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Machine Learning for Cyber Security

Submission Deadline: 30 October 2021 (closed)

Guest Editors

Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.
Dr. Dhanalakshmi R, KCG College of Technology, India.
Dr. Kong Fah TEE, University of Greenwich, United Kingdom.


Machine Learning is a computational concept that is concerned with automating the process of conceptual model building. It is an endeavour to replicate the functionality of the human brain. In today’s world, it is widely being used by corporate and research institutes. Machine Learning is implemented in domains such as banking, finance, healthcare and space exploration. Machine learning models are more vulnerable to attacks than methodical computer programs since the former is not usually built with cogitate security concerns. Ergo, the compromise in security of these models can lead to drastic effects and damage Security in ML models is imperative as these models superintend sensitive and confidential data.


Inviting research papers on the various security threats faced by machine learning models and the precautionary measures proposed to mitigate these risks as mentioned below.


- Deep learning systems and the encountered attacks

- IDS - A Machine Learning Approach

- Network Anomaly Detection - Machine Learning Perspective

- Data Analytics and Decision Support for Cyber Security

- Machine Learning and Security - Protecting systems with Data and algorithms

- Biometric Recognition systems - Poisoning Attacks - NN Model

- Model Inversion Attack

- Cyber Security and Applied Mathematics

- MiTM attacks and countermeasures

- Improved Detection of Intrusion and Enhanced Predicting Ability

- Attacks in Image classification

- Attacks in Speech recognition

- Attacks in Text Processing

- Secure implementation of deep learning systems

- Defense Mechanisms against various attacks

- Future directions of Attacks and defense implementing Data Science


Machine Learning, Cyber Security, Intrusion Detection System, Recognition systems

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