A New Realistic Benchmark for Advanced Persistent Threats in Network Traffic

被引：5

作者：

Liu, Jinxin ^{[1
]}

Shen, Yu ^{[1
]}

Simsek, Murat ^{[1
]}

Kantarci, Burak ^{[1
]}

Mouftah, Hussein T. ^{[1
]}

Bagheri, Mehran ^{[2
]}

Djukic, Petar ^{[2
]}

机构：

[1] University of Ottawa, School of Electrical Engineering and Computer Science, Ottawa,ON,K1N 6N5, Canada

[2] Ciena Corporation, Ai and Analytics Department, Ottawa,ON,K2K 3K1, Canada

来源：

IEEE Networking Letters | 2022年 / 4卷 / 03期

关键词：

Advanced persistent threat - Attack centric method - Classification-tree analysis - Features extraction - Intrusion Detection Systems - Machine-learning - Networks security - Predictive models - Random forests - Reconnaissance;

D O I：

10.1109/LNET.2022.3185553

中图分类号：

学科分类号：

摘要：

In order to define a benchmark for Machine Learning (ML)-based Advanced Persistent Threat (APT) detection in the network traffic, this letter presents SCVIC-APT-2021, a new dataset that can realistically represent the contemporary network architecture and APT characteristics. Following upon this, an ML-based Attack Centric Method (ACM) is introduced to evaluate the APT detection performance on the generated dataset. Furthermore, ACM has been shown to outperform the baseline approaches with a maximum macro average F1 score of 82.27% corresponding to 9.4% improvement with respect to the baseline performance. © 2019 IEEE.

引用

页码：162 / 166

共 50 条

[41] A Survey of Network Traffic Visualization in Detecting Network Security Threats
Liu, Xiaomei
Sun, Yong
Fang, Liang
Liu, Junpeng
Yu, Lingjing
TRUSTWORTHY COMPUTING AND SERVICES (ISCTCS 2014), 2015, 520 : 91 - 98
[42] Multiobjective Network Design for Realistic Traffic Models
Banerjee, Nilanjan
Kumar, Rajeev
GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2, 2007, : 1904 - +
[43] Generating realistic network traffic for security experiments
Luo, S
Marin, GA
PROCEEDINGS OF THE IEEE SOUTHEASTCON 2004: ENGINEERING CONNECTS, 2004, : 200 - 207
[44] Swing: Realistic and Responsive Network Traffic Generation
Vishwanath, Kashi Venkatesh
Vahdat, Amin
IEEE-ACM TRANSACTIONS ON NETWORKING, 2009, 17 (03) : 712 - 725
[45] Defense Against Advanced Persistent Threats: Optimal Network Security Hardening Using Multi-stage Maze Network Game
Zhang, Hangsheng
Liu, Haitao
Liang, Jie
Li, Ting
Geng, Liru
Liu, Yinlong
Chen, Shujuan
2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2020, : 724 - 729
[46] Surviving advanced persistent threats in a distributed environment - Architecture and analysis
Mehresh, Ruchika
Upadhyaya, Shambhu
INFORMATION SYSTEMS FRONTIERS, 2015, 17 (05) : 987 - 995
[47] Learning Games for Defending Advanced Persistent Threats in Cyber Systems
Zhu, Tianqing
Ye, Dayong
Cheng, Zishuo
Zhou, Wanlei
Yu, Philip S.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (04): : 2410 - 2422
[48] A Context-Based Detection Framework for Advanced Persistent Threats
Giura, Paul
Wang, Wei
2012 ASE INTERNATIONAL CONFERENCE ON CYBER SECURITY (CYBERSECURITY), 2012, : 69 - 74
[49] Dealing with Advanced Persistent Threats in Smart Grid ICT Networks
Skopik, Florian
Friedberg, Ivo
Fiedler, Roman
2014 IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE (ISGT), 2014,
[50] An Advanced Persistent Threats Awareness Technology Based on "Condensed matter"
Wang, Yutong
Chang, Chaowen
Ma, Zengbang
PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON MECHATRONICS, MATERIALS, CHEMISTRY AND COMPUTER ENGINEERING 2015 (ICMMCCE 2015), 2015, 39 : 1614 - 1619

← 1 2 3 4 5 →