Malicious Traffic Compression and Classification Technique for Secure Internet of Things

作     者：Yu-Rim Lee Na-Eun Park Seo-Yi Kim Il-Gu Lee 

作者机构：Department of Future Convergence Technology EngineeringSungshinWomen’s UniversitySeoul02844Korea Department of Convergence Security EngineeringSungshinWomen’s UniversitySeoul02844Korea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第76卷第9期

页      面：3465-3482页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by a Korea Institute for Advancement of Technology(KIAT)Grant funded by theKorean Government(MOTIE)(P0008703,The Competency Development Program for Industry Specialists) the MSIT under the ICAN(ICT Challenge and Advanced Network ofHRD)program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information Communication Technology Planning and Evaluation(IITP) 

主　　题：IoT security intrusion detection machine learning traffic classification 

摘      要：With the introduction of 5G technology,the application of Internet of Things(IoT)devices is expanding to various industrial ***,introducing a robust,lightweight,low-cost,and low-power security solution to the IoT environment is ***,this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT *** first method,compressed sensing and learning(CSL),compresses an event log in a bitmap format to quickly detect ***,the attack log is detected using a machine-learning classification *** second method,precise re-learning after CSL(Ra-CSL),comprises a two-step *** uses CSL as the 1st step analyzer,and the 2nd step analyzer is applied using the original dataset for a log that is detected as an attack in the 1st step *** the experiment,the bitmap rule was set based on the boundary value,which was 99.6%true positive on average for the attack and benign data found by analyzing the training *** results showed that the CSL was effective in reducing the training and detection time,and Ra-CSL was effective in increasing the detection *** to the experimental results,the data compression technique reduced the memory size by up to 20%and the training and detection times by 67%when compared with the conventional *** addition,the proposed technique improves the detection accuracy;the Naive Bayes model with the highest performance showed a detection rate of approximately 99%.