Abstract
Malware brings constant threats to the services and facilities used by modern society. In order to perform and improve anti-malware defense, there is a need for methods that are capable of malware categorization. As malware grouped into categories according to its functionality, dynamic malware analysis is a reliable source of features that are useful for malware classification. Different types of dynamic features are described in literature [5, 6, 13]. These features can be divided into two main groups: high-level features (API calls, File activity, Network activity, etc.) and low-level features (memory access patterns, high-performance counters, etc). Low-level features bring special interest for malware analysts: regardless of the anti-detection mechanisms used by malware, it is impossible to avoid execution on hardware. As hardware-based security solutions are constantly developed by hardware manufacturers and prototyped by researchers, research on low-level features used for malware analysis is a promising topic. The biggest problem with low-level features is that they don’t bring much information to a human analyst. In this paper, we analyze potential correlation between the low- and high-level features used for malware classification. In particular, we analyze n-grams of memory access operations found in [6] and try to find their relationship with n-grams of API calls. We also compare performance of API calls and memory access n-grams on the same dataset as used in [6]. In the end, we analyze their combined performance for malware classification and explain findings in the correlation between high- and low-level features.
The research leading to these results has received funding from the Center for Cyber and Information Security, under budget allocation from the Ministry of Justice and Public Security.
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References
Types of malware. https://usa.kaspersky.com/resource-center/threats/types-of-malware. Accessed 17 Mar 2019
Virusshare.com. https://virusshare.com/. Accessed 12 Mar 2019
Weka: Data mining software in Java (2019). http://www.cs.waikato.ac.nz/ml/weka/. Accessed 12 Mar 2019
Alazab, M., Layton, R., Venkataraman, S., Watters, P.: Malware detection based on structural and behavioural features of api calls (2010)
Bahador, M.B., Abadi, M., Tajoddin, A.: HPCMalHunter: behavioral malware detection using hardware performance counters and singular value decomposition. In: 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE), pp. 703–708. IEEE (2014). https://doi.org/10.1109/iccke.2014.6993402
Banin, S., Dyrkolbotn, G.O.: Multinomial malware classification via low-level features. Digit. Invest. 26, S107–S117 (2018). https://doi.org/10.1016/j.diin.2018.04.019
Banin, S., Shalaginov, A., Franke, K.: Memory access patterns for malware detection. (NISK) 96–107 (2016). Norsk informasjonssikkerhetskonferanse
Cole, E.: Advanced Persistent Threat: Understanding the Danger and How to Protect Your Organization. Newnes, Amsterdam (2012)
Hoglund, G.: What APT Means To Your Enterprise (2011). https://pdfs.semanticscholar.org/d0a0/47c6b19fc3645973f8f300b507886b54196a.pdf
Group, T.R.: Testimon research group (2017). https://testimon.ccis.no/
Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)
IntelPin: A dynamic binary instrumentation tool (2019)
Islam, R., Tian, R., Batten, L.M., Versteeg, S.: Classification of malware based on integrated static and dynamic features. J. Netw. Comput. Appl. 36(2), 646–656 (2013). https://doi.org/10.1016/j.jnca.2012.10.004
Khasawneh, K.N., Ozsoy, M., Donovick, C., Abu-Ghazaleh, N., Ponomarev, D.: Ensemble learning for low-level hardware-supported malware detection. In: Bos, H., Monrose, F., Blanc, G. (eds.) RAID 2015. LNCS, vol. 9404, pp. 3–25. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26362-5_1
Kononenko, I., Kukar, M.: Machine Learning and Data Mining: Introduction to Principles and Algorithms. Horwood Publishing, Cambridge (2007)
Lim, H.I.: Detecting malicious behaviors of software through analysis of api sequence k-grams i (2016). https://doi.org/10.13189/csit.2016.040301
Ozsoy, M., Donovick, C., Gorelik, I., Abu-Ghazaleh, N., Ponomarev, D.: Malware-aware processors: a framework for efficient online malware detection. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA), pp. 651–661. IEEE (2015). https://doi.org/10.1109/hpca.2015.7056070
Ozsoy, M., Khasawneh, K.N., Donovick, C., Gorelik, I., Abu-Ghazaleh, N., Ponomarev, D.: Hardware-based malware detection using low-level architectural features. IEEE Trans. Comput. 65(11), 3332–3344 (2016). https://doi.org/10.1109/tc.2016.2540634
Reuters: Ukraine’s power outage was a cyber attack: Ukrenergo (2017). https://www.reuters.com/article/us-ukraine-cyber-attack-energy/ukraines-power-outage-was-a-cyber-attack-ukrenergo-idUSKBN1521BA
Shalaginov, A., Grini, L.S., Franke, K.: Understanding neuro-fuzzy on a class of multinomial malware detection problems. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 684–691. IEEE (2016). https://doi.org/10.1109/ijcnn.2016.7727266
Shijo, P., Salim, A.: Integrated static and dynamic analysis for malware detection. Procedia Comput. Sci. 46, 804–811 (2015). https://doi.org/10.1016/j.procs.2015.02.149
Tang, A., Sethumadhavan, S., Stolfo, S.J.: Unsupervised anomaly-based malware detection using hardware features. In: Stavrou, A., Bos, H., Portokalidis, G. (eds.) RAID 2014. LNCS, vol. 8688, pp. 109–129. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11379-1_6
The Verge: the petya ransomware is starting to look like a cyberattack in disguise (2017). https://www.theverge.com/2017/6/28/15888632/petya-goldeneye-ransomware-cyberattack-ukraine-russia
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Appendix A. Raw Data Sample
Appendix A. Raw Data Sample
In this Appendix we present a sample of a raw data gather during our experiments. We also explain each field included in the data.
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1.
Opcode id: each opcode is given a unique identifier. If this opcode is executed again (e.g. in a loop), it will receive the same id.
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Module name: a name of a module where current instruction is executed, It can be a name of a library or a name of an executable itself.
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Section name: a name of a section in executable file or library where current instruction is executed. Often it will be .text or CODE, however it some cases (especially with malware) a name of an executable section can be different from standard.
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Current function name: if a function name of a current instruction can be found we record it to understand which function performed a certain part of logic.
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Opcode: text representation of an assembly instruction together with arguments but without arguments values.
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Type of module: whether an instruction is executed from the main module of executable under analysis or from the external library.
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Memory operations: memory operations performed by an instruction. Only read or write without addresses and values.
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Name of a function being called: if a current instruction is call - a name of a function is being stored.
A real example of raw data is present in the Listing 2. The first line represents header: names of fields are in the same order as in the list above.
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Banin, S., Dyrkolbotn, G.O. (2019). Correlating High- and Low-Level Features:. In: Attrapadung, N., Yagi, T. (eds) Advances in Information and Computer Security. IWSEC 2019. Lecture Notes in Computer Science(), vol 11689. Springer, Cham. https://doi.org/10.1007/978-3-030-26834-3_9
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