Detection of SPAM Attacks in the Remote Triggered WSN Experiments
Spam attack is the deliberate delivery of unsolicited or unwanted messages across the computer networks with the intention to deplete the resources that results in Denial of Service (DoS) to the end user. This is more important to consider in Wireless sen
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Abstract $Spam attack is the deliberate delivery of unsolicited or unwanted messages across the computer networks with the intention to deplete the resources that results in Denial of Service (DoS) to the end user. This is more important to consider in Wireless sensor networks test beds where the nodes already have only little computing resources (4kb RAM), and low network bandwidth for their applications. The Remote Triggered WSN test bed (http://vlab.amrita.edu/?sub=78) that we have deployed in our university consists of more than 80 nodes connected with various sensors, digital multimeters etc., allows any student in the internet to upload their programs, execute them and view their experiment results with real time video streaming to learn the WSN concepts intuitively. Hence, there is a need to detect such type of spam attacks in the test bed, in case, a user uploads the malicious programs that affects the functioning of nodes in other experiments. We have tried two packet inspection techniques, Gaussian Naive Bayes (GNB) and k-Nearest Neighbour (K-NN) for learning the pattern and identifying whether the new incoming message is Spam or Non-spam. It is observed that the GNB method could catch spam messages at 94-96% Accuracy, with only 5-10% false positive rate (FPR). It is also found that the performance of k-NN gradually decreases as k-value increases. The complexity and execution speed becomes worse at larger k-values where as they are invariant in case of GNB. Hence it shows GNB is more appropriate than k-NN for inspecting the messages.
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Introduction
A sensor network is a collection of sensor nodes that collaboratively work in wireless communications architecture. As sensor nodes are deployed densely in an area can frequently exchange messages, there can be thousands of message S. Kumar() · P. Pradeep · S. Kj Amrita Vishwa Vidyapeetham University, Coimbatore, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 K.J. Kim and N. Joukov (eds.), Information Science and Applications (ICISA) 2016, Lecture Notes in Electrical Engineering 376, DOI: 10.1007/978-981-10-0557-2_70
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exchanges possible over a period of time. In such scenarios, there has to be security measure to detect spam attacks, wherein a third kind of node i.e. SPAM node can inject unsolicited or spam messages which can deplete the network, memory, battery resources in WSN. We can notice spam delivery in areas like Networking, E-mail, Defence, Internet, Web-sites etc. which is considered as a breach of security. Various methods have been proposed to solve SPAM attacks in other areas like e-mails, mobile SMSs, and content of Web pages where they use filters, pattern classification, machine learning, string matching, packet inspection etc. After analysing these literature works, it is found that packet inspection techniques using Machine learning and Data mining approach works better in such situations. So we view this as a classification problem, which classify messages exchanged in a
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