Visible to the public Biblio

Filters: Author is Debbabi, Mourad  [Clear All Filters]
2019-06-10
Karbab, ElMouatez Billah, Debbabi, Mourad.  2018.  ToGather: Automatic Investigation of Android Malware Cyber-Infrastructures. Proceedings of the 13th International Conference on Availability, Reliability and Security. :20:1-20:10.

The popularity of Android, not only in handsets but also in IoT devices, makes it a very attractive target for malware threats, which are actually expanding at a significant rate. The state-of-the-art in malware mitigation solutions mainly focuses on the detection of malicious Android apps using dynamic and static analysis features to segregate malicious apps from benign ones. Nevertheless, there is a small coverage for the Internet/network dimension of Android malicious apps. In this paper, we present ToGather, an automatic investigation framework that takes Android malware samples as input and produces insights about the underlying malicious cyber infrastructures. ToGather leverages state-of-the-art graph theory techniques to generate actionable, relevant and granular intelligence to mitigate the threat effects induced by the malicious Internet activity of Android malware apps. We evaluate ToGather on a large dataset of real malware samples from various Android families, and the obtained results are both interesting and promising.

2019-01-31
Mohammady, Meisam, Wang, Lingyu, Hong, Yuan, Louafi, Habib, Pourzandi, Makan, Debbabi, Mourad.  2018.  Preserving Both Privacy and Utility in Network Trace Anonymization. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :459–474.

As network security monitoring grows more sophisticated, there is an increasing need for outsourcing such tasks to third-party analysts. However, organizations are usually reluctant to share their network traces due to privacy concerns over sensitive information, e.g., network and system configuration, which may potentially be exploited for attacks. In cases where data owners are convinced to share their network traces, the data are typically subjected to certain anonymization techniques, e.g., CryptoPAn, which replaces real IP addresses with prefix-preserving pseudonyms. However, most such techniques either are vulnerable to adversaries with prior knowledge about some network flows in the traces, or require heavy data sanitization or perturbation, both of which may result in a significant loss of data utility. In this paper, we aim to preserve both privacy and utility through shifting the trade-off from between privacy and utility to between privacy and computational cost. The key idea is for the analysts to generate and analyze multiple anonymized views of the original network traces; those views are designed to be sufficiently indistinguishable even to adversaries armed with prior knowledge, which preserves the privacy, whereas one of the views will yield true analysis results privately retrieved by the data owner, which preserves the utility. We formally analyze the privacy of our solution and experimentally evaluate it using real network traces provided by a major ISP. The results show that our approach can significantly reduce the level of information leakage (e.g., less than 1% of the information leaked by CryptoPAn) with comparable utility.

2018-01-23
Huang, He, Youssef, Amr M., Debbabi, Mourad.  2017.  BinSequence: Fast, Accurate and Scalable Binary Code Reuse Detection. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :155–166.
Code reuse detection is a key technique in reverse engineering. However, existing source code similarity comparison techniques are not applicable to binary code. Moreover, compilers have made this problem even more difficult due to the fact that different assembly code and control flow structures can be generated by the compilers even when implementing the same functionality. To address this problem, we present a fuzzy matching approach to compare two functions. We first obtain an initial mapping between basic blocks by leveraging the concept of longest common subsequence on the basic block level and execution path level. We then extend the achieved mapping using neighborhood exploration. To make our approach applicable to large data sets, we designed an effective filtering process using Minhashing. Based on the proposed approach, we implemented a tool named BinSequence and conducted extensive experiments with it. Our results show that given a large assembly code repository with millions of functions, BinSequence is efficient and can attain high quality similarity ranking of assembly functions with an accuracy of above 90%. We also present several practical use cases including patch analysis, malware analysis and bug search.
2017-03-20
Karbab, ElMouatez Billah, Debbabi, Mourad, Derhab, Abdelouahid, Mouheb, Djedjiga.  2016.  Cypider: Building Community-based Cyber-defense Infrastructure for Android Malware Detection. Proceedings of the 32Nd Annual Conference on Computer Security Applications. :348–362.

The popularity of Android OS has dramatically increased malware apps targeting this mobile OS. The daily amount of malware has overwhelmed the detection process. This fact has motivated the need for developing malware detection and family attribution solutions with the least manual intervention. In response, we propose Cypider framework, a set of techniques and tools aiming to perform a systematic detection of mobile malware by building an efficient and scalable similarity network infrastructure of malicious apps. Our detection method is based on a novel concept, namely malicious community, in which we consider, for a given family, the instances that share common features. Under this concept, we assume that multiple similar Android apps with different authors are most likely to be malicious. Cypider leverages this assumption for the detection of variants of known malware families and zero-day malware. It is important to mention that Cypider does not rely on signature-based or learning-based patterns. Alternatively, it applies community detection algorithms on the similarity network, which extracts sub-graphs considered as suspicious and most likely malicious communities. Furthermore, we propose a novel fingerprinting technique, namely community fingerprint, based on a learning model for each malicious community. Cypider shows excellent results by detecting about 50% of the malware dataset in one detection iteration. Besides, the preliminary results of the community fingerprint are promising as we achieved 87% of the detection.