Detection of Malicious Node in Wireless Sensor Network Based on Data Mining

Singh, Mandeep; Mehta, Gaurav; Vaid, Chetna; Oberoi, Parul

doi:10.1109/iccs.2012.24

Cited by 13 publications

(10 citation statements)

References 4 publications

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“…Therefore, they are internal or external attacks that have a direct impact on the network by denying users the available resources [9]. The main motivation of selecting the DoS attack is that it can be launched against any layer in the network [10] and is therefore a high priority to defend against. Yan et al proposed a novel solution to this security problem, namely; a combinatorial approach to collect data from three different resources: radar detection, traffic and neighboring data.…”

Section: Related Workmentioning

confidence: 99%

An intrusion detection system against malicious attacks on the communication network of driverless cars

Alheeti

Gruebler

McDonald-Maier

2015

2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)

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Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack. Keywords-security; vehicular ad hoc networks; intrusion detection system; driverless car.We propose a new approach to secure external communication in self-driving and semi self-driving vehicles

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Section: Related Workmentioning

confidence: 99%

An intrusion detection system against malicious attacks on the communication network of driverless cars

Alheeti

Gruebler

McDonald-Maier

2015

2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC)

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“…We will use the bootstrapped ID3 that is called J48, decision tree over the data to be trained. J48 extracts the best attribute from the training set that can be utilized for the partitioning of the given samples [28]. It terminates its operation when the attribute efficiently and fully classifies the training set, or else it recursively functions on the 'm' detached subsets to get the "best" attributes.…”

Section: B Decision Treementioning

confidence: 99%

“…The service models of cloud are represented as a layered architecture of cloud computing. The three most commonly used service models [28] are described in Figure 1, where resources that make the resource pool and pull the computing power for cloud computing lie in IaaS [3], the platform for providing the applications to the user is present in PaaS [4,28] and the services that are hosted at users' machines are deployed by SaaS [21,28], sometimes also called "On-demand software" [31]. The user interfaces lie on the top of these three models.…”

Section: Introductionmentioning

confidence: 99%

“…The term cloud in cloud computing accelerates the way through which everythingfrom computing power to computing infrastructure, multiple applications, various business processes to non public collaborations -can be delivered to users as a service wherever and whenever required [2]. Cloud computing is a sculpture for facilitating ubiquitous (availability), convenient, on-demand network access to a shared pool of configurable computing resources (e.g., servers, storage, networks, applications, and services) that can be swiftly provisioned and released with nominal management exertion or service provider interaction [28].…”

Section: Introductionmentioning

confidence: 99%

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Anomaly-based IDS implementation in cloud environment using BOAT algorithm

Vaid

Verma

2014

Proceedings of 3rd International Conference on Reliability, Infocom Technologies and Optimization

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Innovations are essential to ride the inevitable tide of revolutions. Most of enterprises are striving to reduce their computing cost through the means of virtualization. This demand of reducing the computing cost has led to the innovation of cloud computing. With the increasing number of companies resorting to employ resources in the cloud, the protection of the users' data is becoming a significant issue of concern. This report tackles this concern for enterprises in terms of security with intrusion detection while adopting cloud computing. The main aim of this research is to understand the security threats and identify a security technique used to mitigate them in cloud computing. The intrusion detection will be undertaken on the basis of anomalydetection on the data generated from the transactions captured within the cloud network. The data captured will undergo intense data mining through clustering and then performing classification using BOAT algorithm to detect the presence of intruders on the cloud. The mining approach will consider various attributes of the data to scrutinize the user behaviour.

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“…the most objective of paper [16] is to construct an efficient detector security sightion system that is adaptation to the behavioral changes of the nodes and therefore the knowledge flowing between varied detector nodes and thence detect the malicious node in our surroundings supported its skeptical behavior.…”

Section: Related Workmentioning

confidence: 99%

Anomalous Node Detection and Removal in Ad-hoc Network using Enhanced Prime Product Scheme

Siddiqui¹,

Farooqui²

2015

IJCA

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In Associate in Nursing Ad-hoc network may be a assortment of mobile nodes dynamically forming a short lived network while not the employment of any existing network infrastructure or centralized administer. attributable to restricted communication vary among mobile nodes in ad-hoc network, many network hopes is also required to deliver a packet from one node to a different node within the wireless network. Many users want to save its resources like battery power, processing and capability for only their personal use hence such nodes become misbehaving in nature and not cooperate do selfish activities. The security of MANET is manipulated by malicious node attack. In such type of attach, a malicious node insert a fake route reply claiming to have the shortest and freshest route to the destination. However, when the data packets arrive, the malicious node discards them. To avoid malicious attack, this work gives behavior analysis with PPN (Prime Product Number) scheme for detection and removal of malicious node.

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Detection of Malicious Node in Wireless Sensor Network Based on Data Mining

Cited by 13 publications

References 4 publications

An intrusion detection system against malicious attacks on the communication network of driverless cars

An intrusion detection system against malicious attacks on the communication network of driverless cars

Anomaly-based IDS implementation in cloud environment using BOAT algorithm

Anomalous Node Detection and Removal in Ad-hoc Network using Enhanced Prime Product Scheme

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