Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks.
As the number and cleverness of cyber-attacks keep increasing rapidly, it's more important than ever to have good ways to detect and prevent them. Recognizing cyber threats quickly and accurately is crucial because they can cause severe damage to individuals and businesses. This paper takes a close look at how we can use artificial intelligence (AI), including machine learning (ML) and deep learning (DL), alongside metaheuristic algorithms to detect cyber-attacks better. We've thoroughly examined over sixty recent studies to measure how effective these AI tools are at identifying and fighting a wide range of cyber threats. Our research includes a diverse array of cyberattacks such as malware attacks, network intrusions, spam, and others, showing that ML and DL methods, together with metaheuristic algorithms, significantly improve how well we can find and respond to cyber threats. We compare these AI methods to find out what they're good at and where they could improve, especially as we face new and changing cyber-attacks. This paper presents a straightforward framework for assessing AI Methods in cyber threat detection. Given the increasing complexity of cyber threats, enhancing AI methods and regularly ensuring strong protection is critical. We evaluate the effectiveness and the limitations of current ML and DL proposed models, in addition to the metaheuristic algorithms. Recognizing these limitations is vital for guiding future enhancements. We're pushing for smart and flexible solutions that can adapt to new challenges. The findings from our research suggest that the future of protecting against cyber-attacks will rely on continuously updating AI methods to stay ahead of hackers' latest tricks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.