Deep Learning-based Intrusion Detection Systems: A Survey
By: Zhiwei Xu , Yujuan Wu , Shiheng Wang and more
Potential Business Impact:
Finds computer attacks faster using smart learning.
Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made great progress due to their increasing generalizability. The rationale behind this is that by learning the underlying patterns of known system behaviors, IDS detection can be generalized to intrusions that exploit zero-day vulnerabilities. In this survey, we refer to this type of IDS as DL-based IDS (DL-IDS). From the perspective of DL, this survey systematically reviews all the stages of DL-IDS, including data collection, log storage, log parsing, graph summarization, attack detection, and attack investigation. To accommodate current researchers, a section describing the publicly available benchmark datasets is included. This survey further discusses current challenges and potential future research directions, aiming to help researchers understand the basic ideas and visions of DL-IDS research, as well as to motivate their research interests.
Similar Papers
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
Cryptography and Security
Keeps computers safe from hackers.
Intrusion Detection System Using Deep Learning for Network Security
Cryptography and Security
Finds bad computer stuff to keep networks safe.
Leveraging Machine Learning Techniques in Intrusion Detection Systems for Internet of Things
Cryptography and Security
Makes smart devices safer from hackers.