CONDITIONAL RANDOM FIELDS BASED REAL-TIME INTRUSION DETECTION FRAMEWORK

被引：0

作者：

Gu, Jiaojiao ^{[1
]}

Jiang, Wenzhi ^{[1
]}

Hu, Wenxuan ^{[1
]}

Zhang, Xiaoyu ^{[1
]}

机构：

[1] Naval Aeronaut & Astronaut Univ, Yantai, Peoples R China

来源：

3RD INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND COMPUTER SCIENCE (ITCS 2011), PROCEEDINGS | 2011年

关键词：

intrusion detection; anomaly; CRFs; Machine Learning; layered framework;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Intrusion detection systems are now an essential component in the all kinds of network even including wireless ad hoc network. With the rapid advancement in the network technologies the focus of intrusion detection has shifted from simple signature matching approaches to detecting attacks based on analyzing contextual information that employed in anomaly and hybrid intrusion detection approaches. This paper proposed a layered anomaly intrusion detection framework using Conditional Random Fields to detect a wide variety of attacks. With this framework attacks can be identified and intrusion response can be initiated in real time. Experiments show that the CRF model can detect attacks effectively.

引用

页码：186 / 189

页数：4

共 50 条

[21] Real-Time Packet-Based Intrusion Detection on Edge Devices
Borgioli, Niccolo
Phan, Linh Thi Xuan
Aromolo, Federico
Biondi, Alessandro
Buttazzo, Giorgio C.
2023 CYBER-PHYSICAL SYSTEMS AND INTERNET-OF-THINGS WEEK, CPS-IOT WEEK WORKSHOPS, 2023, : 234 - 240
[22] RAPID: Real-time Anomaly-based Preventive Intrusion Detection
Doshi, Keval
Mozaffari, Mahsa
Yilmaz, Yasin
PROCEEDINGS OF THE 2019 ACM WORKSHOP ON WIRELESS SECURITY AND MACHINE LEARNING (WISEML '19), 2019, : 49 - 54
[23] A Real-time CSI-based Passive Intrusion Detection Method
Wang, Xingang
Wang, Yufei
Wang, Dong
2020 IEEE INTL SYMP ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, INTL CONF ON BIG DATA & CLOUD COMPUTING, INTL SYMP SOCIAL COMPUTING & NETWORKING, INTL CONF ON SUSTAINABLE COMPUTING & COMMUNICATIONS (ISPA/BDCLOUD/SOCIALCOM/SUSTAINCOM 2020), 2020, : 1091 - 1098
[24] Deep Neural Network Based Real-Time Intrusion Detection System
Sharuka Promodya Thirimanne
Lasitha Jayawardana
Lasith Yasakethu
Pushpika Liyanaarachchi
Chaminda Hewage
SN Computer Science, 2022, 3 (2)
[25] Research of DoS intrusion real-time detection based on danger theory
Xu Chun
Chen Xing-shu
Zhao Hui
Jiang Yu-ming
Liu Nian
Wang Tie-fang
PROCEEDINGS OF THE FIRST INTERNATIONAL SYMPOSIUM ON DATA, PRIVACY, AND E-COMMERCE, 2007, : 209 - 211
[26] RBF-based real-time hierarchical intrusion detection systems
Jiang, J
Zhang, CL
Kamel, M
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1512 - 1516
[27] Research on Real-time Intrusion Detection Technology Based on Data Mining
Fan Zhiqin
Wang Mian
PROCEEDINGS OF THE 2018 3RD INTERNATIONAL WORKSHOP ON MATERIALS ENGINEERING AND COMPUTER SCIENCES (IWMECS 2018), 2018, 78 : 171 - 175
[28] Real-time Distributed-Random-Forest-Based Network Intrusion Detection System Using Apache Spark
Zhang, Hao
Dai, Shumin
Li, Yongdan
Zhang, Wenjun
2018 IEEE 37TH INTERNATIONAL PERFORMANCE COMPUTING AND COMMUNICATIONS CONFERENCE (IPCCC), 2018,
[29] Real-Time Network Intrusion Detection System Based on Deep Learning
Dong, Yuansheng
Wang, Rong
He, Juan
PROCEEDINGS OF 2019 IEEE 10TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2019), 2019, : 1 - 4
[30] Real-time intrusion detection system base on random subspace PCA-SVM ensemble
Zhang, Hongmei
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2009, 30 (12): : 2680 - 2684

← 1 2 3 4 5 →