• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1565-1585
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• 2012

The characteristics of ad hoc networks, such as the absence of infrastructure, a dynamic topology, a shared wireless medium and a resource-constrained environment pose various security challenges. Most previous studies focused on the detection of misbehavior after it had occurred. However, in this paper we propose a new way of thinking to evade the occurrence of misbehavior. In our scheme, we firstly present a clustering algorithm that employs a powerful analytic hierarchy process methodology to elect a clusterhead for each cluster. The clusterhead in each cluster is then allowed to assign the backoff values to its members, i.e., originators, rather than permitting the originators to choose the backoff values by themselves. Through this media access control layer misbehavior detection mechanism, the misuse of the backoff in the media access control layer in the 802.11 distributed coordination function can be detected.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1153-1166
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• 2011

Detecting misbehavior in vehicular ad-hoc networks is very important problem with wide range of implications including safety related and congestion avoidance applications. Most misbehavior detection schemes are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners. Because of rational behavior, it is more important to detect false information than to identify misbehaving nodes. In this paper, we propose the variable precision rough sets based misbehavior detection scheme which detects false alert message and misbehaving nodes by observing their action after sending out the alert messages. In the proposed scheme, the alert information system, alert profile is constructed from valid actions of moving nodes in vehicular ad-hoc networks. Once a moving vehicle receives an alert message from another vehicle, it finds out the alert type from the alert message. When the vehicle later receives a beacon from alert raised vehicle after an elapse of time, then it computes the relative classification error by using variable precision rough sets from the alert information system. If the relative classification error is lager than the maximum allowable relative classification error of the alert type, the vehicle decides the message as false alert message. Th performance of the proposed scheme is evaluated as two metrics: correct ratio and incorrect ratio through a simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5354-5369
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• 2019

Sensor networks are deployed in unheeded environment to monitor the situation. In view of the unheeded environment and by the nature of their communication channel sensor nodes are vulnerable to various attacks most commonly malicious packet dropping attacks namely blackhole, grayhole attack and sinkhole attack. In each of these attacks, the attackers capture the sensor nodes to inject fake details, to deceive other sensor nodes and to interrupt the network traffic by packet dropping. In all such attacks, the compromised node advertises itself with fake routing facts to draw its neighbor traffic and to plunge the data packets. False routing advertisement play vital role in deceiving genuine node in network. In this paper, behavior based routing misbehavior detection (BRMD) is designed in wireless sensor networks to detect false advertiser node in the network. Herein the sensor nodes are monitored by its neighbor. The node which attracts more neighbor traffic by fake routing advertisement and involves the malicious activities such as packet dropping, selective packet dropping and tampering data are detected by its various behaviors and isolated from the network. To estimate the effectiveness of the proposed technique, Network Simulator 2.34 is used. In addition packet delivery ratio, throughput and end-to-end delay of BRMD are compared with other existing routing protocols and as a consequence it is shown that BRMD performs better. The outcome also demonstrates that BRMD yields lesser false positive (less than 6%) and false negative (less than 4%) encountered in various attack detection.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2447-2455
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• 2018

Security is more important in many sensor applications. The node replication attack is a major issue on sensor networks. The replicated node can capture all node details. Node Replication attacks use its secret cryptographic key to successfully produce the networks with clone nodes and also it creates duplicate nodes to build up various attacks. The replication attacks will affect in routing, more energy consumption, packet loss, misbehavior detection, etc. In this paper, a Secure-Efficient Centralized approach is proposed for detecting a Node Replication Attacks in Wireless Sensor Networks for Static Networks. The proposed system easily detects the replication attacks in an effective manner. In this approach Secure Cluster Election is used to prevent from node replication attack and Secure Efficient Centralized Approach is used to detect if any replicated node present in the network. When comparing with the existing approach the detection ratio, energy consumption performs better.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.71-80
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• 2009

