Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review

Abstract

Machine learning is of rising importance in cybersecurity. The primary objective of applying machine learning in cybersecurity is to make the process of malware detection more actionable, scalable and effective than traditional approaches, which require human intervention. The cybersecurity domain involves machine learning challenges that require efficient methodical and theoretical handling. Several machine learning and statistical methods, such as deep learning, support vector machines and Bayesian classification, among others, have proven effective in mitigating cyber-attacks. The detection of hidden trends and insights from network data and building of a corresponding data-driven machine learning model to prevent these attacks is vital to design intelligent security systems. In this survey, the focus is on the machine learning techniques that have been implemented on cybersecurity data to make these systems secure. Existing cybersecurity threats and how machine learning techniques have been used to mitigate these threats have been discussed. The shortcomings of these state-of-the-art models and how attack patterns have evolved over the past decade have also been presented. Our goal is to assess how effective these machine learning techniques are against the ever-increasing threat of malware that plagues our online community.

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

“…The e-health apps enabled with 5G-IoT and deep learning models such as CNN-DMA are useful for ensuring data safety and system security against cyberattacks. The deep learning models can be trained directly from original data, such as images and text [218]. Therefore, raw data does not need to be preprocessed before being used for training.…”

Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation

“…Traditional security systems based on predefined rules and signatures are often insufficient to defend against advanced and adaptive threats [ 3 ]. Machine learning (ML) models, on the other hand, can provide a more flexible and adaptive solution to cybersecurity by learning from historical data and evolving over time to better detect and respond to new threats [ 4 , 5 , 6 ].…”

“…However, there are still significant challenges that need to be addressed to effectively apply RL in the context of cybersecurity. One of the primary challenges is the lack of training data [ 5 , 19 ]. Adversarial cyber-attack scenarios are often rare and complex, making it difficult to collect sufficient data to train RL models effectively.…”

LoGD-ai: An Efficient Network Intrusion Detection System Using a Soft Voting-Based Ensemble Learner

“…With the widespread usage of Internet applications, numerous network security issues occur on a regular basis, weakening network security. The vulnerabilities in cyberspace have led to various cyber-attacks including unauthorized access, denial of service (DoS), malware attacks, zero-day attacks, data breaches, social engineering, or phishing [5]- [7]. In May 2017, a ransomware virus caused massive losses in several areas, including banking, energy, medical care, and colleges.…”