Hierarchical Classification of Android Malware Traffic

Bovenzi, Giampaolo; Persico, Valerio; Pescapè, Antonio; Piscitelli, Anna; Spadari, Vincenzo

doi:10.1109/trustcom56396.2022.00191

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Android malware is malware that is particular to the Android OS and affects or exploits data from such an Android-based mobile device. Trojans, Spyware, Adware, Worms, Botnets, and Rootkits are some of the types of malware which can be transmitted to the cloud server [ 12 ]. The importance of this activity is illustrated by the fact that Android has started growing to be the most popular cellphone operating system in the globe, with over a billion people around the world, whereas the number of malware precisely targeting them has increased at the same time: anti-virus vendors detect thousands of additional malware samples every day with little or no end in sight.…”

Section: Introductionmentioning

confidence: 99%

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm

Babbar,

Rani,

Sah

et al. 2023

Sensors

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Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system’s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

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Section: Introductionmentioning

confidence: 99%