Ad-hoc network consists f mobile nodes, which they are together in the communication. However, if some misbehaving nodes are in network, it is faced to many threats. Therefore, detection and management of misbehaving node are necessary to make confident in Ad-hoc networks. To solve this problem, we use Node Weight Management Server(NWMS), which it manage each node's weight in local area. When NWMS detect misbehaving node, it adds the node's weight and if the node's weight exceeds threshold then NWMS broadcasts the node's information to isolate in network. These mechanisms show that they are highly effective and can reliably detect a multitude of misbehaving node.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3B
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• pp.408-420
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• 2010

On a Mobile Ad hoc NETwork(MANET), it is difficult to detect and prevent misbehaviors nodes existing between end nodes, as communication between remote nodes is made through multiple hop routes due to lack of a fixed networked structure. Therefore, to maintain MANET's performance and security, a technique to identify misbehaving middle nodes and nodes that are compromise by such nodes is required. However, previously proposed techniques assumed that nodes comprising MANET are in a friendly and cooperative relationship, and suggested only methods to identify misbehaving nodes. When these methods are applied to a larger-scale MANET, large overhead is induced. As such, this paper suggests a system model called Secure Cluster-based MANET(SecCBM) to provide secure communication between components aperANET and to ensure eed. As such, this pand managems suapemisbehavior nodes. SecCBM consists apetwo stages. The first is the preventis pstage, whereemisbehavior nodes are identified when rANET is comprised by using a cluster-based hierarchical control structure through dynamic authentication. The second is the post-preventis pstage, whereemisbehavior nodes created during the course apecommunication amongst nodes comprising the network are dh, thed by using FC and MN tables. Through this, MANET's communication safety and efficiency were improved and the proposed method was confirmed to be suitable for MANET through simulation performance evaluation.

• Journal of the Korea Safety Management & Science
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• v.21 no.4
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• pp.7-15
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• 2019

This paper proposes the FMEA-based model to avoid backdoor transactions when purchasers select suppliers for products and services. In the model, backdoor transactions consist of two categories: backdoor selling and maverick buying. Both of which influence negative effects on cost savings due to not only uncompetitive advantage but also unusable purchasing leverage by unethical and misbehavior of purchase requestors. For the risk evaluation based on FMEA, three and five risk types of backdoor selling and maverick buying are identified respectively. Current risk priority numbers(RPN) based on those risk types are calculated by three categories: occurrence, detection and severity. Six risk mitigation strategies and fourteen mitigation tactics are identified to improve current RPN. In order to validate the model, questionnaires are collected from fifteen companies and statistically analyzed. The analysis result shows that the model reduces backdoor transaction risks and has no differences in reduction of backdoor transaction risks regardless of the type of purchasing organization units and existence of purchasing procedures in the organization.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.12-21
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• 2012

IEEE 802.11 MAC protocol standard, DCF(CSMA/CA), is originally designed to ensure the fair channel access between mobile nodes sharing the local wireless channel. It has been, however, revealed that some misbehavior nodes transmit more data than other nodes through artificial means in hot spot area spreaded rapidly. The misbehavior nodes may modify the internal process of their MAC protocol or interrupt the MAC procedure of normal nodes to achieve more data transmission. This problem has been referred to as a selfish node problem and almost literatures has proposed methods of analyzing the MAC procedures of all mobile nodes to detect the selfish nodes. However, these kinds of protocol analysis methods is not effective at detecting all kinds of selfish nodes enough. This paper address this problem of detecting selfish node using Tsallis-Entropy which is a kind of statistical method. Tsallis-Entropy is a criteria which can show how much is the density or deviation of a probability distribution. The proposed algorithm which operates at a AP node of wireless LAN extracts the probability distribution of data interval time for each node, then compares the one with a threshold value to detect the selfish nodes. To evaluate the performance of proposed algorithm, simulation experiments are performed in various wireless LAN environments (congestion level, how selfish node behaviors, threshold level) using ns2. The simulation results show that the proposed algorithm achieves higher successful detection rate.