Opcode graph similarity and metamorphic detection

Runwal, Neha; Low, Richard M.; Stamp, Mark

doi:10.1007/s11416-012-0160-5

Cited by 143 publications

(117 citation statements)

References 9 publications

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“…We use two different aspects of the instructions, the first one is instruction opcode and the second one is instruction category. Instruction opcode is one of the features previously used for static malware detection [52,54,10,67]. However, it is not common to use the opcodes for dynamic detection of malware.…”

Section: Features Related To Instructionsmentioning

confidence: 99%

“…Santos et al and Yan et al evaluate opcode sequence signatures [54,67], while in particular, opcode sequence signatures were found to effectively classify metamorphic malware. Runwal et al [52] study opcode sequence similarity graphs. These techniques obtain this information from running programs and malware inside heavyweight profiling tools such as Pin [15].…”

Section: Related Workmentioning

confidence: 99%

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Malware-aware processors: A framework for efficient online malware detection

Özsoy

Donovick

Gorelik

et al. 2015

2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)

115

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Security exploits and ensuant malware pose an increasing challenge to computing systems as the variety and complexity of attacks continue to increase. In response, software-based malware detection tools have grown in complexity, thus making it computationally difficult to use them to protect systems in real-time. Therefore, software detectors are applied selectively and at a low frequency, creating opportunities for malware to remain undetected. In this paper, we propose Malware-Aware Processors (MAP) -processors augmented with an online hardware-based detector to serve as the first line of defense to differentiate malware from legitimate programs. The output of this detector helps the system prioritize how to apply more expensive software-based solutions. The always-on nature of MAP detector helps protect against intermittently operating malware. Our work improves on the state of the art in the following ways: (1) We define and explore the use of sub-semantic features for online detection of malware. (2) We explore hardware implementations and show that simple classifiers appropriate for such implementations can effectively classify malware. We also study different classifiers, develop implementation optimizations, and explore complexity to performance trade-offs. (3) We propose a two-level detection framework where the hardware classifier prioritizes the work of a more accurate but more expensive software defense mechanism. (4) We integrate the MAP implementation with an open-source x86-compatible core, synthesizing the resulting design to run on an FPGA.

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Section: Features Related To Instructionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Malware-aware processors: A framework for efficient online malware detection

Özsoy

Donovick

Gorelik

et al. 2015

2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA)

115

View full text Add to dashboard Cite

show abstract

“…Others use sequence signatures of the opcodes [28,34]. Runwal et al use similarity graphs of opcode sequences [27]. However, these works used offline analysis.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Khasawneh

Özsoy

Donovick

et al. 2015

Research in Attacks, Intrusions, and Defenses

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Abstract. Recent work demonstrated hardware-based online malware detection using only low-level features. This detector is envisioned as a first line of defense that prioritizes the application of more expensive and more accurate software detectors. Critical to such a framework is the detection performance of the hardware detector. In this paper, we explore the use of both specialized detectors and ensemble learning techniques to improve performance of the hardware detector. The proposed detectors reduce the false positive rate by more than half compared to a single detector, while increasing the detection rate. We also contribute approximate metrics to quantify the detection overhead, and show that the proposed detectors achieve more than 11x reduction in overhead compared to a software only detector (1.87x compared to prior work), while improving detection time. Finally, we characterize the hardware complexity by extending an open core and synthesizing it on an FPGA platform, showing that the overhead is minimal.

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“…G2 viruses are one of several well-known metamorphic families. The benign files we use to compare against the G2 viruses are 16 specific Cygwin utility files [11] chosen for their representation as non-virus files in previous papers such as [3,21,30]. The exact files included in the benign data set are shown in Table 3.…”

Section: Test Data 411 Second Generation Virus Generatormentioning

confidence: 99%

Compression-based analysis of metamorphic malware

Lee

Austin

Stamp

2015

IJSN

Self Cite

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Compression-based Analysis of Metamorphic Malware by Jared LeeRecent work has presented a technique based on structural entropy measurement as an effective way to detect metamorphic malware. The technique uses two steps, file segmentation and sequence comparison, to calculate file similarity. In another previous work, it was observed that similar malware have similar measures of Kolmogorov complexity. A proposed method of estimating Kolmogorov complexity was to calculate the compression ratio of a given malware which could then be used to cluster the malicious software. Malware detection has also been attempted through the use of adaptive data compression and showed promising results. In this paper, we attempt to combine these concepts and propose using compression ratios as an alternative measure of entropy with the purpose of segmenting files according to their structural characteristics. We then compare the segment-based sequences of two given files to determine file similarity. The idea is that even after malware is transformed using a metamorphic engine, the resulting variants still share identifiable structural similarities with the original. Using this proposed technique to identify metamorphic malware, we compare our results with previous work. ACKNOWLEDGMENTS

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Opcode graph similarity and metamorphic detection

Cited by 143 publications

References 9 publications

Malware-aware processors: A framework for efficient online malware detection

Malware-aware processors: A framework for efficient online malware detection

Ensemble Learning for Low-Level Hardware-Supported Malware Detection

Compression-based analysis of metamorphic malware

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