An Overview of Cyber Threat Intelligence Platform and Role of Artificial Intelligence and Machine Learning

被引:6
作者
Dutta, Abir [1 ]
Kant, Shri [1 ]
机构
[1] Sharda Univ, Res & Technol Dev Ctr, Noida, Up, India
来源
INFORMATION SYSTEMS SECURITY, ICISS 2020 | 2020年 / 12553卷
关键词
Cyber threat intelligence; Artificial intelligence; Machine learning; Cyber security; Threat;
D O I
10.1007/978-3-030-65610-2_5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ever enhancing computational capability of digital system along with upgraded tactics, technology and procedure (TTPs) enforced by the cybercriminals, does not match to the conventional security mechanism for detection of intrusion and prevention of threat in current cyber security landscape. Integration of artificial intelligence, machine learning and cyber threat intelligence platform with the signature-based threat detection models like intrusion detection system (IDS), SNORT, security information and event management (SIEM) which are being primarily implemented in the network for continuous analysis of the indicator of compromise (IoC) becomes inevitable, for prompt identification of true events and subsequent mitigation of the threat. In this paper, author illustrated the approach to integrate artificial intelligence and machine learning with the cyber threat intelligence for the collection of actionable threat intelligence from various sources like dark web, hacker's forum, hacker's assets, honeypot, etc. Furthermore, the application of threat intelligence in the aspect of cyber security has been discussed in this paper. Finally, a model has been proposed for generating actionable threat intelligence implementing a supervised machine learning approach employing Naive Bayes classifier.
引用
收藏
页码:81 / 86
页数:6
相关论文
共 7 条
[1]   A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection [J].
Buczak, Anna L. ;
Guven, Erhan .
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2016, 18 (02) :1153-1176
[2]   A Supervised Machine Learning Based Approach for Automatically Extracting High-Level Threat Intelligence from Unstructured Sources [J].
Ghazi, Yumna ;
Anwar, Zahid ;
Mumtaz, Rafia ;
Saleem, Shahzad ;
Tahir, Ali .
2018 INTERNATIONAL CONFERENCE ON FRONTIERS OF INFORMATION TECHNOLOGY (FIT 2018), 2018, :129-134
[3]   Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles [J].
Lee, Jonghoon ;
Kim, Jonghyun ;
Kim, Ikkyun ;
Han, Kijun .
IEEE ACCESS, 2019, 7 :165607-165626
[4]   Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey [J].
Liu, Hongyu ;
Lang, Bo .
APPLIED SCIENCES-BASEL, 2019, 9 (20)
[5]   An Integrated Approach to Network Intrusion Detection and Prevention [J].
Prakash, B. Bhanu ;
Yeswanth, Kaki ;
Srinivas, M. Sai ;
Balaji, S. ;
Sekhar, Y. Chandra ;
Nair, Aswathy K. .
INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES, ICICCT 2019, 2020, 89 :43-51
[6]  
Raad Abbas A., 2018, Lecture Notes, V1989, P1307
[7]  
Ussath M., 2016, SPRING C INF TECHN N, P213