Detection and Mitigation of Android Malware Through Hybrid Approach

Malware analysts still need to manually inspect malware samples that are considered suspicious by heuristic rules. They dissect software pieces and look for malware evidence in the code. The increasing number of malicious applications targeting Android devices raises the demand for analyzing them to find where the malcode is triggered when user interacts with them. In this paper a framework to monitor and visualize Android applications’ anomalous function calls is described. Our approach includes platform-independent application instrumentation, introducing hooks in order to trace restricted API functions used at runtime of the application. These function calls are collected at a central server where the application behavior filtering and a visualization take place. This can help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions.

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“…Next we show example rules that describe suspicious behavior. Some of the rules generated by us are similar or resemble the ones in [24], namely,…”

Section: Rules "Generation"mentioning

confidence: 86%

Detection and Visualization of Android Malware Behavior

Somarriba

Zurutuza

Uribeetxeberria

et al. 2016

Journal of Electrical and Computer Engineering

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“…One of the well-known methods for knowledge manipulation and knowledge representation was a rule-based system which has been applied by Zaman and Petel [7], [19] in the form of R 1 : if θ 1 then θ 2 where the θ 2 statement of consequence can be determined with level of certainty whenever θ 1 statement of condition is observed. Let another rule conditions that R 2 : if θ 2 then θ 3 where θ 3 statement represents the forward chaining factor which involves the rules of R 1 and R 2 immediately after x 1 is established in the chain.…”

Section: ) Methods Overcomes Reduced Parameters θmentioning

confidence: 99%

A Review of Predictive Analytic Applications of Bayesian Network

Yusof¹,

Mokhtar²

2016

International Journal on Advanced Science, Engineering and Information Technology

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Malware can be defined as malicious software that infiltrates a network and computer host in a variety of ways, from software flaws to social engineering. Due to the polymorphic and stealth nature of malware attacks, a signature-based analysis that is done statically is no longer sufficient to solve such a problem. Therefore, a behavioral or anomalous analysis will provide a more dynamic approach for the solution. However, recent studies have shown that current behavioral methods at the network-level have several issues such as the inability to predict zero-day attacks, high-level assumptions, non-inferential analysis and performance issues. Other than performance issues, this study has identified common scientific characteristics which are reduced parameter, θ and lack of priori information p(θ) that causes the problems. Previous methods were proposed to address the problem, however, were still unable to resolve the stated scientific hitches. Due to the shortcomings, the Bayesian Network in terms of its probabilistic modeling would be the best method to deal with the stated scientific glitches which also have been proven in the area of Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition. This study will critically review the predictive analytic applications of Bayesian Network model in different research domain such as Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition and discover any potential approach available in the domain of Computer Networks. Based on the review, this paper has identified several Bayesian Network properties which have been used to overcome the abovementioned problems. Those properties will be applied in future studies to model the Behavioral Malware Predictive Analytics.

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“…Through Hybrid Approach" Presented system [12] is a malware recognition system consolidated on static and dynamic investigation. First, APKs is decompiled and various permission-based parameters are scanned for each APK and comma separated CSV file is generated.…”

Section: B "Detection and Mitigation Of Android Malwarementioning

confidence: 99%

A Review of Hybrid Malware Detection Techniques in Android

Shabir

Sabahat

2020

2020 IEEE 23rd International Multitopic Conference (INMIC)

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Android is one of the most popular mobile platforms with availability of thousands of applications on Play Store. These applications help us to be productive in our daily lives and now-a-days life without smartphones cannot be imagined. Unlike other platforms, Android's open source nature gives the developers a great leverage to create many applications with very few restrictions. This also attracts malware developers to target Android platform and since Android allows installing applications from unverified sources, the risk of installing an unwanted malware application arises which can compromise Android user's privacy and personal data. Some several tools and techniques that can detect these sorts of applications before or after installation and prevents loss of valuable data. In this paper, existing malware detection techniques are explored that use hybrid approach, compared, and contrasted in detail and a useful insight is presented for future research.

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Detection and Mitigation of Android Malware Through Hybrid Approach

Cited by 17 publications

References 28 publications

Detection and Visualization of Android Malware Behavior

Detection and Visualization of Android Malware Behavior

A Review of Predictive Analytic Applications of Bayesian Network

A Review of Hybrid Malware Detection Techniques in Android

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