Research on Cyber Attack Detection Technology Based on Residual Convolutional Neural Network

doi:10.3969/j.issn.1671-1122.2025.02.005

Abstract

Abstract:

As our cyber security capabilities are gradually improving, the number and complexity of network attacks are also gradually increasing, and cyber attack detection technology are facing greater challenges. To improve the accuracy of cyber attack detection, this article proposed a cyber attack detection model HaoResNet based on residual convolutional neural network and tested the HaoResNet model on the USTC-TFC2016 dataset. First, HaoResNet model converted the pcap traffic file into a grayscale image, and then performed 2-classification, 10-classification, and 20-classification experiments on normal and malicious traffic. The experimental results demonstrate that HaoResNet achieves 100% accuracy on the 2-classification task, 99% accuracy on the normal traffic 10-classifier task, 98% accuracy on the malicious traffic 10- classification task, and 98% accuracy on the 20-classification task. Compared with existing models, HaoResNet achieves the higher detection precision on the 2- classification task.

Key words: cyber attack detection, convolutional neural network, malicious traffic, multi-classification

CLC Number:

TP309

ZHANG Shuangquan, YIN Zhonghao, ZHANG Huan, GAO Peng. Research on Cyber Attack Detection Technology Based on Residual Convolutional Neural Network[J]. Netinfo Security, 2025, 25(2): 240-248.

