Detecting intelligent malware on dynamic Android analysis environments

Singh, Shikha; Mishra, Bharavi; Singh, Saket Kumar

doi:10.1109/icitst.2015.7412132

Cited by 6 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A mobile cloud-based architecture was used to perform real-time detection efficiently [32]. In a study by Singh et al, a framework for the identification of malicious applications is presented [33]. The intent of this framework is to imitate artificial user behavior and analyze the real behavior of malicious software [33].…”

Section: Hybrid Analysis Methodsmentioning

confidence: 99%

“…In a study by Singh et al, a framework for the identification of malicious applications is presented [33]. The intent of this framework is to imitate artificial user behavior and analyze the real behavior of malicious software [33]. In the study of Wang et al, a static analysis technique was used to obtain permission usage information from API calls [34].…”

Section: Hybrid Analysis Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

View full text Add to dashboard Cite

Android is the most used operating system (OS) by mobile devices. Since applications uploaded to Google Play and other stores are not analyzed comprehensively, it is not known whether the applications are malicious software or not. Therefore, there is an urgent need to analyze these applications regarding malicious software. Moreover, mobile devices have limited resources to analyze the applications. In this study, a malicious detection system named “Web-Based Android Malicious Software Detection and Classification System” was developed. The system is based on client-server architecture, static analysis and web-scraping methods. The proposed system overcomes the resource restriction issue, as well as providing third-party service support by means of client-server architecture. Based on the performance evaluation conducted in this research, the developed system’s success rate is 97.62% on benign and malicious datasets.

show abstract

Section: Hybrid Analysis Methodsmentioning

confidence: 99%

Section: Hybrid Analysis Methodsmentioning

confidence: 99%

Web-Based Android Malicious Software Detection and Classification System

Doğru¹,

Kiraz²

2018

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Dietzel et al [243] offered a false responder agent that provides misleading values to the malware regarding the execution environment. Singh [179] used the detection of user interactions and anti-emulators to enhance the resilience of identifying dynamic malware [244]. Petsas et al [115] suggested several countermeasures for different types of evasion detection, such as anti-emulation employing IMEI alteration and precise sensor simulation.…”

Section: (A)mentioning

confidence: 99%

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Faruki

Bhan

Jain³

et al. 2023

Information

View full text Add to dashboard Cite

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection.

show abstract

“…Scientists studied the possibility of detecting the running environment fingerprints to differentiate between an emulator and a physical device ( Jing et al, 2014 ; Maier, Muller & Protsenko, 2014 ; Maier, Protsenko & Müller, 2015 ; Vidas & Christin, 2014 ). Android.obad ( Faruki et al, 2015b ; Singh, Mishra & Singh, 2015 ) is an emulator-aware malware, which complicates the analysis process. The malware looks for the “Android.os.build.MODEL” value throughout the code and exits if it matches the emulator’s model.…”

Section: Evasion Techniquesmentioning

confidence: 99%

“…Singh ( Singh, Mishra & Singh, 2015 ) enhances the dynamic malware detection robustness, using anti-emulator and user interaction detection. Petsas ( Petsas et al, 2014 ) proposes countermeasures for different evasion detections, such as anti-emulation using realistic sensor simulation and IMEI modification.…”

Section: Evaluation Of Evasion Detection Frameworkmentioning

confidence: 99%

The rise of obfuscated Android malware and impacts on detection methods

Elsersy¹,

Feizollah²,

Anuar³

2022

PeerJ Computer Science

View full text Add to dashboard Cite

The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques. The study concludes the recent Android malware detection-related issues and lessons learned which require researchers’ attention in the future.

show abstract

Detecting intelligent malware on dynamic Android analysis environments

Cited by 6 publications

References 3 publications

Web-Based Android Malicious Software Detection and Classification System

Web-Based Android Malicious Software Detection and Classification System

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

The rise of obfuscated Android malware and impacts on detection methods

Contact Info

Product

Resources

About