critical decision-making particularly during critical cure-phase deviations due to limited integration of blockchain with intelligent sensor systems. …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 126–145 Read article
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Predictive Maintenance Strategies for Safety-critical Mechanical Systems
Abstract: Ensuring the reliability and safety of industrial systems is essential, especially in high-risk sectors such as aerospace, manufacturing, and energy. Predictive maintenance (PdM) has become a crucial approach for minimizing operational failures and improving maintenance efficiency. This research introduces an advanced PdM framework that enhances industrial safety by integrating Internet of Things (IoT) technology, machine learning (ML), and big data analytics. By enabling real-time monitoring and predictive fault detection, this …
Published in Journal of Industrial Safety Engineering · Vol. 12, Issue 1, 2025 · pp. 12–17 Read article
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Fortifying the Blockchain Fortress: A Machine Learning Paradigm for Enhanced Security
Abstract: Blockchain technology has emerged as a revolutionary tool in the digital landscape, enabling secure and transparent transactions across a decentralized network. Despite its robust security features, blockchain systems remain vulnerable to anomalies and malicious activities. The detection of these anomalies using machine learning has become essential for protecting blockchain networks and ensuring their integrity. This project delves into the application of machine learning techniques to detect abnormal patterns within blockchain …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 2, 2024 · pp. 33–40 Read article
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Swarm-Enabled AI for Smart Mobility and Sustainable Transport
Abstract: The significant issues facing the modern urban infrastructure are the management of road traffic problems, such as severe traffic congestion, the detection of unsafe driving behavior, and road safety. The traditional ground-based surveillance systems will be helpful, but they will reach their limits in large and dynamic environments. It is because of predetermined perspectives, blindness, and the inability to scale. To designate these problems, the present study proposes a traffic …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 33–38 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