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10 articles for “Suspicious Activity”
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Automated Suspicious Activity Detection in Video Surveillance Using Deep Learning: A Review
Abstract: In the current era of advanced security systems, video surveillance plays an essential role in ensuring safety by detecting suspicious activities. With the increase in real-time data, manual monitoring has become impractical, paving the way for automated surveillance systems utilizing machine learning (ML) and artificial intelligence (AI) technologies. This paper explores the integration of ML and AI models, specifically convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, for …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 1, 2025 · pp. 20–27 Read article
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Deep Guard: A Comprehensive Deep Learning System for Unmasking Suspicious Activities in Surveillance Footage
Abstract: These days, video surveillance is quite vital. Technology has evolved considerably as machine learning, artificial intelligence, and deep learning become increasingly widespread. There are several algorithms that assist in identifying distinct kinds of suspicious behaviour from live footage by combining the techniques. A person's behaviour is the most unpredictable thing, and it can be quite challenging to determine whether it is normal or suspicious. Video surveillance is automated to address …
Published in International Journal of Satellite Remote Sensing · Vol. 1, Issue 1, 2023 · pp. 23–28 Read article
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Money Laundering Transaction with Machine Learning
Abstract: This study discusses the use of machine learning algorithms to discover firms that are prone to money laundering. The purpose of this research is to develop, describe, and test a machine learning model for determining which bank transactions should be physically scrutinized for money laundering activities. To train a supervised machine learning model, three categories of historical data are required: legitimate "normal" transactions, transactions flagged as suspicious by the bank's …
Published in Current Trends in Information Technology · Vol. 14, Issue 2, 2024 · pp. 1–15 Read article
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Design and Implementation of an IoT-Based Power Theft Detection System Using Sensor Networks
Abstract: With the increasing demand for efficient energy distribution and consumption monitoring in smart grid systems, the need for robust power theft detection mechanisms becomes paramount. This project proposes an IoT-based solution utilizing energy metering to detect instances of power theft within different segments of the distribution network. The system employs three types of energy meters: Distribution Point (DP), Pole, and Domestic meters. Every meter is positioned thoughtfully to track the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 11, Issue 2, 2024 Read article
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Real-Time Video Surveillance Using ESP-32 CAM for IoT Applications
Abstract: The rapid advancements in technology and the growing concerns for security have driven the need for innovative, cost-effective, and scalable surveillance systems. This project presents the development of a smart surveillance system using the ESP-32 CAM module, a compact and low-cost microcontroller integrated with Wi-Fi, Bluetooth, and a high-resolution OV2640 camera. The system is designed to provide real-time monitoring, motion detection, and automated alert features, catering to the security needs …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 · pp. 26–35 Read article
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Digital Agriculture Schemes in Kerala: Risk Analysis and Multi-Layered Security Framework
Abstract: Kerala’s agricultural sector is undergoing rapid digital transformation, driven by initiatives such as the e-Krishi platform and the growing adoption of Internet of Things (IoT)–enabled precision farming technologies. While these developments promise improved productivity, transparency, and data-driven decision-making, they also introduce significant cybersecurity and data governance challenges that cannot be overlooked. This paper critically examines the emerging security, privacy, and operational risks associated with the digitization of agricultural services in …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 · pp. 23–30 Read article
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Advanced AI based Energy Monitoring and Demand Prediction with Theft Detection
Abstract: This paper presents a study on an AI-based energy management system, which is designed for real-time monitoring of energy consumption for theft detection and energy demand prediction. Our energy management system has voltage and current sensors for energy consumption measurement and provides real- time data on voltage (V), current (mA), and energy units. We have implemented Machine Learning algorithm SVM to improve the process of theft detection by identifying anomalies …
Published in Journal of Alternate Energy Sources & Technologies · Vol. 17, Issue 1, 2026 Read article
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Border Security Alert for Soldiers
Abstract: Secure a nation by protecting its people and the country’s borders. Soldiers guarding remote and hostile areas of the border experience very difficult situations when confronted with the possibility of arms and/or people crossing the border without permission and environmental conditions or obstacles that can impede communication. The proposed project titled, “Border Security Alert for Soldiers”, is an attempt to assist soldiers in their border surveillance efforts by using an …
Published in Journal of Microcontroller Engineering and Applications · Vol. 13, Issue 2, 2026 Read article
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Enhancing LAN Security Using Machine Learning
Abstract: The modern Local Area Network (LAN) is a critical component of any organization's infrastructure, facilitating communication, resource sharing, and access to the wider internet. However, this connectivity also brings inherent security risks. Traditional security measures, relying on signature-based detection and rule-based systems, are increasingly struggling to keep pace with the evolving sophistication of cyberattacks. This is where Machine Learning (ML) offers a powerful alternative, enabling proactive threat detection and enhanced …
Published in International Journal of Wireless Security and Networks · Vol. 3, Issue 2, 2025 · pp. 07–16 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