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Static Malware Analysis Using Machine Learning Methods

  • Conference paper
Recent Trends in Computer Networks and Distributed Systems Security (SNDS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 420))

Abstract

Malware analysis forms a critical component of cyber defense mechanism. In the last decade, lot of research has been done, using machine learning methods on both static as well as dynamic analysis. Since the aim and objective of malware developers have changed from just for fame to political espionage or financial gain, the malware is also getting evolved in its form, and infection methods. One of the latest form of malware is known as targeted malware, on which not much research has happened. Targeted malware, which is a superset of Advanced Persistent Threat (APT), is growing in its volume and complexity in recent years. Targeted Cyber attack (through targeted malware) plays an increasingly malicious role in disrupting the online social and financial systems. APTs are designed to steal corporate / national secrets and/or harm national/corporate interests. It is difficult to recognize targeted malware by antivirus, IDS, IPS and custom malware detection tools. Attackers leverage compelling social engineering techniques along with one or more zero day vulnerabilities for deploying APTs. Along with these, the recent introduction of Crypto locker and Ransom ware pose serious threats to organizations/nations as well as individuals. In this paper, we compare various machine-learning techniques used for analyzing malwares, focusing on static analysis.

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Author information

Authors and Affiliations

  1. Center for Information Assurance & Management, Institute for Development and Research in Banking Technology, India

    Hiran V. Nath & Babu M. Mehtre

  2. School of Computer and Information Sciences (SCIS), University of Hyderabad, India

    Hiran V. Nath

Authors
  1. Hiran V. Nath
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  2. Babu M. Mehtre
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Editor information

Editors and Affiliations

  1. Facultad de Informatica, Campus de Espinardo, s/n, 30., 100, Murcia, Spain

    Gregorio Martínez Pérez

  2. Indian Institute of Information Technology and Management, Technopark Campus, 695581, Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Computing and Mathematical Sciences, The University of Waikato, Waikato Mail Centre, Private Bag 3105, Hamilton, New Zealand

    Ryan Ko

  4. Guangdong University of Petrochemical Technology, P.R. China

    Lei Shu

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Nath, H.V., Mehtre, B.M. (2014). Static Malware Analysis Using Machine Learning Methods. In: Martínez Pérez, G., Thampi, S.M., Ko, R., Shu, L. (eds) Recent Trends in Computer Networks and Distributed Systems Security. SNDS 2014. Communications in Computer and Information Science, vol 420. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54525-2_39

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  • DOI: https://doi.org/10.1007/978-3-642-54525-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54524-5

  • Online ISBN: 978-3-642-54525-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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