Detection of Multiple Malicious Nodes Using Entropy for Mitigating the Effect of Denial of Service Attack in VANETs

Author(s):  
Sushil Kumar ◽  
Kulwinder Singh Mann

Internet of Things is a distributed collection of smart devices, where the smart device communicates with each other using Device to Device (D2D communication. Due to the resource constraint nature of IOT, the lightweight communication protocol is needed. Message Querying Telemetry Transport (MQTT) is one of the lightweight communication protocol which employs publish and subscribe method. The most of existing MQTT protocols are vulnerable to Denial of Service attack. In order to overcome the issues of the existing system, in this work a novel lightweight protocol by name EES-MQTT (Energy Efficient and Secured MQTT) is proposed which can be able to provide efficient authentication during data transmission by identifying the intruders and removing the malicious nodes. Moreover, the proposed protocol can be able to provide security with better energy optimization. The feasibility of EES-MQTT is carried out using MQTT.fx simulation tool and the Eclipse Paho. The results from the simulation proves that the EES-MQTT reduces impact of malicious nodes and optimizes the energy consumption during the data transmission.


2019 ◽  
Vol 8 (4) ◽  
pp. 3002-3007

The internet of things is the decentralized type of network in which sensor devices can join or leave the network when they want. Due to such nature of the network malicious nodes enter the network which affects network performance in terms of certain parameters. This research work is based on the detection and isolation of distributed denial of service attack in internet of things. The distributed denial of service attack is the denial of service type attack which affects network performance to large extent. In the existing techniques there are two main drawbacks. The first drawback is that the technique does not pin point malicious nodes from the network. The second drawback is that the malicious node detection time is very high. In this research, the new technique will be proposed for the isolation of malicious nodes from the network. In this technique, similarity of the traffic is analyzed using the cosine similarity. The sensor node which is generated dissimilar type of traffic is detected as malicious nodes. The proposed technique has been implemented in MATLAB and results have been analyzed in terms of certain parameters. It is expected that proposed technique detect malicious nodes in least amount of time.


Security is the main concern for IOT devices as are expected to share a lot of crucial information about the user and his surroundings. The traditional security mechanisms are ineffective against sophisticated and advanced security attacks such as Man in the Middle Attack, Denial of Service attack, Identity cloning. Different solutions have been proposed for user authentication. Device authentication is crucial in IOT environment and cannot be neglected. Despite this device authentication has not gained equal attention from the research community. The aim of this research is to develop a lightweight and robust device authentication algorithm by Artificial Immune System to ensure data integrity in IoT networks. The concepts of Artificial Immune system are utilized for generating a non-redundant device signature which is used to differentiate between authentic and malicious nodes. The device signature is generated dynamically and is non reusable. This property makes the proposed algorithm secure against numerous high-level attacks such as frequency analysis attacks, Man in the Middle attack, side channel attacks, Denial of Service attack. The developed algorithm is tested in real time and prevents malicious nodes from entering the network. In addition to being immune against the high level attacks the proposed algorithm functions with low communication cost. The proposed algorithm can be used for providing security in IOT devices with limited battery life and processing power such as IOT enabled and remotely deployed Wireless Sensor Networks for forest fire detection, power plant monitoring , remote military applications and many others.


2005 ◽  
Vol 9 (4) ◽  
pp. 363-365 ◽  
Author(s):  
A. Shevtekar ◽  
K. Anantharam ◽  
N. Ansari

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