• Mr. Trivedi Vishal Rajendrakumar
• Dr. Bhawesh Kumawat

Network safety stays a basic worry in the computerized age, with malware representing a tenacious danger to the honesty and classification of data frameworks. Traditional approaches as well as cutting-edge technologies are covered in great detail in this abstract, which provides a comprehensive overview of current malware detection methods. The review starts by looking at signature-based detection, which has been around for a long time and relies on predefined patterns of known malware. It addresses the limitations of signature-based methods in the face of polymorphic and metamorphic variants of malware and discusses their efficacy. Consequently, the record investigates heuristic and behavior based location components, clarifying how these procedures break down the way of behaving of possibly pernicious code to recognize dangers. Machine learning (ML) and artificial intelligence (AI) have received a lot of attention recently as effective malware detection tools. This abstract delves into various machine learning and artificial intelligence models, including techniques for supervised and unsupervised learning. It looks at these models' strengths and weaknesses, taking into account things like their ability to adapt to new threats and their ability to be interpreted. The survey also starts with a look at signature-based detection, focusing on its history and the problems that polymorphic and metamorphic malware have caused in the past. The role of heuristic and behavior-based analysis techniques in identifying malicious code based on observed patterns and behaviors is then discussed. The foundational method of signature-based detection, which relies on well-known malware patterns, is the focus of the investigation. This method works well against common threats, but it has problems with polymorphic and metamorphic malware variants, so it's important to know how it works in the current threat landscape. The subsequent analysis places an emphasis on the adaptive nature of heuristic and behavior-based detection methods in spotting potential threats based on observed patterns and behaviors. A dynamic approach to malware detection is provided by these techniques, which go beyond the limitations imposed by static signatures. The job of Threat Intelligence (TI) and sandboxing technologists analyzed as essential parts in proactive protection methodologies. Danger insight takes care of give continuous information on arising dangers, empowering fast reaction and moderation. In contrast, sandboxing creates isolated environments for suspicious file analysis, making it easier to identify malware behavior without jeopardizing the host system's integrity. The abstract delves into the paradigm shift in malware detection brought about by Machine Learning (ML) and AI. The effectiveness and adaptability of various machine learning (ML) models, such as supervised learning, unsupervised learning, and deep learning, are examined in light of changing cyber threats. The abstract acknowledges persistent difficulties in the field in spite of these advancements. Threat actors are looking to take advantage of vulnerabilities and alter detection mechanisms, so adversarial attacks on ML models represent a significant threat. The paramount ethical considerations are user privacy and data security, necessitating a delicate balance between effective detection and individual rights respect.

Cyber security, signature-based detection, heuristic analysis, behavior-based detection, machine learning, and artificial intelligence are all examples of malware detection.

"Enhancing Cyber security : A Comprehensive Survey of Malware Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.e696-e698, January-2024, Available :http://www.jetir.org/papers/JETIR2401484.pdf

"Enhancing Cyber security : A Comprehensive Survey of Malware Detection Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppe696-e698, January-2024, Available at : http://www.jetir.org/papers/JETIR2401484.pdf