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TRUVACE RECORD VERSION
record: TRV-2026-0792
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-16T14:21:48.485698Z
status: published
lens: trace
sector: crime
headline: A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
dek: 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…
gain_title: Machine learning, deep learning and reinforcement learning techniques improve cybersecurity systems' ability to detect and mitigate cyberattacks including malware and intrusions.
problem_title: 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.
trace_subject: use of ML, DL and RL models for cybersecurity defense
gain_reading: Machine learning, deep learning and reinforcement learning techniques improve cybersecurity systems' ability to detect and mitigate cyberattacks including malware and intrusions.
gain_evidence: have proven to be effective in detecting and mitigating cyberattacks
problem_reading: 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.
problem_evidence: susceptible to adversarial attacks | ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data
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.
limitation: Effectiveness claims are constrained by data quality, interpretability limits, and susceptibility to adversarial attacks that can undermine ML-based defenses.
tag: Dual reading
key_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.
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.
sources:
- peer_reviewed | IEEE Access | https://doi.org/10.1109/access.2024.3355547 | 2024-01-01
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