• Title/Summary/Keyword: Malicious sensor detection

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Development of the Wireless Sensor S/W for Wireless Traffic Intrusion Detection/Protection on a Campus N/W (캠퍼스 망에서의 무선 트래픽 침입 탐지/차단을 위한 Wireless Sensor S/W 개발)

  • Choi, Chang-Won;Lee, Hyung-Woo
    • Journal of the Korea Society of Computer and Information
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    • v.11 no.6 s.44
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    • pp.211-219
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    • 2006
  • As the wireless network is popular and expanded, it is necessary to development the IDS(Intrusion Detection System)/Filtering System from the malicious wireless traffic. We propose the W-Sensor SW which detects the malicious wireless traffic and the W-TMS system which filters the malicious traffic by W-Sensor log in this paper. It is efficient to detect the malicious traffic and adaptive to change the security rules rapidly by the proposed W-Sensor SW. The designed W-Sensor by installing on a notebook supports the mobility of IDS in compare with the existed AP based Sensor.

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Secure and Robust Clustering for Quantized Target Tracking in Wireless Sensor Networks

  • Mansouri, Majdi;Khoukhi, Lyes;Nounou, Hazem;Nounou, Mohamed
    • Journal of Communications and Networks
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    • v.15 no.2
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    • pp.164-172
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    • 2013
  • We consider the problem of secure and robust clustering for quantized target tracking in wireless sensor networks (WSN) where the observed system is assumed to evolve according to a probabilistic state space model. We propose a new method for jointly activating the best group of candidate sensors that participate in data aggregation, detecting the malicious sensors and estimating the target position. Firstly, we select the appropriate group in order to balance the energy dissipation and to provide the required data of the target in the WSN. This selection is also based on the transmission power between a sensor node and a cluster head. Secondly, we detect the malicious sensor nodes based on the information relevance of their measurements. Then, we estimate the target position using quantized variational filtering (QVF) algorithm. The selection of the candidate sensors group is based on multi-criteria function, which is computed by using the predicted target position provided by the QVF algorithm, while the malicious sensor nodes detection is based on Kullback-Leibler distance between the current target position distribution and the predicted sensor observation. The performance of the proposed method is validated by simulation results in target tracking for WSN.

Transmission Power Range based Sybil Attack Detection Method over Wireless Sensor Networks

  • Seo, Hwa-Jeong;Kim, Ho-Won
    • Journal of information and communication convergence engineering
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    • v.9 no.6
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    • pp.676-682
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    • 2011
  • Sybil attack can disrupt proper operations of wireless sensor network by forging its sensor node to multiple identities. To protect the sensor network from such an attack, a number of countermeasure methods based on RSSI (Received Signal Strength Indicator) and LQI (Link Quality Indicator) have been proposed. However, previous works on the Sybil attack detection do not consider the fact that Sybil nodes can change their RSSI and LQI strength for their malicious purposes. In this paper, we present a Sybil attack detection method based on a transmission power range. Our proposed method initially measures range of RSSI and LQI from sensor nodes, and then set the minimum, maximum and average RSSI and LQI strength value. After initialization, monitoring nodes request that each sensor node transmits data with different transmission power strengths. If the value measured by monitoring node is out of the range in transmission power strengths, the node is considered as a malicious node.

Bayesian Rules Based Optimal Defense Strategies for Clustered WSNs

  • Zhou, Weiwei;Yu, Bin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.12
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    • pp.5819-5840
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    • 2018
  • Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

A Two level Detection of Routing layer attacks in Hierarchical Wireless Sensor Networks using learning based energy prediction

