Hardware Implementation of Intrusion Detection System for Ad-Hoc Network

2016 ◽  
Vol 5 (3) ◽  
pp. 158
Author(s):  
Reji Mano ◽  
P.C. Kishore Raja ◽  
Christeena Joseph ◽  
Radhika Baskar
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Ad Hoc Network ◽  
Hardware Implementation ◽  
Ad Hoc ◽  
New Technologies ◽  
Detection System ◽  
Delivery Ratio ◽  
On Demand ◽  
Distance Vector

<p>New technologies have been developed in wireless adhoc network need more security. To widespread the adhoc networks we turn in the attention of wireless hand held device mobile phones communicate with short distance using wireless lan card or Bluetooth. The performance of mobile phone are improved greatly for last few years .so security is more important for mobile networks In this paper  hardware implementation of single hop ad-hoc network is implemented and analysed using microcontroller. The protocol implemented in this paper is primarily based on, Ad hoc On-Demand Distance Vector routing. We adopt On Demand Distance Vector routing solely based on source routing and “On Demand” process, so each packet does not have to transmit any periodic routing information. We implemented   intrusion detection system with five different nodes and the performance parameters like packet delivery ratio, throughput, delay are computed with attacker and without attacker and on demand distance vector routing protocols is proposed to implement in hardware using Zigbee</p>

scholarly journals An intrusion detection system for the prevention of an active sinkhole routing attack in Internet of things

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988990 ◽  
Author(s):  
Sabeen Tahir ◽  
Sheikh Tahir Bakhsh ◽  
Rayan A Alsemmeari
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Base Station ◽  
Detection Techniques ◽  
On Demand ◽  
Distance Vector ◽  
Routing Attack

Internet of things (IoT) is a complex and massive wireless network, where millions of devices are connected together. These devices gather different types of data from different systems that transform human daily lives by modernizing home appliances, business, medicine, traveling, research, and so on. Security is a critical challenge for a stable IoT network, for instance, routing attacks, especially sinkhole attack is a severe attack which has the capability to direct network data toward the intruder, and it can also disrupt and disconnect the devices from their network. The IoT needs multi-facet security solutions where network communication is protected with integrity, confidentiality, and authentication verification services. Therefore, the IoT network should be secured against intrusions and disruptions; the data exchanged throughout the network should be an encrypted form. In this article, an intrusion detection system for the prevention of an active sinkhole routing attack (PASR) in IoT is presented. The proposed PASR solves the problem of the sinkhole attack; for this purpose, the whole network is divided into the clusters of IoT. All the IoT devices are connected to their respective gateways. The gateway devices are equipped with an intrusion detection system. The intrusion detection system activates intrusion analyzer to detect anomalies in the context of ad hoc on-demand distance vector protocol. The base station is the main device that is responsible to receive data from all devices. Therefore, it detects and prevents sinkhole attacks; the base station keeps the record of all active devices and their possible links. The PASR is implemented and compared with the existing intrusion detection techniques ad hoc on-demand distance vector, and dual attack detection for black and gray hole attack. It was observed from the simulation results that the PASR outperforms in terms of data packet delivery, energy consumption, the detection rate of sinkhole attack, and routing overhead.

scholarly journals A Hybrid Intrusion Detection System for Mobile Adhoc Networks using FBID Protocol

2020 ◽  
Vol 21 (1) ◽  
pp. 137-145
Author(s):  
D Rajalakshmi ◽  
K Meena
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Ad Hoc Network ◽  
Cognitive Mapping ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Performance Metrics ◽  
Detection System ◽  
Delivery Ratio ◽  
Mobile Ad Hoc

The security in a mobile ad hoc networks is more vulnerable and susceptible to the environment, because in this network no centralized environment for monitoring individual nodes activity during communication. The intruders are hacked the networks either locally and globally. Now a day’s mobile ad hoc network is an emerging area of research due to its unique characteristics. It’s more vulnerable to detect malicious activities, and error prone in nature due to their dynamic topology configuration. Based on their difficulties of intrusion detection system, in this paper proposed a novel approach for mobile ad hoc network is Fuzzy Based Intrusion Detection (FBID) protocol, to identify, analyze and detect a malicious node in different circumstances. This protocol it improves the efficiency of the system and does not degrade the system performance in real time.This FBID system is more efficient and the performance is compared with AODV, Fuzzy Cognitive Mapping with the following performance metrics: Throughput, Packet Delivery Ratio, Packets Dropped, Routing overhead, Propagation delay and shortest path for delivering packets from one node to another node. The System is robust. It produces the crisp output to the benefit of end users. It provides an integrated solution capable of detecting the majority of security attacks occurring in MANETs.

Artificial Intelligence Based Intrusion Detection System to Detect Flooding Attack in VANETs

2018 ◽  
pp. 87-100 ◽  
Author(s):  
Mannat Jot Singh Aneja ◽  
Tarunpreet Bhatia ◽  
Gaurav Sharma ◽  
Gulshan Shrivastava
Keyword(s):  
Intrusion Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Performance Metrics ◽  
Detection System ◽  
Delivery Ratio ◽  
Flooding Attack ◽  
Hoc Networks

This chapter describes how Vehicular Ad hoc Networks (VANETs) are classes of ad hoc networks that provides communication among various vehicles and roadside units. VANETs being decentralized are susceptible to many security attacks. A flooding attack is one of the major security threats to the VANET environment. This chapter proposes a hybrid Intrusion Detection System which improves accuracy and other performance metrics using Artificial Neural Networks as a classification engine and a genetic algorithm as an optimization engine for feature subset selection. These performance metrics have been calculated in two scenarios, namely misuse and anomaly. Various performance metrics are calculated and compared with other researchers' work. The results obtained indicate a high accuracy and precision and negligible false alarm rate. These performance metrics are used to evaluate the intrusion system and compare with other existing algorithms. The classifier works well for multiple malicious nodes. Apart from machine learning techniques, the effect of the network parameters like throughput and packet delivery ratio is observed.

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