Intrusion Prevention and Detection in Wireless Sensor Network
Volumn 1

Intrusion Prevention and Detection in Wireless Sensor Network

Vivek A. Chakole1
Research Scholor
Department of Electronics Communication Technocrats Institute of Technology, EXCELLENCE ,

Gulrej Ahmed2
Asst. Professor
Department of Electronics Communication Technocrats Institute of Technology, EXCELLENCE ,


The research of IDS in wireless sensor networks has not advanced significantly, because an effective intrusion detection system (IDS) can help us to design better prevention mechanisms, on collecting information of intruder detection technique and its attack patterns. There are a few attempts that concentrate on specific attacks, but not generalized approach that can be both realistic and lightweight enough to run on computationally and memory restricted devices such as the nodes of a sensor network. In this research we will design the number of attacks pattern on number of nodes of sensor network. The generalized approach that can be applicable for both computationally and memory restricted devices.

Keywords – IDS, WSN, DES


The security mechanisms such as encryption and decryption provide an essential authentication to protect information transfers. The broadcast nature of the transmission medium in wireless sensor networks makes information more vulnerable than in wired applications [1]. However, existing network security mechanisms are not feasible in this domain, because it provide limited storage, bandwidth and resources. The Public- key algorithms emphasize methods having expensive technolo- gies [2]. Instead, symmetric encryption/decryption algorithms and hashing method are between two to four orders of mag- nitude faster, and compose the basic tools for securing sensor networks Communication. To develop security mechanisms and protocols for sensor networks [2], a necessary requirement is security key known as the establishment and maintenance of shared keys between pairs of communicating nodes. However, bootstrapping secures communication between sensor nodes by setting up secret keys among them, can become a tough task. If we know which nodes would be in the same vicinity could decide a priority before deployment of keys [1, 2]. Unfortunately most sensor network deployments are random therefore such a priori knowledge does not exist. There are also some other necessary mode that need to be considered while designing a key management. A desirable feature is resistance to the node capture. Then the node is compromised with its key material is revealed, an adversary should not be able to gain control of other parts of the network by using such material. Therefore the compromise of nodes should result in a breach of security that is constrained within a small, localized part of

the network [5, 8] appear constantly. As a result, attacker can always find security holes to utilize in order to gain access in the sensor networks. These intrusions will go unnoticed and they will likely lead to failure in the normal operation of the network.

Fig. 1. Intruder Detection


We first propose a distributed, deterministic key manage- ment protocol designed to satisfy authentication and confiden- tiality, which does not requires the key distribution center. The scheme is scalable since every node only needs to hold a small number of keys independent size of existing networks, and it is resilient to the attacked node capture and replication due to the fact that keys are localized keys that appear in some part of the network are not used again. Another important property of the protocol is that it is optimized for message broadcast [2, 4]. Each node share a pair wise key with all its immediate environs, so only one transmission is necessary to broadcast a message. This protocol later used to design novel techniques for security in network processing (i.e. secure data aggregation) for sensor networks. Data aggregation is possible only if intermediate nodes have access to encrypted data so that they can extract measurement values and apply to them

aggregation functions. Therefore, nodes that send data packets toward the base station must encrypt them with keys available to the aggregating nodes by using key management scheme provided in.NET. Not that it is in any way finer or inferior to C#, but most of the other examples (and all of the good ones) were done in C#[3]. It uses the UTF- encoder to ensure that the strings which are encrypted or decrypted are in an eight-bit format. Many examples that uses the ASCII format of coding, which is a 7-bit format. When you combine this with Triple DES [6], you can get yourself into situations where you cannot decrypt something you encrypted. It uses the Convert object’s Base-64 methods to make sure that the encrypted text is output in such a way that it can be easily stored in text files and/or database fields without the risk of your encrypted content being inadvertently modified by implicit conversions. It centralizes the actual encryption and decryption functional- ity into a one method, thus removing what would otherwise be ninety-nine percentage superfluous codes Confidentiality.

Fig. 2. Cluster nodes
Fig. 3. Flow Chart

In order to protect sensed data and communication exchanges between sensor nodes it is important to guarantee the secrecy with the messages. In the sensor network were it can be usually achieved by the use of symmetric cryptography as asymmetric key cryptography in general is considered highly costing of the system [4]. However, while encryption protects against outside attacks, it does not protect against inside attack which contains, as an attacker can use recovered cryptographic key to successfully insure to participate in the secret communications

