As the sensor nodes communicate continuously from the target areas to base station, hundreds of thousands of data are collected to be used for the decision making. With the advances in sensor technology, sensor nodes, the tiny yet powerful device are used to collect data from the various domain. Finally, future work directions are presented as guidelines for academia and industry alike to help them reduce or even avoid the harmful impact of these annoying efforts. From the intruder point of view, we have compared various evasion techniques that are used prominently by the malware authors to hinder detection efforts. Furthermore, we have discussed and classified forensic analysis efforts in mobile malware detection perspective. Another taxonomy comprises of mobile malware attack vector is presented to look threat clusters and loopholes to locate their malicious widespread impact on communities. We have presented various well-organized and in-depth taxonomies that uncover mobile malware detection approaches based on their analysis techniques, working platform, data acquisition, operational impact, obtained results and artificial intelligence component involved. This paper provides a comprehensive review of state-of-the-art mobile malware attacks, vulnerabilities, detection techniques and security solutions over the period of 2013–2019 that majorly targeted Android platform. As a result, Android malware detection is one of the sizzling topics in the mobile security domain. Android's popular and attractive environment not only captured the attention of users but also increased security concerns. Furthermore, it also explains how some conventional approaches are still relevant today in terms of detection speed.Ī pervasive increase in the adoption rate of smartphones with Android OS is noted in recent years. This article explores the transition of malware detection from traditional to AI‐based techniques. It is also beneficial to create models that are less prone to malware variations and capture the malicious behavior holistically. AI has helped to improve malware detection and reduce manual work through automation of feature extraction and feature selection. AI is vulnerable to attacks, such as dataset poisoning and adversarial data input, which can reduce model accuracy and increase false negatives. This article presents an extensive analysis of traditional and AI‐based methods for malware detection and related challenges. AI can detect a zero‐day attack and malware, but it suffers from several false positives. This behavioral identification paved the way for artificial intelligence (AI) in cybersecurity. Hence the efforts to identify malware have been focused on behavioral modeling to identify and classify malware. Polymorphic malware has rendered traditional signature‐based detection ineffective. An infected endpoint device can take part in aggressive or slow distributed denial of service attacks globally. It also affects the security of endpoint devices. Malware is a constant threat to the safety of the public Internet and private networks. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to more effective and practical research. Therefore, this article aims at providing researchers with in-depth knowledge in the field and identifying potential future research and a framework for a thorough evaluation. In contrast to other SLR studies, our study classified the means of attack as supervised and unsupervised learning. We explore the mobile malware detection techniques used in recent studies based on attack intentions, such as server, network, client software, client hardware, and user. We critically evaluate 154 selected articles and highlight their strengths and weaknesses as well as potential improvements. This study expands on previous research on machine learning-based mobile malware detection. Industry and academia have attempted to address cyber security challenges by implementing automated malware detection and machine learning algorithms. With the deployment of the 5G cellular system, the upsurge of diverse mobile applications and devices has increased the potential challenges and threats posed to users.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |