Network Intrusion Detection in Encrypted Traffic

被引:2
|
作者
Papadogiannaki, Eva [1 ]
Tsirantonakis, Giorgos [1 ]
Ioannidis, Sotiris [2 ]
机构
[1] FORTH ICS, Iraklion, Greece
[2] Tech Univ Crete, Khania, Greece
关键词
IDENTIFICATION;
D O I
10.1109/DSC54232.2022.9888942
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Traditional signature-based intrusion detection systems inspect packet headers and payloads to report any malicious or abnormal traffic behavior that is observed in the network. With the advent and rapid adoption of network encryption mechanisms, typical deep packet inspection systems that focus only on the processing of network packet payload contents are gradually becoming obsolete. Advancing intrusion detection tools to be also effective in encrypted networks is crucial. In this work, we propose a signature language indicating packet sequences. Signatures detect events of possible intrusions and malicious actions in encrypted networks using packet metadata. We demonstrate the effectiveness of this methodology using different tools for penetrating vulnerable web servers and a public dataset with traffic that originates from IoT malware. We implement the signature language and we integrate it into an intrusion detection system. Using our proposed methodology, the generated signatures can effectively and efficiently report intrusion attempts.
引用
收藏
页数:8
相关论文
共 50 条
  • [41] Intrusion Detection in IoT Network Traffic Using Markov Model
    Liu, I-Hsien
    Huang, Hsiao-Ching
    Lee, Meng-Huan
    Li, Jung-Shian
    SENSORS AND MATERIALS, 2024, 36 (03) : 1127 - 1134
  • [42] Evolutive modeling of TCP/IP network traffic for intrusion detection
    Neri, F
    REAL-WORLD APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS, 2000, 1803 : 214 - 223
  • [43] Protocol identification of encrypted network traffic
    Gebski, Matthew
    Penev, Alex
    Wong, Raymond K.
    2006 IEEE/WIC/ACM International Conference on Web Intelligence, (WI 2006 Main Conference Proceedings), 2006, : 957 - 960
  • [44] Network Traffic Examination for Network Intrusion Detection in IOV using Autoencoder and Decoder
    Vaishnodevi, S.
    Kumar, Vinod D.
    Murali, G.
    Azhagiri, M.
    Madhuvappan, Arunkumar C.
    Sathishkumar, K.
    2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024, 2024, : 13 - 18
  • [45] An Intrusion Detection System Based on Convolutional Neural Network for Imbalanced Network Traffic
    Zhang, Xiaoxuan
    Ran, Jing
    Mi, Jize
    PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019), 2019, : 456 - 460
  • [46] A Neural Network based NIDS framework for intrusion detection in contemporary network traffic
    Subba, Basant
    13TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED NETWORKS AND TELECOMMUNICATION SYSTEMS (IEEE ANTS), 2019,
  • [47] The use of Entropy in Lossy Network Traffic Compression for Network Intrusion Detection Applications
    Smith, Sidney
    Neyens, Stephen
    Hammell, Robert, II
    PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON CYBER WARFARE AND SECURITY (ICCWS 2017), 2017, : 352 - 360
  • [48] Detection and utilization of new-type encrypted network traffic in distributed scenarios
    Zhang, Ping
    Chen, Feng
    Yue, Hongyuan
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2024, 127
  • [49] Encrypted Malware Traffic Detection via Graph-based Network Analysis
    Fu, Zhuoqun
    Liu, Mingxuan
    Qin, Yue
    Zhang, Jia
    Zou, Yuan
    Yin, Qilei
    Li, Qi
    Duan, Haixin
    PROCEEDINGS OF 25TH INTERNATIONAL SYMPOSIUM ON RESEARCH IN ATTACKS, INTRUSIONS AND DEFENSES, RAID 2022, 2022, : 495 - 509
  • [50] BlindIDS: Market-Compliant and Privacy-Friendly Intrusion Detection System over Encrypted Traffic
    Canard, Sebastien
    Diop, Aida
    Kheir, Nizar
    Paindavoine, Marie
    Sabt, Mohamed
    PROCEEDINGS OF THE 2017 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ASIA CCS'17), 2017, : 561 - 574