Energy-efficient intrusion detection in wireless sensor network

The use of Wireless Sensor Networks (WSNs) has developed rapidly in the last decade. Deploying tiny sensors with limited battery power in open and unprotected environment and dynamic topology in WSNs raises security issues in this kind of networks. Attacks can occur from any direction and any node in WSNs, so one crucial security challenge is to detect networks' intrusion. There are several algorithms for building Intrusion Detection Systems (IDS) based on different WSN routing protocol classifications with respect to energy-efficient manner. This paper provides an overview of the research on IDS in WSNs, focusing on routing protocol classification depending on network structure with respect to energy consumption as a crucial parameter in these kinds of networks. In addition, some simulation manners are reviewed

Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate

Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate

SEEDI: Secure and Energy Efficient Approach for Detection of an Intruder in Homogeneous Wireless Sensor Networks

Wireless Sensor Networks consists of tiny devices which processes and routes the sensed data. The process of detecting any external and internal intruders entering to a Wireless Sensor Network area is referred to as intrusion detection. An intruder is a moving attacker entering a particular area. In this paper, we propose an algorithm Secure and Energy Efficient Approach for Detection of Intruder (SEEDI) in homogeneous Wireless Sensor Networks. Single sensing and Multi-sensing intruder detection are considered in our algorithm. Simulation results showed that the proposed algorithm resulted in better performance

Secured node detection technique based on artificial neural network for wireless sensor network

The wireless sensor network is becoming the most popular network in the last recent years as it can measure the environmental conditions and send them to process purposes. Many vital challenges face the deployment of WSNs such as energy consumption and security issues. Various attacks could be subjects against WSNs and cause damage either in the stability of communication or in the destruction of the sensitive data. Thus, the demands of intrusion detection-based energy-efficient techniques rise dramatically as the network deployment becomes vast and complicated. Qualnet simulation is used to measure the performance of the networks. This paper aims to optimize the energy-based intrusion detection technique using the artificial neural network by using MATLAB Simulink. The results show how the optimized method based on the biological nervous systems improves intrusion detection in WSN. In addition to that, the unsecured nodes are affected the network performance negatively and trouble its behavior. The regress analysis for both methods detects the variations when all nodes are secured and when some are unsecured. Thus, Node detection based on packet delivery ratio and energy consumption could efficiently be implemented in an artificial neural network

Intrusion Detection Mechanism for Empowered Intruders Using IDEI

In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available

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