Detection of Intrusions with Machine Learning Methods

被引：0

作者：

Bostanci, Beyzanur ^{[1
]}

Albayrak, Ahmet ^{[2
]}

机构：

[1] Duzce Univ, Grad Sch Educ Comp Engn, Duzce, Turkey

[2] Duzce Univ, Comp Engn Engn Fac, Duzce, Turkey

来源：

2ND INTERNATIONAL INFORMATICS AND SOFTWARE ENGINEERING CONFERENCE (IISEC) | 2021年

关键词：

Big Data; Support Vector Machine; Random Forest; Machine Learning; BIG DATA;

D O I：

10.1109/IISEC54230.2021.9672361

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Today, especially with the emergence of social networks and IoT technologies, big data has entered the literature. With the development of technology, the size of the data has increased and accordingly data security gaps have emerged. In this study, Support Vector Machines and Random Forest algorithms, which are Supervised Machine Learning Algorithms, were used to analyze a data set consisting of unauthorized network logins. As a result of the experimental studies, it was observed that both algorithms produced good results, but the Random Forest approach produced better results.

引用

页数：4

共 50 条

[1] Comparison of Machine Learning Algorithms for Detection of Network Intrusions
Li, Zhida
Batta, Prerna
Trajkovic, Ljiljana
[J]. 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 4242 - 4247
[2] An Intellectual Detection System for Intrusions based on Collaborative Machine Learning
Dhikhi, T.
Saravanan, M. S.
[J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2020, 11 (02) : 446 - 452
[3] A Novel Framework for Detecting Network Intrusions Based on Machine Learning Methods
Omarov, Batyrkhan
Abdinurova, Nazgul
Abdulkhamidov, Zhamshidbek
[J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (07) : 499 - 510
[4] An Intelligent Detection of Malicious Intrusions in IoT Based on Machine Learning and Deep Learning Techniques
Iftikhar, Saman
Khan, Danish
Al-Madani, Daniah
Alheeti, Khattab M. Ali
Fatima, Kiran
[J]. COMPUTER SCIENCE JOURNAL OF MOLDOVA, 2022, 30 (03) : 288 - 307
[5] Machine Learning techniques optimized by Practical Swarm optimization for Intrusions Detection in IoT
Belaissaoui, Mustapha
Maleh, Yassine
[J]. JOURNAL OF INFORMATION ASSURANCE AND SECURITY, 2021, 16 (03): : 105 - 116
[6] Machine Learning Methods for Software Vulnerability Detection
Chernis, Boris
Verma, Rakesh
[J]. IWSPA '18: PROCEEDINGS OF THE FOURTH ACM INTERNATIONAL WORKSHOP ON SECURITY AND PRIVACY ANALYTICS, 2018, : 31 - 39
[7] ANALYSIS OF MACHINE LEARNING METHODS ON MALWARE DETECTION
Aydogan, Emre
Sen, Sevil
[J]. 2014 22ND SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2014, : 2066 - 2069
[8] Detection of Network Anomalies with Machine Learning Methods
Kara, Ihsan Riza
Varol, Asaf
[J]. 2022 10TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSICS AND SECURITY (ISDFS), 2022,
[9] Geological Fractures Detection by Methods of Machine Learning
M. V. Muratov
V. A. Biryukov
I. B. Petrov
[J]. Lobachevskii Journal of Mathematics, 2020, 41 : 533 - 537
[10] Machine Learning Methods for Automatic Gender Detection
Morales Sanchez, Damian
Moreno, Antonio
Jimenez Lopez, M. Dolores
[J]. INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2022, 31 (03)

← 1 2 3 4 5 →