  • Katiravan, Jeevaa;N, Duraipandian;N, Dharini
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.11
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    • pp.4644-4661
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    • 2015
  • Wireless sensor networks are often organized in the form of clusters leading to the new framework of WSN called cluster or hierarchical WSN where each cluster head is responsible for its own cluster and its members. These hierarchical WSN are prone to various routing layer attacks such as Black hole, Gray hole, Sybil, Wormhole, Flooding etc. These routing layer attacks try to spoof, falsify or drop the packets during the packet routing process. They may even flood the network with unwanted data packets. If one cluster head is captured and made malicious, the entire cluster member nodes beneath the cluster get affected. On the other hand if the cluster member nodes are malicious, due to the broadcast wireless communication between all the source nodes it can disrupt the entire cluster functions. Thereby a scheme which can detect both the malicious cluster member and cluster head is the current need. Abnormal energy consumption of nodes is used to identify the malicious activity. To serve this purpose a learning based energy prediction algorithm is proposed. Thus a two level energy prediction based intrusion detection scheme to detect the malicious cluster head and cluster member is proposed and simulations were carried out using NS2-Mannasim framework. Simulation results achieved good detection ratio and less false positive.

Energy-efficient intrusion detection system for secure acoustic communication in under water sensor networks

  • N. Nithiyanandam;C. Mahesh;S.P. Raja;S. Jeyapriyanga;T. Selva Banu Priya
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.6
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    • pp.1706-1727
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    • 2023
  • Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

  • Terence, Sebastian;Purushothaman, Geethanjali
    • 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.

Design and Implementation of Sensor based Intrusion Detection System (센서 기반 침입 탐지 시스템의 설계와 구현)

  • Choi, Jong-Moo;Cho, Seong-Je
    • The KIPS Transactions:PartC
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    • v.12C no.6 s.102
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    • pp.865-874
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    • 2005
  • The information stored in the computer system needs to be protected from unauthorized access, malicious destruction or alteration and accidental inconsistency. In this paper, we propose an intrusion detection system based on sensor concept for defecting and preventing malicious attacks We use software sensor objects which consist of sensor file for each important directory and sensor data for each secret file. Every sensor object is a sort of trap against the attack and it's touch tan be considered as an intrusion. The proposed system is a new challenge of setting up traps against most interception threats that try to copy or read illicitly programs or data. We have implemented the proposed system on the Linux operating system using loadable kernel module technique. The proposed system combines host~based detection approach and network-based one to achieve reasonably complete coverage, which makes it possible to detect unknown interception threats.

Detection of Malicious Node using Timestamp in USN Adapted Diffie-Hellman Algorithm (Diffie-Hellman 알고리즘이 적용된 USN에서 타임스탬프를 이용한 악의적인 노드 검출)

  • Han, Seung-Jin;Choi, Jun-Hyeog
    • The Journal of the Korea Contents Association
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    • v.9 no.1
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    • pp.115-122
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    • 2009
  • In this paper, we proposed scheme that we use a difference of timestamp in time in Ubiquitous environments as we use the Diffie-Hellman method that OTP was applied to when it deliver a key between nodes, and can detect a malicious node at these papers. Existing methods attempted the malicious node detection in the ways that used correct synchronization or directed antenna in time. We propose an intermediate malicious node detection way at these papers without an directed antenna addition or the Trusted Third Party (TTP) as we apply the OTP which used timestamp to a Diffie-Hellman method, and we verify safety regarding this. A way to propose at these papers is easily the way how application is possible in Ubiquitous environment.

A Study of Intrusion Detection Scheme based on Software-Defined Networking in Wireless Sensor Networks (무선 센서 네트워크에서 소프트웨어 정의 네트워킹 기법을 사용한 침입 탐지 기법에 대한 연구)

  • Kang, Yong-Hyeog;Kim, Moon Jeong;Han, Moonseog
    • Journal of the Korea Convergence Society
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    • v.8 no.8
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    • pp.51-57
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    • 2017
  • A wireless sensor network is composed of many resource constrained sensor nodes. These networks are attacked by malicious attacks like DDoS and routing attacks. In this paper, we propose the intrusion detection and prevention system using convergence of software-defined networking and security technology in wireless sensor networks. Our proposed scheme detects various intrusions in a central server by accumulating log messages of OpenFlow switch through SDN controller and prevents the intrusions by configuring OpenFlow switch. In order to validate our proposed scheme, we show it can detect and prevent some malicious attacks in wireless sensor networks.