With recent examples of APT attacks being used to disable or disrupt critical infrastructure in Ukraine in 2015, the urgency to produce a robust detection framework for rootkits is increased. These capabilities make them popular with a wide range of cyber attackers, including the instigators of advanced persistent threat attacks like the Stuxnet, Flame, and Gauss malware campaigns. Rootkits are powerful and dangerous pieces of malware that use stealth and administrative privilege to maintain a persistent, covert foothold for a cyber attacker on compromised systems. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally. This review will help academics gain a full picture of Android malware detection based on machine learning. Finally, we assess the future prospects for research into Android malware detection based on machine learning. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Prediction accuracy in training, thus giving an initial validationĪndroid applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. The ransomware sample on a real Android device with perfect That our NLPSA approach can classify and detect execution of Tool as a potential data provider, which now comes standard on As aĬontribution, we are the first to utilize the Perfetto system tracing Our study looks at a single ransomware artifact (WannaLocker)Įvaluated on a single Android device and operating system. Technique and use a nonlinear phase space analysis (NLPSA)įor detecting anomalous behavior based on power variations. We perform a case-study analysis to validate feasibility of the Look at an alternative method for malware detection using side�channel analysis of CPU frequency data on Android smartphones. Means to detect malicious activity or malware. Likewise, researchers have begun to use mathematical modelsĪnd machine learning techniques on side-channel data as a Monitor system data such as power consumption, electromagneticĮmissions, and CPU timing to infer sensitive user information. In the past, side-channelĪttacks have been used for malicious purposes where attackers Android devices continue to dominate the marketįor global smartphone users, thus making them an ideal targetįor malicious software developers.
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