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18 articles for “Malware Analysis”
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Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis
Abstract: The ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This study explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio-inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The study begins by introducing the GWO …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 43–53 Read article
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A Securing Infrastructure Against Signature-based Assaults Using Karnaugh Detection Systems
Abstract: Cloud computing is one of the efficient business models for offering customers online services. Approximately 81% of businesses worldwide rely on the cloud; however, one of the most pressing issues is cloud security. Malware of several kinds can cause havoc in the clouds. Attacks on the cloud might come from both the inside and the outside. The signature attacks that can occur from outside the cloud server will be the …
Published in Recent Trends in Parallel Computing · Vol. 10, Issue 1, 2023 · pp. 1–13 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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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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Study and Research Survey of Machine Learning on Different Field for Next Research Directions
Abstract: Machine learning (ML) is a part of artificial intelligence (AI). ML is a brain of AI, which works to same to same as human creature. ML used to several learning algorithms is a part of related learning techniques as supervised, unsupervised, reinforcement, deep and deep-reinforcement learning techniques. ML used on various research fields as, cyber security, big data, IOT, big code naturalness, flood prediction, ultra sound computer-aided diagnostic system, medicine …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 2, 2020 · pp. 46–55 Read article
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Malware Detection Using Data Mining
Abstract: In the digital world, everything is getting digitalized. Mobile devices, such as smartphones and handsets, are becoming increasingly popular, increasing criminals' appeal as targets for mobile malware. Consequently, in order to properly identify mobile malware, an antimalware detector plays a significant role. Prior to this, we concentrated on developing a behavioural analysis based on an ontology. To learn more about mobile malware, to assist end users in using their mobile …
Published in Journal of Advances in Shell Programming · Vol. 9, Issue 3, 2022 · pp. 9–13 Read article
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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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Exploring the Connection: Analyzing the Relationship Between Duqu and Stuxnet
Abstract: Malware computer worms like Stuxnet and Duqu have the ability to take down any computer system in the globe. Although they are extremely similar to one another, Duqu is superior to Stuxnet. Because it comes in two versions, Duqu 1.0 and 2.0, it is also more harmful and dangerous than Stuxnet. The first malware attack to garner international attention was Stuxnet, which was designed to physically harm industrial infrastructure that …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 11, Issue 3, 2024 · pp. 11–17 Read article
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The Evolution of Ransomware: In-Depth Analysis of Threat Development and Modern Defense Mechanisms
Abstract: Ransomware is the most severe of all cybersecurity threats the contemporary digital world is confronted with. It is a form of malware that encrypts a victim's information and requests ransom, usually in cryptocurrency, for its decryption. From its first appearance in the late 1980s, ransomware grew from simple malware into very advanced and targeted attacks that can be used to bring down entire organizations, businesses and critical infrastructure. This study …
Published in Journal Of Network security · Vol. 13, Issue 3, 2025 · pp. 35–43 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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Artificial Intelligence in Cybersecurity: Emerging Trends, Technological Advancements, and Future Directions for Cyber Defense
Abstract: Artificial Intelligence (AI) is revolutionizing the field of cybersecurity by automating complex security tasks, improving threat detection capabilities, and enhancing the precision of threat response mechanisms. With the rapid evolution of cyber threats such as malware, ransomware, phishing, and data breaches, conventional security systems are often insufficient to provide timely and accurate protection. AI, powered by machine learning algorithms and neural networks, enables the analysis of vast datasets to detect …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 103–112 Read article
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Brief Study of Cybercrime on an Internet
Abstract: Cybercrime is a serious challenge to society and is harmful to the individuals who become victims. This research paper engages analysis of the cybercrimes that target individuals on the internet and also examines the motivation of criminals that perform such attacks which include (e. g phishing, scams), online harassment, identity theft, malware, hacking and denial-of-service crimes. Cybercrime is a threat to today’s internet-dependent society and is a huge growing problem. …
Published in Journal of Communication Engineering & Systems · Vol. 11, Issue 1, 2021 · pp. 22–27 Read article
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Risk Analysis of Android Apps and Fake ID Flaw Solution Model
Abstract: In this era of modern technology where everyone rely on the latest technical specifications especially concerning with the latest mobiles and apps. Keeping this in mind the various software companies have launched their own operating system for mobile, and in the same field ‘Google’ also launched their own mobile operating system android which has its own technicality and specification which is competing with the other company’s operating system. After using …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 2, Issue 3, 2015 · pp. 46–58 Read article
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A Correlative Study of Machine Learning Algorithms
Abstract: Machine learning (ML) is the term used to define a systematic reading of algorithms and mathematical models of computer structure used to perform a specific task without precise programming. Learning algorithms in many programs we use every day. Nowadays, one of the reasons an online search engine like Google performs so effectively is because the learning algorithm has mastered the art of rating web sites. Additional uses for these algorithms …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 9, Issue 2, 2022 · pp. 30–35 Read article
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An Experimental Approach of Machine Learning Algorithms to Detect Botnet DDoS Attacks
Abstract: Botnets are one of the threats in a network to hamper the quality of the network by disrupting theresources of the network. These Botnets can be controlled remotely by Botmaster. The Machine Learning Algorithms play a major role to detect and control the Botnets that cause to DDoS attacks, malwares and phishing attacks that are vulnerable to network resources. The DDoS attacks are most dangerous malware events that disrupt whole …
Published in Journal Of Network security · Vol. 10, Issue 3, 2022 · pp. 7–14 Read article
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An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids
Abstract: The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 16–25 Read article
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An in-Depth Analysis of Cyber Security: Vulnerabilities, Threat Landscapes, Attack Mechanisms, and Defense Solutions
Abstract: In a highly digitalized world today, cyber security has taken over as a necessary component to safeguarding the modern world technology. With the increase in the dependency on the digital platform and online services, the threat of cyber threats has also risen. Such threats are not confined to mere attacks anymore, as they have taken a new form of complexity and magnitude to impact individuals, businesses, government institutions among others. …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 Read article
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A Security Investigation Survey of Ransomware Detection and Avoidance Strategies for IoT Networks
Abstract: The internet of things (IoT) refers to the interconnection of a large number of distinct physical objects, which in turn makes possible a variety of services and applications. Because the IoT sector is developing at such a rapid rate, ensuring its safety ought to be a high concern. At this time, ransomware attacks constitute the biggest danger to IoT posed by cyberattacks. Ransomware is software that blocks access to or …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 2, 2023 · pp. 14–23 Read article