Network attack prediction method based on threat intelligence for IoT

被引:0
|
作者
Hongbin Zhang
Yuzi Yi
Junshe Wang
Ning Cao
Qiang Duan
机构
[1] Hebei University of Science and Technology,School of Information Science and Engineering
[2] Hebei Normal University,Hebei Key Laboratory of Network and Information Security
[3] Qingdao Binhai University,College of Information Engineering
[4] Pennsylvania State University,Department of Information Science & Technology
来源
关键词
Social internet of things; Internet of things; Support vector machine; Threat intelligence; Social networks; Malicious behavior;
D O I
暂无
中图分类号
学科分类号
摘要
The Social Internet of Things (SIoT) is a combination of the Internet of Things (IoT) and social networks, which enables better service discovery and improves the user experience. The threat posed by the malicious behavior of social network accounts also affects the SIoT, this paper studies the analysis and prediction of malicious behavior for SIoT accounts, proposed a method for predicting malicious behavior of SIoT accounts based on threat intelligence. The method uses support vector machine (SVM) to obtain threat intelligence related to malicious behavior of target accounts, analyze contextual data in threat intelligence to predict the behavior of malicious accounts. By collecting and analyzing the data in a SIoT environment, verifies the malicious behavior prediction method of SIoT account proposed in this paper.
引用
收藏
页码:30257 / 30270
页数:13
相关论文
共 50 条
  • [31] Network security situational awareness model based on threat intelligence
    Zhang H.
    Yin Y.
    Zhao D.
    Liu B.
    1600, Editorial Board of Journal on Communications (42): : 182 - 194
  • [32] Network Security Situation Awareness Framework based on Threat Intelligence
    Zhang, Hongbin
    Yi, Yuzi
    Wang, Junshe
    Cao, Ning
    Duan, Qiang
    CMC-COMPUTERS MATERIALS & CONTINUA, 2018, 56 (03): : 381 - 399
  • [33] Cyber Threat Intelligence for IoT Using Machine Learning
    Mishra, Shailendra
    Albarakati, Aiman
    Sharma, Sunil Kumar
    PROCESSES, 2022, 10 (12)
  • [34] Mapping Cyber Threat Intelligence to Probabilistic Attack Graphs
    Gylling, Andreas
    Ekstedt, Mathias
    Afzal, Zeeshan
    Eliasson, Per
    PROCEEDINGS OF THE 2021 IEEE INTERNATIONAL CONFERENCE ON CYBER SECURITY AND RESILIENCE (IEEE CSR), 2021, : 304 - 311
  • [35] Identifying the attack surface for IoT network
    Rizvi, Syed
    Orr, R. J.
    Cox, Austin
    Ashokkumar, Prithvee
    Rizvi, Mohammad R.
    INTERNET OF THINGS, 2020, 9
  • [36] IoT Network Attack Detection and Mitigation
    Gelenbe, Erol
    Froehlich, Piotr
    Nowak, Mateusz
    Papadopoulos, Stavros
    Protogerou, Aikaterini
    Drosou, Anastasios
    Tzovaras, Dimitrios
    2020 9TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2020, : 123 - 128
  • [37] Secure IoT edge: Threat situation awareness based on network traffic
    Zhao, Yuyu
    Cheng, Guang
    Duan, Yu
    Gu, Zhouchao
    Zhou, Yuyang
    Tang, Lu
    COMPUTER NETWORKS, 2021, 201 (201)
  • [38] HSDL-based intelligent threat detection framework for IoT network
    Santhadevi, D.
    Janet, B.
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2023, 45 (01) : 1775 - 1790
  • [39] Insider Threat Risk Prediction based on Bayesian Network
    Elmrabit, Nebrase
    Yang, Shuang-Hua
    Yang, Lili
    Zhou, Huiyu
    COMPUTERS & SECURITY, 2020, 96
  • [40] Research on Threat Information Network Based on Link Prediction
    Du, Jin
    Yuan, Feng
    Ding, Liping
    Chen, Guangxuan
    Liu, Xuehua
    INTERNATIONAL JOURNAL OF DIGITAL CRIME AND FORENSICS, 2021, 13 (02) : 94 - 102