Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (6): 1742-1746.DOI: 10.11772/j.issn.1001-9081.2018102096

• Cyber security • Previous Articles     Next Articles

Intrusion detection approach for IoT based on practical Byzantine fault tolerance

PAN Jianguo, LI Hao   

  1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China
  • Received:2018-10-16 Revised:2018-12-13 Online:2019-06-17 Published:2019-06-10
  • Supported by:
    This work is partially supported by the Shanghai Natural Science Foundation (18ZR1428300).


潘建国, 李豪   

  1. 上海师范大学 信息与机电工程学院, 上海 200234
  • 通讯作者: 李豪
  • 作者简介:潘建国(1977-),男,江西上饶人,副教授,博士,主要研究方向:智能信息处理、嵌入式开发;李豪(1994-),男,山东泗水人,硕士研究生,主要研究方向:智能信息处理、嵌入式开发。
  • 基金资助:

Abstract: Current Internet of Things (IoT) networks have high detection rate of known types of attacks but the network node energy consumption is high. Aiming at this fact, an intrusion detection approach based on Practical Byzantine Fault Tolerance (PBFT) algorithm was proposed. Firstly, Support Vector Machine (SVM) was used for pre-training to obtain the intrusion detection decision rule, and the trained rule was applied to each node in IoT. Then, some nodes were voted to perform the active intrusion detection on other nodes in the network, while announce their detection results to other nodes. Finally, each node judged the state of other nodes according to PBFT algorithm, making the detection results reach consistency in the system. The simulation results on NSL-KDD dataset by TinyOS show that the proposed approach reduces the energy consumption by 12.2% and 7.6% averagely and respectively compared with Integrated Intrusion Detection System (ⅡDS) and Two-layer Dimension reduction and Two-tier Classification (TDTC) approach, effectively reducing the energy consumption of IoT.

Key words: Internet of Things (IoT), Practical Byzantine Fault Tolerance (PBFT), intrusion detection, low energy consumption, Support Vector Machine (SVM)

摘要: 物联网入侵的检测率虽高,但面临节点能力消耗过大的问题,为此提出一种基于共识的实用拜占庭容错(PBFT)算法的入侵检测方法。首先,使用支持向量机(SVM)进行预训练得到入侵检测判定规则,并将训练规则应用于物联网中的每个节点;然后,选举出部分节点对网络中其他节点进行主动入侵检测,同时将自身的检测结果向其他节点公布;最后,每个节点依据PBFT算法判断其他节点的状态,使检测结果在系统内达到一致性。在NSL-KDD数据集上使用TinyOS进行仿真的实验结果表明,所提方法与集成入侵检测系统(ⅡDS)和双重降维双重检测(TDTC)方法相比,能量消耗平均降低12.2%和7.6%,能够有效地降低物联网的能量消耗。

关键词: 物联网, 实用拜占庭容错, 入侵检测, 低能耗, 支持向量机

CLC Number: