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39 articles for “malicious”
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Analysis and Identification of Malicious Mobile Applications Using Machines Learning
Abstract: Over the past few years, malware attacks on the Android platform have surged, posing significant risks to users' financial security, personal information, and device integrity. In the first half of 2019 alone, approximately 25 million smartphones were infected, highlighting the severity of these threats. The model ranks manifest features based on their frequency in normal and malicious apps, identifying key components that distinguish benign from malicious applications. To improve detection …
Published in Journal of Microcontroller Engineering and Applications · Vol. 12, Issue 2, 2025 · pp. 17–24 Read article
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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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Malicious Application Detection in Windows Using SVM Algorithm
Abstract: In recent years, both the development of Windows application clients and the uses of smart mobile phones have increased significantly. As the number of Windows application users continues to grow, there is a rise in malicious individuals who develop harmful Windows applications with the intent of unlawfully obtaining confidential information and engaging in fraudulent activities. These applications are designed to target vulnerable areas such as mobile banking and digital wallets, …
Published in International Journal of Mobile Computing Technology · Vol. 1, Issue 1, 2023 · pp. 30–36 Read article
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VHDL Programming for Secure Bootloaders in IoT Security
Abstract: The proliferation of Internet of Things (IoT) devices has brought unparalleled connectivity and convenience. However, this increased connectivity comes with a heightened risk of security vulnerabilities. One of the most critical areas for securing IoT devices is the bootloader, the software responsible for initializing the device and loading the operating system. A compromised bootloader can lead to a complete takeover of the device, making it a prime target for malicious …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 1, 2025 · pp. 19–28 Read article
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Designing a Novel Insider Threat Model for Enhanced Cybersecurity
Abstract: Designing a novel insider threat model is a critical imperative in the realm of cybersecurity. As organizations face an ever-expanding threat landscape, insider threats, whether deliberate or inadvertent, present a formidable challenge to the safeguarding of sensitive data and critical assets. This abstract encapsulates the significance, challenges, and innovations inherent in crafting an effective insider threat model for enhanced cybersecurity. The necessity for novel insider threat models arises from the …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 2, 2023 · pp. 24–27 Read article
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Cyber Threats Unveiled: From Terrorism to Warfare
Abstract: The paper offers a detailed study of cyber security intimidations, cyber extremism, and cyber warfare in the worldwide context. It touches upon the progress of cyber intimidations from discrete hackers to state-supported actors, exploratory mutual attack vectors such as malware and phishing. The conversation probes into the features of cyber extremism and the inspirations driving such actions. Besides, it clarifies the idea of cyber warfare, as well as strategies and …
Published in International Journal of Information Security Engineering · Vol. 3, Issue 1, 2025 · pp. 7–18 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
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IMAT: Intuitive Malware Analyzer Tool
Abstract: Malware refers to malicious software intentionally created to damage or exploit computer systems, networks, and devices. Malware can steal information, damage computers, and cause other problems disrupting normal computer operations, or gaining unauthorized access to systems. Our proposed system, "IMAT (Intuitive Malware Analyzer Tool)" uses special Python tools like VirusTotal and YARA to look for and understand malware. Imagine having a guard for your computer that checks all the files …
Published in Journal Of Network security · Vol. 12, Issue 1, 2024 · pp. 13–18 Read article
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Securing AI: A Survey Addressing Cyber Threats Arising in Cyber Security Due to Artificial Intelligence
Abstract: In today’s ever-evolving technological landscape, the integration of Artificial Intelligence (AI) across various industries underscores the critical need for a robust cybersecurity framework. This comprehensive survey delves into the pressing necessity of safeguarding AI systems against cyber threats. Recent incidents have highlighted the alarming susceptibility of AI to malicious attacks, showcasing the potential repercussions of compromised systems. These attacks range from data manipulation to privacy infringements, posing significant risks to …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 2, 2024 · pp. 41–49 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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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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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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Secure Coding Practices for Enhancing the Art of Ethical Hacking: A Comprehensive Study
Abstract: In the quickly developing scene of network safety, secure coding, and moral hacking are essential for strengthening computerized environments. Secure coding systems and moral hacking are linked to digital strength. This study examines the relationship between secure coding and moral hacking, emphasizing the importance of a hierarchical culture that focuses on secure coding standards. Attention to detail in software development can protect against malicious exploits. Security considerations are integrated throughout …
Published in International Journal of Wireless Security and Networks · Vol. 2, Issue 1, 2024 · pp. 21–29 Read article
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Ransomware Detection and Prevention Using Honeypot
Abstract: The significance of network security and explores the details of ransomware attacks, highlighting the crucial parameters essential to fortifying defences against this pernicious cyber threat. Network security involves safeguarding computer networks against unauthorized access, data breaches, and cyberattacks. Ransomware attack, a specific type of cyberattack, entail malicious software encrypting a computer system, making them unavailable to use in return attacker asks for ransom in form of cryptocurrency like Bitcoin or …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 1, 2024 · pp. 8–13 Read article
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Detecting Fake Accounts on Social Media Using Machine Learning
Abstract: The growing frequency of fake accounts on social media platforms underscores the critical necessity for effective detection methods. In response to this challenge, our study leverages state-of-the-art machine learning techniques to identify and counter deceptive entities effectively. By conducting a thorough analysis of social media data, our approach unveils intricate patterns indicative of fraudulent accounts, enabling proactive measures against them. Through the application of advanced algorithms, we present a comprehensive …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 2, 2024 · pp. 21–32 Read article
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Developing a Comprehensive Framework for User and Entity Behavior Analytics (UEBA): Integrating Advanced Machine Learning and Contextual Insights
Abstract: User and Entity Behavior Analytics (UEBA) has emerged as a crucial approach in modern cybersecurity for detecting and mitigating insider threats, compromised accounts, and other malicious activities within organizational networks. However, existing UEBA frameworks often face challenges in scalability, detection accuracy, and response effectiveness. This research work proposes a novel framework for UEBA that aims to address these limitations and enhance threat detection and response capabilities. The framework integrates advanced …
Published in Journal of Communication Engineering & Systems · Vol. 14, Issue 2, 2024 · pp. 20–32 Read article
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Cracking the Code: A Study on Exploitable Weaknesses in QR Code Technology
Abstract: This study investigates the exploitable weaknesses inherent in quick response (QR) code technology, aiming to provide insights into potential security risks and mitigation strategies. QR codes, ubiquitous in modern society, serve various purposes ranging from marketing to authentication. However, their widespread utilization also renders them vulnerable to exploits by malicious actors. The research identifies common vulnerabilities such as data tampering, code injection, and phishing attacks, which can have significant consequences, …
Published in Journal Of Network security · Vol. 12, Issue 2, 2024 · pp. 9–17 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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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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Advanced Private Cloud Security and Privacy Preservation Through the Integration of Machine Learning and Cryptography
Abstract: In modern technological landscapes, private cloud security is of paramount concern due to the ever-increasing volume and complexity of cyber threats. This research work explores the integration of machine learning and cryptography to enhance security within private cloud environments. This study aims to mitigate vulnerabilities that may compromise data integrity, confidentiality, and availability in private cloud infrastructures by using machine learning algorithms and strong cryptography. By detecting anomalous cloud patterns …
Published in International Journal of Advanced Control and System Engineering · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article