Figures/Tables 10

References 28

[1]	HU Xiaoya. Research on Lightweight Secure Communication and Data Sharing Technology for Internet of Vehicles[D]. Beijing: Beijing University of Posts and Telecommunications, 2024.
	胡晓雅. 面向车联网的轻量级安全通信及数据共享技术研究[D]. 北京: 北京邮电大学, 2024.
[2]	ZHAO Xin, HOU Kuo, WANG Hao, et al. Design and Implementation of an Enterprise Network Monitoring System Based on SNMP and Zabbix[J]. Computer Knowledge and Technology, 2023, 19(1): 91-94.
	赵鑫, 侯阔, 王昊, 等. 一个基于SNMP和Zabbix的企业网络监控系统的设计与实现[J]. 电脑知识与技术, 2023, 19(1):91-94.
[3]	LIU Ping, CAO Yun. Research on Network Security[J]. Journal of Xiangnan University, 2010, 31(2): 84-86.
	刘平, 曹云. 入侵检测在网络安全中的地位与作用[J]. 湘南学院学报, 2010, 31(2):84-86.
[4]	XU Jinyan. Deep Learning-Based Intrusion Detection for Class-Oriented Unbalanced Networks[D]. Nanchang: Jiangxi Normal University, 2023.
	许晋俨. 基于深度学习的面向类不均衡网络入侵检测研究[D]. 南昌: 江西师范大学, 2023.
[5]	GONG Siyue, LIU Hui, WANG Baohui. Malicious Encrypted Traffic Detection Method Based on Conversation Statistical Encoder Model[J]. Computer Science, 2024, 51(11): 340-346. doi: 10.11896/jsjkx.231000121
	巩思越, 刘辉, 王宝会. 基于会话统计编码器的恶意加密流量检测方法研究[J]. 计算机科学, 2024, 51(11):340-346. doi: 10.11896/jsjkx.231000121
[6]	SUN Zhenhao, ZHANG Diansheng, DONG Leilei, et al. Discussion on Foreign Body Detection around Airport Based on Convolutional Neural Network[J]. Information Recording Materials, 2024, 25(2): 217-219.
	孙振浩, 张殿生, 董嫘嫘, 等. 基于卷积神经网络的机场周界异物检测论述[J]. 信息记录材料, 2024, 25(2):217-219.
[7]	LU Da, FENG Zhonghua. Research on Network Intrusion Detection Based on Convolutional Neural Network[J]. Communications Technology, 2024, 57(5): 529-536.
	卢达, 冯中华. 基于卷积神经网络的网络入侵检测技术研究[J]. 通信技术, 2024, 57(5):529-536.
[8]	LIU Dongqi, ZHANG Qiong, LIANG Haolan, et al. Study on Smart Grid AMI Intrusion Detection Method Based on Federated Learning[J]. Computer Science, 2024, 51(S1): 944-951.
	刘东奇, 张琼, 梁皓澜, 等. 基于联邦学习的智能电网AMI入侵检测方法研究[J]. 计算机科学, 2024, 51(S1):944-951.
[9]	WANG Xiangyue, ZHAO Lihui. Network Intrusion Detection Model Based on Multi-Stage Feature Selection and CNN-GRU[J]. Journal of North University of China (Natural Science Edition), 2024, 45(1): 66-73.
	王相月, 赵利辉. 基于多阶段特征选择和CNN-GRU的网络入侵检测模型[J]. 中北大学学报(自然科学版), 2024, 45(1):66-73.
[10]	CHENG Bailiang, ZHOU Hongbo, ZHONG Linhui. Intrusion Detection System Based on Anomaly and Misuse[J]. Computer Engineering and Design, 2007, 28(14): 3341-3343.
	程柏良, 周洪波, 钟林辉. 基于异常与误用的入侵检测系统[J]. 计算机工程与设计, 2007, 28(14):3341-3343.
[11]	SHANG Yuling, LI Peng, ZHU Feng, et al. Overview of IoT Traffic Attack Detection Technology Based on Fuzzy Logic[J]. Computer Science, 2024, 51(3): 3-13. doi: 10.11896/jsjkx.230700130
	商钰玲, 李鹏, 朱枫, 等. 基于模糊逻辑的物联网流量攻击检测技术综述[J]. 计算机科学, 2024, 51(3):3-13. doi: 10.11896/jsjkx.230700130
[12]	JIAN Shijie, LU Zhigang, DU Dan, et al. Overview of Network Intrusion Detection Technology[J]. Journal of Cyber Security, 2020, 5(4): 96-122.
[13]	VAN G M, BOHTE S. Editorial: Artificial Neural Networks as Models of Neural Information Processing[EB/OL]. (2017-12-19)[2024-11-20]. https://doi.org/10.3389/fncom.2017.00114.
[14]	JIA Hanshuang, ZHANG Ka, YANG Suiming. Design of Abnormal Intrusion Detection System for Train Communication Network Based on Bidirectional AC Algorithm[J]. Computer Measurement & Control, 2024, 32(8): 14-19.
	贾寒霜, 张卡, 杨碎明. 基于双向AC算法的列车通信网络异常入侵检测系统设计[J]. 计算机测量与控制, 2024, 32(8):14-19.
[15]	YANG Yongping, WANG Siting. Research on a Network Traffic Classification Algorithm Based on GRU and CNN[J]. Information & Computer, 2024, 36(6): 25-28.
	杨永平, 王思婷. 一种GRU结合CNN的网络流量分类算法研究[J]. 信息与电脑, 2024, 36(6):25-28.
[16]	LIU Yufei. ConvLSTM-CNN Model Based on Mixed Balanced Sampling and Its Application in Network Security Situation Awareness[D]. Shanghai: East China Normal University, 2022.
	刘雨菲. 一种基于混合平衡采样的ConvLSTM-CNN模型及其在网络安全态势感知中的应用研究[D]. 上海: 华东师范大学, 2022.
[17]	YANG Dongyang. Research on High-Speed Network Traffic Measurement Based on Deep Learning[D]. Suzhou: Soochow University, 2023.
	杨东阳. 基于深度学习的高速网络流量测量研究[D]. 苏州: 苏州大学, 2023.
[18]	WANG Bo, SU Yang, ZHANG Mingshu, et al. A Deep Hierarchical Network for Packet-Level Malicious Traffic Detection[J]. IEEE Access, 2020, 8: 201728-201740.
[19]	SUN Fengxu, LI Aihua, SHENG Xia. Research on Encrypted Traffic Detection Technology Based on Fusion Attention Mechanism[C]// IEEE. 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC). New York: IEEE, 2024: 158-161.
[20]	WANG Yifeng, GUO Yuanbo, CHEN Qingli, et al. Method Based on Contrastive Learning for Fine-Grained Unknown Malicious Traffic Classification[J]. Journal on Communications, 2022, 43(10): 12-25. doi: 10.11959/j.issn.1000-436x.2022180
	王一丰, 郭渊博, 陈庆礼, 等. 基于对比学习的细粒度未知恶意流量分类方法[J]. 通信学报, 2022, 43(10):12-25. doi: 10.11959/j.issn.1000-436x.2022180
[21]	GU Yue, LI Dan, GAO Kaihui. Research on Network Traffic Classification Based on Machine Learning and Deep Learning[J]. Telecommunications Science, 2021, 37(3): 105-113. doi: 10.11959/j.issn.1000-0801.2021052
	顾玥, 李丹, 高凯辉. 基于机器学习和深度学习的网络流量分类研究[J]. 电信科学, 2021, 37(3):105-113. doi: 10.11959/j.issn.1000-0801.2021052
[22]	YAN Jianwei, ZHAO Yuan, ZHANG Lewei, et al. Recognition of Rosa Roxbunghii in Natural Environment Based on Improved Faster RCNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(18): 143-150.
	闫建伟, 赵源, 张乐伟, 等. 改进Faster-RCNN自然环境下识别刺梨果实[J]. 农业工程学报, 2019, 35(18):143-150.
[23]	DONG Shuqin, ZHANG Bin. Network Traffic Anomaly Detection Method Based on Deep Features Learning[J]. Journal of Electronics & Information Technology, 2020, 42(3): 695-703.
	董书琴, 张斌. 基于深度特征学习的网络流量异常检测方法[J]. 电子与信息学报, 2020, 42(3):695-703.
[24]	HWANG R, PENG M, NGUYEN V L, et al. An LSTM-Based Deep Learning Approach for Classifying Malicious Traffic at the Packet Level[J]. Applied Sciences, 2019, 9(16): 3414-3427.
[25]	HUANG Hong, ZHANG Xingxing, LU Ye, et al. BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features[J]. Computers, Materials & Continua, 2024, 78(3): 3929-3951.
[26]	WANG Wei, ZHU Ming, ZENG Xuewen, et al. Malware Traffic Classification Using Convolutional Neural Network for Representation Learning[C]// IEEE. 2017 International Conference on Information Networking (ICOIN). New York: IEEE, 2017: 712-717.
[27]	GARG S, KAUR K, KUMAR N, et al. Hybrid Deep-Learning-Based Anomaly Detection Scheme for Suspicious Flow Detection in SDN: A Social Multimedia Perspective[J]. IEEE Transactions on Multimedia, 2019, 21(3): 566-578.
[28]	GUO Shuai, LIU Yao, SU Yang. Network Traffic Anomaly Detection Method Based on CAE and LSTM[EB/OL]. (2021-09-01)[2024-11-20]. https://doi.org/10.1088/1742-6596%2F2025%2F1%2F012025.

