System Call Dependence Graphs Research Articles

Detection and classification of malicious software utilizing Max-Flows between system-call groups

In this work, we present a graph-based method for the detection and classification of malicious software samples utilizing the Max-Flows exhibited through their corresponding behavioral graphs. In the proposed approach, we utilize the Max-Flows exhibited in the behavioral graphs that represent the interaction of software samples with their host environment, in order to depict the flow of information between System-call Groups. Obtaining the System-call Dependency Graphs of the samples under consideration, we construct the corresponding Group Relation Graphs, and proceed with the construction of the so-called, Flow Maps, another representation of Group Relation Graphs, that depict the Max-Flows among its vertices. Additionally, we provide a detailed representation over the architecture and the core components of our proposed approach for malware detection and classification discussing also several technical aspects regarding its implementation and deployment. Finally, we conduct a series of five-fold cross validation experiments in order to evaluate the potentials of our proposed approach in detecting and classifying malicious samples discussing also the exhibited experimental results.

System Call Dependence Graph Based Behavior Decomposition of Android Applications

Millions of developers and third-party organizations have flooded into the Android ecosystem due to Android’s open-source feature and low barriers to entry for developers. .However, that also attracts many attackers. Over 90 percent of mobile malware is found targeted on Android. Though Android provides multiple security features and layers to protect user data and system resources, there are still some overprivileged applications in Google Play Store or third-party Android app stores at wild. In this paper, we proposed an approach to map system level behavior and Android APIs, based on the observation that system level behaviors cannot be avoidedbut sensitive Android APIs could be evaded.To the best of our knowledge, our approach provides the first work to decompose Android application behaviors based on system-level behaviors. We then map system level behaviors and Android APIs through System Call Dependence Graphs. The study also shows that our approach can effectively identify potential permission abusing, with an almost negligible performance impact.

SYSTEM CALL DEPENDENCE GRAPH BASED BEHAVIOR DECOMPOSITION OF ANDROID APPLICATIONS

Millions of developers and third-party organizations have flooded into the Android ecosystem due to Android’s open-source feature and low barriers to entry for developers. .However, that also attracts many attackers. Over 90 percent of mobile malware is found targeted on Android. Though Android provides multiple security features and layers to protect user data and system resources, there are still some overprivileged applications in Google Play Store or third-party Android app stores at wild. In this paper, we proposed an approach to map system level behavior and Android APIs, based on the observation that system level behaviors cannot be avoided but sensitive Android APIs could be evaded. To the best of our knowledge, our approach provides the first work to decompose Android application behaviors based on system-level behaviors. We then map system level behaviors and Android APIs through System Call Dependence Graphs. The study also shows that our approach can effectively identify potential permission abusing, with an almost negligible performance impact.

A graph-based model for malware detection and classification using system-call groups

In this paper we present a graph-based model that, utilizing relations between groups of System-calls, detects whether an unknown software sample is malicious or benign, and classifies a malicious software to one of a set of known malware families. More precisely, we utilize the System-call Dependency Graphs (or, for short, ScD-graphs), obtained by traces captured through dynamic taint analysis. We design our model to be resistant against strong mutations applying our detection and classification techniques on a weighted directed graph, namely Group Relation Graph, or Gr-graph for short, resulting from ScD-graph after grouping disjoint subsets of its vertices. For the detection process, we propose the $$\Delta $$ -similarity metric, and for the process of classification, we propose the SaMe-similarity and NP-similarity metrics consisting the SaMe-NP similarity. Finally, we evaluate our model for malware detection and classification showing its potentials against malicious software measuring its detection rates and classification accuracy.

Replacement attacks: automatically evading behavior-based software birthmark

Software birthmarks utilize certain specific program characteristics to validate the origin of software, so it can be applied to detect software piracy. One state-of-the-art technology on software birthmark adopts dynamic system call dependence graphs as the unique signature of a program, which cannot be cluttered by existing obfuscation techniques and is also immune to the no-ops system call insertion attack. In this paper, we analyze its weaknesses and construct replacement attacks with the help of semantics equivalent system calls to unlock the high frequency dependencies between the system calls in the victim's original system call dependence graph. Our results show that the proposed replacement attacks can destroy the original birthmark successfully.