15,743 research outputs found

    Web spider defense technique in wireless sensor networks

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    Wireless sensor networks (WSNs) are currently widely used in many environments. Some of them gather many critical data, which should be protected from intruders. Generally, when an intruder is detected in the WSN, its connection is immediately stopped. But this way does not let the network administrator gather information about the attacker and/or its purposes. In this paper, we present a bioinspired system that uses the procedure taken by the web spider when it wants to catch its prey. We will explain how all steps performed by the web spider are included in our system and we will detail the algorithm and protocol procedure. A real test bench has been implemented in order to validate our system. It shows the performance for different response times, the CPU and RAM consumption, and the average and maximum values for ping and tracert time responses using constant delay and exponential jitter.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", Project TEC2011-27516.Cánovas Solbes, A.; Lloret, J.; Macias Lopez, EM.; Suarez Sarmiento, A. (2014). Web spider defense technique in wireless sensor networks. International Journal of Distributed Sensor Networks. 2014:1-7. https://doi.org/10.1155/2014/348606S172014Bri, D., Garcia, M., Lloret, J., & Dini, P. (2009). Real Deployments of Wireless Sensor Networks. 2009 Third International Conference on Sensor Technologies and Applications. doi:10.1109/sensorcomm.2009.69Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Xie, M., Han, S., Tian, B., & Parvin, S. (2011). Anomaly detection in wireless sensor networks: A survey. Journal of Network and Computer Applications, 34(4), 1302-1325. doi:10.1016/j.jnca.2011.03.004Yu, Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867-880. doi:10.1016/j.jnca.2011.03.005Zhu, W. T., Zhou, J., Deng, R. H., & Bao, F. (2012). Detecting node replication attacks in wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1022-1034. doi:10.1016/j.jnca.2012.01.002Maleh, Y., & Ezzati, A. (2013). A Review of Security Attacks and Intrusion Detection Schemes in Wireless Sensor Network. International Journal of Wireless & Mobile Networks, 5(6), 79-90. doi:10.5121/ijwmn.2013.5606Alrajeh, N. A., Khan, S., & Shams, B. (2013). Intrusion Detection Systems in Wireless Sensor Networks: A Review. International Journal of Distributed Sensor Networks, 9(5), 167575. doi:10.1155/2013/167575Sun, B., Osborne, L., Xiao, Y., & Guizani, S. (2007). Intrusion detection techniques in mobile ad hoc and wireless sensor networks. IEEE Wireless Communications, 14(5), 56-63. doi:10.1109/mwc.2007.4396943Fatema, N., & Brad, R. (2013). Attacks and Counterattacks on Wireless Sensor Networks. International Journal of Ad hoc, Sensor & Ubiquitous Computing, 4(6), 1-15. doi:10.5121/ijasuc.2013.4601Ankala, R. P., Kavitha, D., & Haritha, D. (2011). MOBILE AGENT BASED ROUTING in MANETS –ATTACKS & DEFENCES. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1351Hylsberg Jacobsen, R., Zhang, Q., & Skjødeberg Toftegaard, T. (2011). Bioinspired Principles for Large-Scale Networked Sensor Systems: An Overview. Sensors, 11(4), 4137-4151. doi:10.3390/s110404137Kofahi, N. (2013). An Empirical Study to Compare the Performance of some Symmetric and Asymmetric Ciphers. International Journal of Security and Its Applications, 7(5), 1-16. doi:10.14257/ijsia.2013.7.5.01Sisodia, M. S., & Raghuwanshi, V. (2011). Anomaly Base Network Intrusion Detection by Using Random Decision Tree and Random Projection: A Fast Network Intrusion Detection Technique. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1342Zhijie, H., & Ruchuang, W. (2012). Intrusion Detection for Wireless Sensor Network Based on Traffic Prediction Model. Physics Procedia, 25, 2072-2080. doi:10.1016/j.phpro.2012.03.352Al-Gharabally, N., El-Sayed, N., Al-Mulla, S., & Ahmad, I. (2009). Wireless honeypots. Proceedings of the 2009 conference on Information Science, Technology and Applications - ISTA ’09. doi:10.1145/1551950.1551969Gopinath V.Success analysis of deception in wireless sensor networks [M.S. thesis]2010Oklahoma State UniversityZhongshan Zhang, Keping Long, Jianping Wang, & Dressler, F. (2014). On Swarm Intelligence Inspired Self-Organized Networking: Its Bionic Mechanisms, Designing Principles and Optimization Approaches. IEEE Communications Surveys & Tutorials, 16(1), 513-537. doi:10.1109/surv.2013.062613.00014Rathore, H., & Jha, S. (2013). Bio-inspired machine learning based Wireless Sensor Network security. 2013 World Congress on Nature and Biologically Inspired Computing. doi:10.1109/nabic.2013.6617852Alrajeh, N. A., & Lloret, J. (2013). Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks. International Journal of Distributed Sensor Networks, 9(10), 351047. doi:10.1155/2013/351047Amirkolaei M. K.Enhancing bio-inspired intrusion response in Ad-hoc networks [Ph.D. thesis]August 2013Edinburgh, UKEdinburgh Napier Universityhttp://researchrepository.napier.ac.uk/6533/Muraleedharan, R., & Osadciw, L. A. (2009). An intrusion detection framework for Sensor Networks using Honeypot and Swarm Intelligence. Proceedings of the 6th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. doi:10.4108/icst.mobiquitous2009.7084Hortos, W. S. (2012). Bio-inspired, cross-layer protocol design for intrusion detection and identification in wireless sensor networks. 37th Annual IEEE Conference on Local Computer Networks -- Workshops. doi:10.1109/lcnw.2012.6424040Benahmed, K., Merabti, M., & Haffaf, H. (2012). Inspired Social Spider Behavior for Secure Wireless Sensor Networks. International Journal of Mobile Computing and Multimedia Communications, 4(4), 1-10. doi:10.4018/jmcmc.2012100101Herberstein, M. E. (Ed.). (2009). Spider Behaviour. doi:10.1017/cbo9780511974496Ficco, M. (2010). Achieving Security by Intrusion-Tolerance Based on Event Correlation. Network Protocols and Algorithms, 2(3). doi:10.5296/npa.v2i3.42

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

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    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Outlier Detection Techniques For Wireless Sensor Networks: A Survey

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    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the multivariate nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a decision tree to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier degree

    Sleep Deprivation Attack Detection in Wireless Sensor Network

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    Deployment of sensor network in hostile environment makes it mainly vulnerable to battery drainage attacks because it is impossible to recharge or replace the battery power of sensor nodes. Among different types of security threats, low power sensor nodes are immensely affected by the attacks which cause random drainage of the energy level of sensors, leading to death of the nodes. The most dangerous type of attack in this category is sleep deprivation, where target of the intruder is to maximize the power consumption of sensor nodes, so that their lifetime is minimized. Most of the existing works on sleep deprivation attack detection involve a lot of overhead, leading to poor throughput. The need of the day is to design a model for detecting intrusions accurately in an energy efficient manner. This paper proposes a hierarchical framework based on distributed collaborative mechanism for detecting sleep deprivation torture in wireless sensor network efficiently. Proposed model uses anomaly detection technique in two steps to reduce the probability of false intrusion.Comment: 7 pages,4 figures, IJCA Journal February 201
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