Fail-Safe Mechanism Using Entropy Based Misbehavior Classification and Detection in Vehicular Ad Hoc Networks

被引：7

作者：

Sharshembiev, Kumar ^{[1
]}

Yoo, Seong-Moo ^{[1
]}

Elmahdi, Elbasher ^{[1
]}

Kim, Yong-Kab ^{[2
]}

Jeong, Geun-Ho ^{[2
]}

机构：

[1] Univ Alabama, Elect & Comp Engn, Huntsville, AL 35899 USA

[2] Wonkwang Univ, Elect Informat Commun Engn, Iksan, South Korea

来源：

2019 INTERNATIONAL CONFERENCE ON INTERNET OF THINGS (ITHINGS) AND IEEE GREEN COMPUTING AND COMMUNICATIONS (GREENCOM) AND IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING (CPSCOM) AND IEEE SMART DATA (SMARTDATA) | 2019年

关键词：

Vehicular ad hoc networks; misbehaving node; flow sampling; entropy; IEEE; 802.11p; probabilistic routing protocols; ANOMALY DETECTION;

D O I：

10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00042

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A novel approach is proposed to perform real-time detection of node misbehavior using selective flow sampling and entropy change. Nodes in vehicular ad hoc networks (VANETs) use broadcast protocols to efficiently disseminate safety information, but nodes do not always behave according to the routing protocols. Misbehavior can be caused by a targeted attack, where an attacking vehicle can intentionally send or route malicious packets to harm. Due to the dynamic nature of nodes in VANET and routing complexity, unintentional misbehavior can also happen due to hardware or software failures in the vehicle. We are not concerned with the targeted attacks, but rather explore how the unintentional misbehavior, which can cause statistical multi-hop routing protocols to operate as a basic flooding protocol, can be detected and accurately classified in real-time. This classification and detection of misbehaving node will help our fail-safe algorithm make the accurate decision. These methods and detection techniques are based on the IEEE 802.11p MAC protocol and weighted p-persistence multi-hop routing protocol. Evaluations were performed using the VEINS simulator using the p-persistence routing protocol in a US city area.

引用

页码：123 / 128

页数：6

共 13 条

[1]

Abadi Trlartin, 2016, P 12 USENIX S SYST O

[2] Improving network anomaly detection via selective flow-based sampling [J].

Androulidakis, G. ;

Papavassiliou, S. .

IET COMMUNICATIONS, 2008, 2 (03) :399-409

[3] Network Anomaly Detection and Classification via Opportunistic Sampling [J].

Androulidakis, Georgios ;

Chatzigiannakis, Vassilis ;

Papavassiliou, Symeon .

IEEE NETWORK, 2009, 23 (01) :6-12

[4] Estimating flow distributions from sampled flow statistics [J].

Duffield, N ;

Lund, C ;

Thorup, M .

IEEE-ACM TRANSACTIONS ON NETWORKING, 2005, 13 (05) :933-946

[5]

Eckhoff David, 2012, P 5 ACM ICST INT C S

[6]

Grosvor Jyoti, 2011, C ADV COMP COMM

[7]

Holm N., 2003, P 3 ACM SIGCOMM C IN

[8]

Ilan Chan-Kyu, 2009, IEEE NETWORK, V235

[9]

Melri Mohamed Nidhal, 2014, P 17 ACM INT C MOD A

[10]

Ranjan S, 2007, INFOCOM 2007 26 IEEE

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