An Easy-to-use Framework to Build and Operate AI-based Intrusion Detection for In-situ Monitoring

Choi, Ikje; Lee, Jun; Kwon, Taewoong; Kim, Kyuil; Choi, Yoon-Su Jeong and Jeong-hee; Song, Jungsuk

doi:10.1109/asiajcis53848.2021.00011

Cited by 4 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These are the most recent datasets which provide characteristics of network attacks, which include new types of attacks [7]. Deep learning is already widely used to solve the detection problems of various network attacks [39][40][41]. In [42], the Deep Neural Network on NSL-KDD dataset was researched for effective attack detection.…”

Section: Related Workmentioning

confidence: 99%

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Imanbayev

Tynymbayev

Одарченко

et al. 2022

Sensors

View full text Add to dashboard Cite

The introduction of fifth generation mobile networks is underway all over the world which makes many people think about the security of the network from any hacking. Over the past few years, researchers from around the world have raised this issue intensively as new technologies seek to integrate into many areas of business and human infrastructure. This paper proposes to implement an IDS (Intrusion Detection System) machine learning approach into the 5G core architecture to serve as part of the security architecture. This paper gives a brief overview of intrusion detection datasets and compares machine learning and deep learning algorithms for intrusion detection. The models are built on the basis of two network data CICIDS2017 and CSE-CIC-IDS-2018. After testing, the ML and DL models are compared to find the best fit with a high level of accuracy. Gradient Boost emerged as the top method when we compared the best results based on metrics, displaying 99.3% for a secure dataset and 96.4% for attacks on the test set.

show abstract

Section: Related Workmentioning

confidence: 99%

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Imanbayev

Tynymbayev

Одарченко

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…[26] Development an establishment of ISMS [27] Awareness measures [28], [29], [30], [31], [32], [33] Lack of skilled workers in public sector and effects [34], [35] Physical Security and Security Assessment [36], [37], [38], [39], [40] Legal framework parameters in cybersecurity domain [41], [42] Maturity models [43], [44] Most papers deal with the safeguarding of the technical components or tackle the analysis of cyber-attacks and pos-sible preventive measures. It is striking that a large number of papers put the staff in the focus and examine how their awareness for cybersecurity can be increased.…”

Section: Thematic Areamentioning

confidence: 99%

Information security management in German local government

Moses¹,

Sandkuhl²,

Kemmerich³

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

View full text Add to dashboard Cite

4The growing importance of information security in organizations is undisputed. This is particularly true of local governments, because modern administrative action is no longer conceivable today without electronic communication media and IT procedures. The complexity of information technology, the increasing degree of networking (also with citizens) and the dependence of the administration on ITsupported procedures has led to the fact that the security of information technology and associated processes must be given a higher priority and a corresponding cybersecurity strategy must be substantiated. Existing approaches either fall short or cannot be applied to the context of local government without revision and adaptation. In this article, case studies of implementations of IT security projects in local government are examined. Specific focus is on the differences between information security management system (ISMS) implementations of different hierarchical levels of governmental organizations. The results show current challenges in increasing the resilience of the local government.

show abstract

“…Furthermore, deep learning-based anomaly detection systems are actively discussed [47]. However, in real-world SOCs, the potential of human security experts may be more trusted than the automated methods so that some SOCs utilize or develop practical machine learning-based anomaly detection solutions combined with information visualization [48,49], which is out of our scope.…”

Section: The Goals Of Network Traffic Analysis For Socsmentioning

confidence: 99%

A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers

Roh

2021

Applied Sciences

View full text Add to dashboard Cite

Recently, a majority of security operations centers (SOCs) have been facing a critical issue of increased adoption of transport layer security (TLS) encryption on the Internet, in network traffic analysis (NTA). To this end, in this survey article, we present existing research on NTA and related areas, primarily focusing on TLS-encrypted traffic to detect and classify malicious traffic with deployment scenarios for SOCs. Security experts in SOCs and researchers in academia can obtain useful information from our survey, as the main focus of our survey is NTA methods applicable to malware detection and family classification. Especially, we have discussed pros and cons of three main deployment models for encrypted NTA: TLS interception, inspection using cryptographic functions, and passive inspection without decryption. In addition, we have discussed the state-of-the-art methods in TLS-encrypted NTA for each component of a machine learning pipeline, typically used in the state-of-the-art methods.

show abstract

An Easy-to-use Framework to Build and Operate AI-based Intrusion Detection for In-situ Monitoring

Cited by 4 publications

References 22 publications

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond

Information security management in German local government

A Survey on TLS-Encrypted Malware Network Traffic Analysis Applicable to Security Operations Centers

Contact Info

Product

Resources

About