A Survey of Adversarial Machine Learning in Cyber Warfare

  • Vasisht Duddu Indraprastha Institute of Information Technology, Delhi
Keywords: Adversarial machine learning, Adversary modelling, Cyber attacks, Security, Privacy


The changing nature of warfare has seen a paradigm shift from the conventional to asymmetric, contactless warfare such as information and cyber warfare. Excessive dependence on information and communication technologies, cloud infrastructures, big data analytics, data-mining and automation in decision making poses grave threats to business and economy in adversarial environments. Adversarial machine learning is a fast growing area of research which studies the design of Machine Learning algorithms that are robust in adversarial environments. This paper presents a comprehensive survey of this emerging area and the various techniques of adversary modelling. We explore the threat models for Machine Learning systems and describe the various techniques to attack and defend them. We present privacy issues in these models and describe a cyber-warfare test-bed to test the effectiveness of the various attack-defence strategies and conclude with some open problems in this area of research.


Author Biography

Vasisht Duddu, Indraprastha Institute of Information Technology, Delhi

Mr Vasisht Duddu is pursuing BTech (Electronics and Communications Engineering) from Indraprastha Institute of Information Technology (IIIT), Delhi and is currently working as a researcher at System Security Lab, School of Computing, National University of Singapore (NUS), Singapore. His primary areas of research are security, privacy, anonymity and applied cryptography.

How to Cite
Duddu, V. (2018). A Survey of Adversarial Machine Learning in Cyber Warfare. Defence Science Journal, 68(4), 356-366. https://doi.org/10.14429/dsj.68.12371
Computers & Systems Studies