Predictive Analysis of Ransomware Attacks using Context-aware AI in IoT Systems

Vytarani Mathane,P.V Lakshmi

doi:10.14569/ijacsa.2021.0120432

Abstract

Ransomware attacks are emerging as a major source of malware intrusion in recent times. While so far ransomware has affected general-purpose adequately resourceful computing systems, there is a visible shift towards low-cost Internet of Things systems which tend to manage critical endpoints in industrial systems. Many ransomware prediction techniques are proposed but there is a need for more suitable ransomware prediction techniques for constrained heterogeneous IoT systems. Using attack context information profiles reduces the use of resources required by resource-constrained IoT systems. This paper presents a context-aware ransomware prediction technique that uses context ontology for extracting information features (connection requests, software updates, etc.) and Artificial Intelligence, Machine Learning algorithms for predicting ransomware. The proposed techniques focus and rely on early prediction and detection of ransomware penetration attempts to resource-constrained IoT systems. There is an increase of 60 % of reduction in time taken when using context-aware dataset over the non-context aware data.

Highlights

IoT systems are distinct from others in that they are ubiquitous, heterogeneous in capabilities, and usually out in adversarial environments [1]
They are present in Industries, Medical centers, Smart cars, Smart homes, Smart cities, and supply chains [2]. Such IoT systems could be susceptible to multiple categories of attacks like Denial of Service (DoS), botnets, man in the middle, identity and data theft attacks, ransomware attacks given the less than the secured or controlled environment of deployment and quite often limited security capabilities [3]
Building on the previous section, we aim to provide a solution for predicting ransomware attacks in lower-end IoT systems using context-aware AI algorithms methodologies

Summary

Introduction

IoT systems are distinct from others in that they are ubiquitous, heterogeneous in capabilities, and usually out in adversarial environments [1]. They are present in Industries, Medical centers, Smart cars, Smart homes, Smart cities, and supply chains [2] Such IoT systems could be susceptible to multiple categories of attacks like Denial of Service (DoS), botnets, man in the middle, identity and data theft attacks, ransomware attacks given the less than the secured or controlled environment of deployment and quite often limited security capabilities [3]. Among all those ransomware attacks could be more impacting owing to attack methodology where victim systems become unusable until a ransom is paid, typically have attacker-defined timelines to respond, and can cause more monetary loss. By using Botnets, Social engineering, and malvertisement (malicious advertising) ransomware can penetrate IoT devices [6]

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