名称	CTU编号	处理方式
Cridex	108-1	原文件
Geodo	119-2	截取
Htbot	110-1	原文件
Miuref	127-1	原文件
Neris	42,43	合并
Nsis-ay	53	原文件
Shifu	142-1	截取
Tinba	150-1	截取
Virut	54	原文件
Zeus	116-2	原文件

名称	种类	大小/MB
BitTorrent	P2P	7.33
Facetime	多媒体流	2.40
FTP	数据传输	60.20
Gmail	电子邮件	9.05
MySQL	数据库	22.30
Outlook	电子邮件	11.10
Skype	即时通信	4.22
SMB	数据传输	1206
Weibo	社交网络	1618
WorldOfWarcraft	电子游戏	14.90

名称	所有数据个数/个	测试集/训练集	验证集	名称	所有数据个数/个	测试集/训练集	验证集
Facetime	5000	4500	500	Tinba	5000	4500	500
Skype	5000	4500	500	Zeus	5000	4500	500
BitTorrent	5000	4500	500	Shifu	5000	4500	500
Gmail	5000	4500	500	Neris	5000	4500	500
Outlook	5000	4500	500	Cridex	5000	4500	500
WorldOf Warcraft	5000	4500	500	Nsis-ay	5000	4500	500
MySQL	5000	4500	500	Geodo	5000	4500	500
FTP	5000	4500	500	Miuref	5000	4500	500
SMB	5000	4500	500	Virut	5000	4500	500
Weibo	5000	4500	500	Htbot	5000	4500	500
总计	50000	45000	5000	总计	50000	45000	5000

	预测为正类别	预测为负类别
实际为正类别	TP	FN
实际为负类别	FP	TN

平均值	二分类器	十分类器		二十分类器
平均值	二分类器	正常流量	恶意流量	二十分类器
99%	100%	99%	98%	98%