A Neoteric Technique for Blackhole Discernment and Seclusion in Iot Networks Employing Big Data

Abstract

The IoT devices are responsible for generating a wealth of information classified as Big data and this is the prime concern of an organization. DOS attack severely hampers the productivity and security of a network of IoT devices and hence puts the big data at risk. The prime DOS attacks include Wormhole and Blackhole attacks and although there exists certain techniques for the detection of the same, yet efficient detection of these attacks can lead to an improved and more secure IoT network which results in increased network throughput. The proposed technique consists of a modified routing table to detect and overcome these attacks along with use of data analytics to identify malicious and malignant nodes and isolating them to prevent further damage to the network and to stop them from adversely impacting the network throughput. COOJA simulator shall be used of implementation of the newly formed technique. The proposed DOS attack detection algorithm will be implemented in the hypothetical network and the volume of big data generated at specified alternating intervals of time will be broadcast and deviation from the same will be a good indicator of malignant nodes which will be simulated as participants in blackhole attacks in the said network to analyze and verify the critical findings of the proposed detection algorithm. The energy consumption, throughput, packet delivery ratio ,end to end relay, overhead are the key identifiers and indicators of an efficient DOS attack detection algorithm. These are the key parameters on which we will evaluate our implementation of the proposed DOS attack detection algorithm on the IoT routing protocols.