MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

Saracino, Andrea; Sgandurra, Daniele; Dini, Gianluca; Martinelli, Fabio

doi:10.1109/tdsc.2016.2536605

Cited by 351 publications

(197 citation statements)

References 24 publications

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“…Saracino et al [79] presents a multilevel and behaviour based Android malware detection using 125 existing malware families and reports 96% detection of malware. Malik et al [80] uses pattern based detection based on Domain Name Service (DNS) queries, their approach is able to detect polymorphic malware.…”

Section: Platforms and Iot Malwarementioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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Section: Platforms and Iot Malwarementioning

confidence: 99%

Internet of Things Malware : A Survey

Karanja¹,

Masupe²,

Jeffrey³

2017

IJCSES

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“…In [26] and [27], the authors present a multi-level behavior-based intrusion detection system called MADAM. The proposed system learns the correct devices' behavior and then detects significant deviations signaling an intrusion.…”

Section: Related Workmentioning

confidence: 99%

Risk analysis of Android applications: A user-centric solution

Dini

Martinelli

Matteucci

et al. 2018

Future Generation Computer Systems

Self Cite

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Android applications (apps) pose many risks to their users, e.g., by including code that may threaten user privacy or system integrity. Most of the current security countermeasures for detecting dangerous apps show some weaknesses, mainly related to users' understanding and acceptance. Hence, users would benefit from an effective but simple technique that indicates whether an app is safe or risky to be installed. In this paper, we present MAETROID (Multi-criteria App Evaluator of TRust for AndrOID), a framework to evaluate the trustworthiness of Android apps, i.e., the amount of risk they pose to users, e.g., in terms of confidentiality and integrity. MAETROID performs a multi-criteria analysis of an app at deploy-time and returns a single easyto-understand evaluation of the app's risk level (i.e., Trusted, Medium Risk, and High Risk), aimed at driving the user decision on whether or not installing a new app. The criteria include the set of requested permissions and a set of metadata retrieved from the marketplace, denoting the app quality and popularity. We have tested MAETROID on a set of 11,000 apps both coming from Google Play and from a database of known malicious apps. The results show a good accuracy in both identifying the malicious apps and in terms of false positive rate.

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“…Yerima et al [22] utilized ensemble learning techniques for Android malware detection and reportedly had an accuracy rate between 97.33 and 99%, with a relatively low false alarm rate (less than 3%). Saracino et al [23] designed a system called MADAM which is a host-based Android malware detection. The MADAM was evaluated using real world apps.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent OS X malware threat detection with code inspection

Pajouh

Dehghantanha

Khayami

et al. 2017

J Comput Virol Hack Tech

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With the increasing market share of Mac OS X operating system, there is a corresponding increase in the number of malicious programs (malware) designed to exploit vulnerabilities on Mac OS X platforms. However, existing manual and heuristic OS X malware detection techniques are not capable of coping with such a high rate of malware. While machine learning techniques offer promising results in automated detection of Windows and Android malware, there have been limited efforts in extending them to OS X malware detection. In this paper, we propose a supervised machine learning model. The model applies kernel base Support Vector Machine and a novel weighting measure based on application library calls to detect OS X malware. For training and evaluating the model, a dataset with a combination of 152 malware and 450 benign were created. Using common supervised Machine Learning algorithm on the dataset, we obtain over 91% detection accuracy with 3.9% false alarm rate. We also utilize Synthetic Minority Over-sampling Technique (SMOTE) to create three synthetic datasets with different distributions based on the refined version of collected dataset to investigate impact of different sample sizes on accuracy of malware detection. Using SMOTE datasets we could achieve over 96% detection accuracy and false alarm of less than 4%.All malware classification experiments are tested using cross validation technique. Our results reflect that increasing sample size in synthetic datasets has direct positive effect on detection accuracy while increases false alarm rate in compare to the original dataset.

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MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

Cited by 351 publications

References 24 publications

Internet of Things Malware : A Survey

Internet of Things Malware : A Survey

Risk analysis of Android applications: A user-centric solution

Intelligent OS X malware threat detection with code inspection

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