of the networks. Furthermore, while confidentiality guarantees the security of communications inside the network, it does not prevent the exploitation of the information reaching the base station. Hence, confidentiality must also be coupled with the right control policies so that only authorized users can have access to confidential information Integrity and Authentication Integrity and authentication is necessary to enable sensor nodes to detect the packets which are modified, or injected, or replayed packets. While it is clear those safety critical applications require authentication and it is still use it even for the rest of applications, otherwise the owner of the sensor network may get the wrong picture of the sensed world thus making inappropriate decisions [5]. However, authentication alone does not solve the problem of node takeovers as compro- mised nodes can still authenticate themselves to the network. Therefore, authentication mechanisms should be collective and aim at securing the entire network. First we focused on the establishment of trust relationship among wireless sensor nodes, and presented a key management protocol for sensor networks. The protocol includes support for establishing four types of keys per sensor node having the unique keys shared with the base station, which will have the pair wise keys shared along the individual neighboring nodes, and a group key which is to be shared with all the nodes in the network. We showed how the keys can be distributed so that the protocol can support in-network processing and efficient dissemination, while restricting the security impact of a node compromise to the immediate network neighborhood of the compromised node. Applying the protocol makes it really hard for an adversary to dislocate the normal operation of the networks [6, 8].


Shared key cryptography can not be used to secure other op- erations in sensor networks, like network programming, where bulk data have to be disseminated from the base station to the sensor nodes. We therefore presented a method for verifying the integrity and authenticity of such data [7]. In particular, the following requirements must be supported by the key management scheme, in order to assist data aggregation and dissemination in process.

  1. Data aggregation is possible only if intermediate nodes have access to encrypt data so that they can extract mea- surement value and apply to them aggregation functions. Hence, nodes that send data packets toward the base station must encrypt them with keys available to the aggregation nodes [8, 10].
  2. Data dissemination implies broadcasting of a message from the aggregate to its group members. In order to broadcast a message to all the nodes, an aggregate shares a different key (or set of keys) with each of the sensor within its group and then it will have to make multiple transmissions encrypted each time with different key. But transmissions must be kept as low as possible because of their high energy burning rate [9, 11].


We proposed a intrusion detection system (IDSs) for net- works. The proposed distributed learning algorithm for the training of reaches high accuracy for detecting the normal and anomalous behavior (accuracy rate over 98%). Signature Based Detection achieves a high detection rate with low false positive rate. Communication in Network consumes a high energy, instead of transmitting all captured data to a centralized point. We also propose the detection Process for intrusion as Encryption and Decryption on each and every node of WSN which is to be developed in this paper


  1. T. H. Hai, F. khan, and E. N. Huh, “Hybrid Intrusion Detection System for Wireless Sensor Networks”, In Proceeding of the ICCSA, LNCS 4706, pp. 383-396.
  2. R. Roman, C. Alcaraz, and J. Lopez, “A Survey of Cryptographic Primitives and Implementations for Hardware-Constrained Sensor Network Nodes”, Mobile Networks and Applications, Springer. Vol.12, no 4, pp 231-244.
  3. J. P. Walters, Z. Liang, W. Shi, and V. Chaudhary, “Wireless Sensor Network Security: A Survey”, Security in Distributed Grid and Pervasive Computing, Auerbach Publications, CRC Press, Vol.1, Issue.2, pp.1-50.
  4. R. Roman, J. Zhou, and J. Lopez, “Applying Intrusion Detection Systems to Wireless Sensor Networks”, the 3rd IEEE Consumer Communications and Networking Conference, pp.640-644, 2006.
  5. Z. J. Haas and T-C. Chen, ”Cluster-based Cooperative Communication with Network Coding in Wireless Networks, ”IEEE MILCOM 2010, San Jose, CA, October 31 – November 3, 2010.
  6. G. E. Arrobo, R. D. Gitlin, Z.J. Haas,” Effect of Link-Level Feedback and Retransmissions on the Performance of Cooperative Networking, ”accepted IEEE WCNC 2011, Cancun, Mexico, March 28-31, 2011.
  7. M. Elhawary, Z. J. Haas, ”Energy-efficient for Cooperative Networks,” accepted for publication in the IEEE/ACM Transactions on Networks, 2011.
  8. M. Patel, J. Wang, ”Applications, challenges, and prospective in emerging body area networking technologies,” IEEE Wireless Communications, vol.17, no.1, 2010, pp.80-88.
  9. H. Alemdar, C. Ersoy, ”Wireless sensor networks for healthcare: A survey,” Computer Networks, vol. 54, Issue 15, 2010, pp. 2688-2710.
  10. H. Cao, V. Leung, C. Chow, C. Chan, ”Enabling technologies for wireless body area networks: A survey and outlook,” IEEE Communications Magazine, vol.47, no.12, 2009, pp.84-93.
  11. IEEE Standard for Safety Levels With Respect to Human Exposure to Radio Frequency Electromagnetic Fields, 3 kHz to 300 GHz, IEEE Standard C95.1, 2005.

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