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Crime·The Trace·Dual reading·Published 2026-08-16

use of ML, DL and RL models for cybersecurity defense

Source article: A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

Abstract: Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large data…

TRV-2026-0792Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 69The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 65The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions

BORDER SECURITY_2015 (VOLUME 1 OF 2) by Committee on Homeland Security and Governmental Affairs. Public domain

The quick read

Published January 2024, this IEEE Access survey reviews how machine learning, deep learning and reinforcement learning are applied to cybersecurity tasks such as malware detection, intrusion detection and vulnerability assessment, including evaluation of ChatGPT-like tools on both defensive and offensive sides.

It matters because cyberattacks cause significant harm, and AI methods are presented as essential for detecting unknown threats, yet the same methods face unresolved vulnerabilities to adversarial attacks and misuse that could undermine security gains.

Main points
  • Survey evaluates ML, DL and RL applications across malware detection, intrusion detection and vulnerability assessment.
  • ML uses statistical methods to identify patterns and anomalies in large datasets to detect previously unknown threats.
  • Authors also evaluated ChatGPT-like tools for both defensive and offensive cyber uses.
Gain

Machine learning, deep learning and reinforcement learning techniques improve cybersecurity systems' ability to detect and mitigate cyberattacks including malware and intrusions.

Problem

ML, DL and RL-based cybersecurity solutions are susceptible to adversarial attacks, and ChatGPT-like tools can be manipulated to threaten data integrity, confidentiality and availability.

The rundown

The survey organizes state-of-the-art studies by main idea, techniques and important findings for malware detection, intrusion detection, vulnerability assessment and other areas.

It notes deep learning potential in image and speech recognition contexts and reinforcement learning effectiveness in dynamic environments through trial-and-error learning.

It concludes ChatGPT can be valuable for cybersecurity while also posing risks when manipulated by adversaries.

What this doesn’t fix

Effectiveness claims are constrained by data quality, interpretability limits, and susceptibility to adversarial attacks that can undermine ML-based defenses.

Sources

Reader signal

How should this claim be treated?

The debate