“…It is critical to successfully identify, mitigate, and fight against Android malware assaults, since Android malware has long been a significant threat to the security of Android applications. …”
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“…With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. …”
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“… Dissecting the dark side of the Internet with its infectious worms, botnets, rootkits, and Trojan horse programs (known as malware) is a treaterous condition for any forensic investigator or analyst. …”
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“…However, the malware images generated by such schemes have the problem of image size imbalance, which affects the accuracy of malware classification. …”
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“…Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. …”
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“…This repository of information can serve future research by enabling the analysis of antivirus scan result patterns to discern the prevalence and behaviour of various malware families. Such analysis can help to extend existing malware taxonomies, the identification of novel variants, and the exploration of malware evolution over time.…”
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“…By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. …”
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“…To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. …”
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“…Malware detection based on API call sequences is widely used for the ability to model program behaviours. …”
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“…Most malware research aims to probe the different methods of preventing, analysing, and detecting malware attacks. …”
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“…The evolution of malware is causing mobile devices to crash with increasing frequency. …”
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“…The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. …”
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“…The increasing similarity between Android malware and benign applications makes it difficult to distinguish them from each other and causes a situation of concern for users. …”
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“…Nowadays, several malicious applications target computers and mobile users. So, malware detection plays a vital role on the internet so that the device is secure without any malicious activity affecting or gathering the useful content of the user. …”
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“…We present a comprehensive empirical analysis of malware detection conducted on recent data (May–June, 2022) from the Koodous platform, which is a collaborative platform where over 70 million Android applications were collected. …”
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“…As IoT devices are increasing rapidly, their security must classify malicious software accurately; however, current IoT malware classification methods cannot detect cross-architecture IoT malware using system calls in a particular operating system as the only class of dynamic features. …”
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“…Therefore, researchers turn to extracting abundant static features to implement efficient malware detection. In this paper, we explore the direction of capturing malware semantic information by using graph learning algorithms and present BejaGNN (Behavior-based Java malware detection via Graph Neural Network), a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. …”
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“…To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. …”
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