Sleep Deprivation Attack Detection in Wireless Sensor Network

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

Validating sensor nodes in Wireless sensor networks using scoring algorithm

Sensor networks are frequently used to collect data in the environment such as agriculture, forest monitoring, healthcare, and military battlefield. In Wireless Sensor Networks (WSN), nodes are used to monitor the environment and gather data where sinks can be used to collect the data from the sensor nodes and transfer them to the back-end server for processing. These sensible data are moved from one node to another node in the network. Such data should not be considered for public accessibility by the nodes in the network where the visibility and ease of access can only be achieved through either authenticated nodes or right authenticated persons. As sensor node can collect an important data (such as medical or military data), security is a critical issue. Hence, the sensor network needs a secure authentication mechanism to solve this problem and protects the unauthorized access. Therefore, the authentication mechanism used by the node and the sink node must be very efficient in terms of both computational time and energy consumptions. This is especially important for nodes with computing capabilities and battery lifetime is very low. Moreover, for extremely lightweight devices, efficient security solutions with simple mathematics operation and low energy consumptions are still required. To make an authentication decision in real-time, a scoring algorithm examines the user model and the user’s recent behavior, and outputs a score indicating the likelihood that the correct user is using the device. The score is used to make an authentication decision

Intrusion Detection in Mobile Adhoc Network with Bayesian model based MAC Identification

Mobile Ad-hoc Networks (MANETs) are a collection of heterogeneous, infrastructure less, self-organizing and battery powered mobile nodes with different resources availability and computational capabilities. The dynamic and distributed nature of MANETs makes them suitable for deployment in extreme and volatile environmental conditions. They have found applications in diverse domains such as military operations, environmental monitoring, rescue operations etc. Each node in a MANET is equipped with a wireless transmitter and receiver, which enables it to communicate with other nodes within its wireless transmission range. However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node?s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Intrusion Detection in Mobile Ad-Hoc Networks using Bayesian Game Methodology

The dynamic and distributed nature of MANETs make them vulnerable to various types of attacks like black hole attack, traffic distortion, IP spoofing, DoS attack etc. Malicious nodes can launch attacks against other normal nodes and deteriorate the overall performance of the entire network [1�3]. Unlike in wired networks, there are no fixed checkpoints like router and switches in MANETs, where the Intrusion Detection System (IDS) can be deployed .However, due to limited wireless communication range and node mobility, nodes in MANET must cooperate with each other to provide networking services among themselves. Therefore, each node in a MANET acts both as a host and a router. Present Intrusion Detection Systems (IDSs) for MANETs require continuous monitoring which leads to rapid depletion of a node�s battery life. To avoid this issue we propose a system to prevent intrusion in MANET using Bayesian model based MAC Identification from multiple nodes in network. Using such system we can provide lightweight burden to nodes hence improving energy efficiency. Simulated results shows improvement in estimated delay and average bits transfer parameter