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1. Representing string computations as graphs for classifying malware

   https://doi.org/10.1145/3387905.3388595  

   Vecchio, Justin Del ; Ko, Steven Y. ; Ziarek, Lukasz ( July 2020 , MOBILESoft '20: Proceedings of the IEEE/ACM 7th International Conference on Mobile Software Engineering and Systems)

   null (Ed.)

   Android applications rely heavily on strings for sensitive operations like reflection, access to system resources, URL connections, database access, among others. Thus, insight into application behavior can be gained through not only an analysis of what strings an application creates but also the structure of the computation used to create theses strings, and in what manner are these strings used. In this paper we introduce a static analysis of Android applications to discover strings, how they are created, and their usage. The output of our static analysis contains all of this information in the form of a graph which we call a string computation. We leverage the results to classify individual application behavior with respect to malicious or benign intent. Unlike previous work that has focused only on extraction of string values, our approach leverages the structure of the computation used to generate string values as features to perform classification of Android applications. That is, we use none of the static analysis computed string values, rather using only the graph structures of created strings to do classification of an arbitrary Android application as malware or benign. Our results show that leveraging string computation structures as features can yield precision and recall rates as high as 97% on modern malware. We also provide baseline results against other malware detection tools and techniques to classify the same corpus of applications. 

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2. Object Allocation Pattern as an Indicator for Maliciousness - An Exploratory Analysis

   https://doi.org/10.1145/3422337.3450322  

   Hussaini, Adamu ; Zahran, Bassam ; Ali-Gombe, Aisha ( April 2021 , ACM CODASPY 2021)

   null (Ed.)

   Traditionally, Android malware is analyzed using static or dynamic analysis. Although static techniques are often fast; however, they cannot be applied to classify obfuscated samples or malware with a dynamic payload. In comparison, the dynamic approach can examine obfuscated variants but often incurs significant runtime overhead when collecting every important malware behavioral data. This paper conducts an exploratory analysis of memory forensics as an alternative technique for extracting feature vectors for an Android malware classifier. We utilized the reconstructed per-process object allocation network to identify distinguishable patterns in malware and benign application. Our evaluation results indicate the network structural features in the malware category are unique compared to the benign dataset, and thus features extracted from the remnant of in-memory allocated objects can be utilized for robust Android malware classification algorithm. 

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3. Impeding Behavior-based Malware Analysis via Replacement Attacks to Malware Specications

   Ming, Jiang ; Xin, Zhi ; Lan, Pengwei ; Wu, Dinghao ; Liu, Peng ; Mao, Bing ( August 2017 , Journal of computer virology and hacking techniques)

   As the underground market of malware flourishes, there is an exponential increase in the number and diversity of malware. A crucial question in malware analysis research is how to define malware specifications or signatures that faithfully describe similar malicious intent and also clearly stand out from other programs. Although the traditional malware specifications based on syntactic signatures are efficient, they can be easily defeated by various obfuscation techniques. Since the malicious behavior is often stable across similar malware instances, behavior-based specifications which capture real malicious characteristics during run time, have become more prevalent in anti-malware tasks, such as malware detection and malware clustering. This kind of specification is typically extracted from the system call dependence graph that a malware sample invokes. In this paper, we present replacement attacks to cam- ouflage similar behaviors by poisoning behavior-based specifications. The key method of our attacks is to replace a system call dependence graph to its semantically equivalent variants so that the similar malware samples within one family turn out to be different. As a result, malware analysts have to put more efforts into reexamining the similar samples which may have been investigated before. We distil general attacking strategies by mining more than 5, 200 malware samples’ behavior specifications and implement a compiler-level prototype to automate replacement attacks. Experiments on 960 real malware samples demonstrate the effectiveness of our approach to impede various behavior-based mal- ware analysis tasks, such as similarity comparison and malware clustering. In the end, we also discuss possible countermeasures in order to strengthen existing mal- ware defense. 

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4. Hybrid Analysis of Android Apps for Security Vetting using Deep Learning

   Chaulagain, Dewan ; Poudel, Prabesh ; Pathak, Prabesh ; Roy, Sankardas ; Caragea, Doina ; Liu, Guojun ; Ou, Xinming ( July 2020 , 2020 IEEE Conference on Communications and Network Security (CNS))

   The phenomenal growth in use of android devices in the recent years has also been accompanied by the rise of android malware. This reality warrants development of tools and techniques to analyze android apps in large scale for security vetting. Most of the state-of-the-art vetting tools are either based on static analysis or on dynamic analysis. Static analysis has limited success if the malware app utilizes sophisticated evading tricks. Dynamic analysis on the other hand may not find all the code execution paths, which let some malware apps remain undetected. Moreover, the existing static and dynamic analysis vetting techniques require extensive human interaction. To ad- dress the above issues, we design a deep learning based hybrid analysis technique, which combines the complementary strengths of each analysis paradigm to attain better accuracy. Moreover, automated feature engineering capability of the deep learning framework addresses the human interaction issue. In particular, using lightweight static and dynamic analysis procedure, we obtain multiple artifacts, and with these artifacts we train the deep learner to create independent models, and then combine them to build a hybrid classifier to obtain the final vetting decision (malicious apps vs. benign apps). The experiments show that our best deep learning model with hybrid analysis achieves an area under the precision-recall curve (AUC) of 0.9998. In this paper, we also present a comparative study of performance measures of the various variants of the deep learning framework. Additional experiments indicate that our vetting system is fairly robust against imbalanced data and is scalable. 

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5. When Malware is Packin' Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features

   https://doi.org/10.14722/ndss.2020.24310  

   Aghakhani, Hojjat ; Gritti, Fabio ; Mecca, Francesco ; Lindorfer, Martina ; Ortolani, Stefano ; Balzarotti, Davide ; Vigna, Giovanni ; Kruegel, Christopher ( January 2020 , Network and Distributed Systems Security (NDSS) Symposium 2020)

   Machine learning techniques are widely used in addition to signatures and heuristics to increase the detection rate of anti-malware software, as they automate the creation of detection models, making it possible to handle an ever-increasing number of new malware samples. In order to foil the analysis of anti-malware systems and evade detection, malware uses packing and other forms of obfuscation. However, few realize that benign applications use packing and obfuscation as well, to protect intellectual property and prevent license abuse. In this paper, we study how machine learning based on static analysis features operates on packed samples. Malware researchers have often assumed that packing would prevent machine learning techniques from building effective classifiers. However, both industry and academia have published results that show that machine-learning-based classifiers can achieve good detection rates, leading many experts to think that classifiers are simply detecting the fact that a sample is packed, as packing is more prevalent in malicious samples. We show that, different from what is commonly assumed, packers do preserve some information when packing programs that is “useful” for malware classification. However, this information does not necessarily capture the sample’s behavior. We demonstrate that the signals extracted from packed executables are not rich enough for machine-learning-based models to (1) generalize their knowledge to operate on unseen packers, and (2) be robust against adversarial examples. We also show that a na¨ıve application of machine learning techniques results in a substantial number of false positives, which, in turn, might have resulted 

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