Fast automated unpacking and classification of malware pdf

Download Now Download Download to read offline. Silvio Cesare Follow. PhD student. Exome sequencing for disease gene identification and patient diagnostics, Gen Related Books Free with a 30 day trial from Scribd. Uncommon Carriers John McPhee. The Art of War Sun Tsu. Related Audiobooks Free with a 30 day trial from Scribd. Elizabeth Howell. Fast Automated Unpacking and Classification of Malware 1. Who am I and where did this talk come from?

Introduction 4. Structure of the Presentation 1. Related Work 2. Problem Definition and Our Approach 3. Automated Unpacking 4. Malware Feature Extraction 5. Malware Classification 6. Conclusions and Future Work 8.

Related Work 9. Control Flow Features Figure. A control flow graph left , and a call graph right. However, in case of Mydoom family, two distinct behaviors could be classified. Therefore, they should be considered as distinct family had distance less than 0.

In addition, binaries in the Netsky family showed Bagle family were grouped in the same family. However, only multiple behaviors. Only In the results of [9], the effectiveness for Netsky same behaviors. The results for the this family have distinctly different behaviors. This was proposed method are consistent with the results of Figure 1. As shown in Figure 4, Further, malware families related to mass-mailing worms most binaries in the set for each of the two families, Mydoom showed similar behaviors to each other.

Only 5. This result can also be seen in the results of group. In last, Figure 3 shows the distance between the malicious pro- the results above are similar to the scanning result by McAfee, grams and benign non-malicious Windows programs, termed an anti-virus software. The anti-virus software labeled 15 the false positive rate. The binaries in Mytob, Agent, were classified as the different families. Figure 4 shows the results for FACT. For this experiment, 4. The experiment was run 7 times; only one run is have been presented.

Static analysis of binaries, depending shown other results were similar. For this run, a random on byte sequences, has long been used. These techniques sample was differently selected, and 6 different groups were are not effective against malware that employs concealment classified according to the proposed method. The Agent and techniques, such as code polymorphism and obfuscation. Bailey, J. Oberheide, J.

Andersen, Z. Mao, and F. Bagle 43 0 0 2 0 0 andJose Nazario. Automated classification and analysis of Mydoom 6 0 0 16 28 0 internet malware. Figure 4: Results of Feedback-based Classification [2] U. Bayer, P. Milani Comparetti, C. Hlauscheck, C. Kruegel, and E. Scalable, Behavior-Based Malware Clustering.

Technique FACT. Each group can be a family. Bunke and K. A graph distance metric based on group size over the training set with a percentage.

Christodorescu, S. Jha, and C. Mining specifications of malicious behavior. In Proceedings of the proposed, using control and data flow graph information. Conte, P. Foggia, and M. Challenging complexity of maximum common subgraph detection algorithms: A packed. Another example is MetaAware [18], which measured performance analysis of three algorithms on a wide database the similarity between two malware binaries based on system of graphs.

Graph Algorithms Appl. Kruegel et al. Symantec global internet security threat report, polymorphic variants of malicious programs having the same April Dinaburg, P. Royal, M. Sharif, and W. Ether: Due to the limitations of static analysis, dynamic run- malware analysis via hardware virtualization extensions.

In time detection and classification of malware is gaining inter- ACM Conference on Computer and Communications Security, pages 51—62, The method is this paper is closely related to such work. Hu, T. Chiueh, and K. Large-scale malware Kolbitsch et al. In Proceedings of the behavioral graph for malware detection. It aims to find indirect depen- Skip to search form Skip to main content Skip to account menu You are currently offline.

Some features of the site may not work correctly. Identification of malware variants provides great benefit in early detection. Control flow has been proposed as a characteristic that can be identified across variants, resulting in flowgraph based malware classification. Static analysis is widely used for the classification but can be ineffective if malware undergoes a code packing transformation to hide its real content.

This paper proposes a novel algorithm for… Expand. Save to Library Save. Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Figures, Tables, and Topics from this paper. Citation Type.