Cognitive Model for Identification of Malicious Sensor Node Behavior 1 (original) (raw)
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Detecting Attacks in Wireless Sensor Networks Using Fuzzy Q-Learning
2017
IJPT| Dec-2016 | Vol. 8 | Issue No.4 | 20922-20926 Page 20922 ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com DETECTING ATTACKS IN WIRELESS SENSOR NETWORKS USING FUZZY Q-LEARNING Radhika Baskar, P.C.Kishore Raja, Suraparaju Nikhil Department of ECE, Department of ECE, Saveetha University, Chennai. Received on: 25.09.2016 Accepted on: 15.10.2016 Abstract: The attacks in Wireless Sensor Networks are increasing step-by-step by generating flooding packets that exhaust crucial computing and communication resources of a device being attacked within a very short intervals. This must be secured. For this, the attack detection technique requires an adaptive learning classifier, with less computational complexity and an accurate decision making to stunt these attacks. Here, Fuzzy Q-Learning algorithm is used to detect the attack patterns. The FQL algorithm protects the wireless nodes within the network and target nodes from the attacks. The accuracy ...
Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed model's attack detection and defense accuracy yield a greater improvement than existing abovementioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate.
Malicious Attack Classification Based-on Continuous Hidden Markov Models in Wireless Sensor Networks
Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be victim of distributed attacks. Attackers can perform intrusions to breakdown the network during the routing process, intercept gathered data by dropping or re-sharing them. To avoid the increasing of security issues, many attack identification models were proposed in WSNs in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. In this work, a new method is introduced to analyze and classify WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security, it efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system which learn from routing dataset to identify network malicious entries. The proposed models is based on Hidden Markov Model (HMM), Gaussian Mixture Model (GMM) stoch...
Using Learned Data Patterns to Detect Malicious Nodes in Sensor Networks
Lecture Notes in Computer Science, 2008
As sensor network applications often involve remote, distributed monitoring of inaccessible and hostile locations, they are vulnerable to both physical and electronic security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. We consider sensor nodes organized in a hierarchy where the non-leaf nodes serve as the aggregators of the data values sensed at the leaf level and the Base Station corresponds to the root node of the hierarchy. To detect compromised nodes, we use neural network based learning techniques where the nets are used to predict the sensed data at any node given the data reported by its neighbors in the hierarchy. The differences between the predicted and the reported values is used to update the reputation of any given node. We compare a Q-learning schemes with the Beta reputation management approach for their responsiveness to compromised nodes. We evaluate the robustness of our detection schemes by varying the members of compromised nodes, patterns in sensed data, etc.
A Well Structured Rule through Reinforcement Learning for Wireless Sensor Networks Security
nguyendangbinh.org
Wireless sensor networks are increasingly becoming viable solutions to many challenging problems and will successively be deployed in many areas in the future. However, deploying new technology without security in mind has often proved to be unreasonably dangerous. In this paper a well structured rule using Reinforcement Learning (RL) is proposed. An agent interacts with the network environment and map the policy for the network security. This paper is organized into five Sections. In Section 1 basic introduction of the sensor network with its architecture and RL is discussed. Section 2 describes the requirement of sensor network security. In Section 3 security management based on policy, its architecture and policy language is discussed. Framework of well structured rule using reinforcement learning agent is proposed in Section 4. Finally conclusions are drawn in Section 5.
Reinforcement based Cognitive Algorithms to Detect Malicious Node in Wireless Networks
International Journal of Computer Applications, 2015
The growth of wireless communication technologies and its applications leads to many security issues. Malicious node detection is one among the major security issues. Adoption of cognition can detect and Prevent malicious activities in the wireless networks. To achieve cognition into wireless networks, we are using reinforcement learning techniques. By using the existing reinforcement techniques, we have proposed GreedyQ cognitive (GQC) and SoftSARSA cognitive (SSC) algorithms for malicious node detection and the performances among these algorithms are evaluated and the result shows SSC algorithm is best algorithm. The proposed algorithms perform better in malicious node detection as compared to the existing algorithms.
AIS for misbehavior detection in wireless sensor networks: Performance and design principles
2007
A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices (nodes) are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior.
Information Sciences, 2019
An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.
Intrusion Detection and Security Mechanisms for Wireless Sensor Networks
International Journal of Distributed Sensor Networks, 2014
Wireless sensor networks are multihop, self-organizing, selfhealing, and distributed in nature. One of their main features is their energy consumptions, so many efforts are focused on power saving techniques. Wireless sensor networks are gaining significant interest from academia and industry and the number of real deployments of wireless sensor networks (WSN) is increasing considerably in the last years. Their intrinsic characteristics make them very vulnerable to external intrusion. Thus, the security has become one of the main issues to study in WSNs. Their ad hoc network nature also increases their vulnerability and exposes sensor nodes to various kinds of security attacks. There is a clear need for new security techniques to guarantee the information transmitted through the WSN. Last research tendencies are focused on including security in the routing protocol, providing security for communication inside groups of nodes and when exchanging data between groups. One of the most efficient techniques to detect an intruder in the network is the use of traffic analysis for detecting anomalies and finding correlated events. Advanced security mechanisms and intrusion detection systems (IDSs) can play an important role in detecting and preventing security attacks in WSNs.
2024
Background and Objectives: Wireless sensor networks (WSNs) are ad-hoc technologies that have various applications in different industries such as in healthcare systems, environment and military surveillance, manufacturing, and IoT context in general. Expanding the scope of sensor network applications has led researchers to develop solutions to provide sustainable communications and networks for distributed environments, as well as how to secure these methods with limited resources. Methods: The lack of infrastructure space and the vulnerable nature of these networks make it difficult to design security models and algorithms for them. So, to run the sensor network in safe mode, any type of attack must be detected before any security breach is materialized. According to the importance of the network and also the nature of the sensor networks along with the critical challenge of energy consumption, solutions and defensive lines such as intrusion prevention and intrusion detection systems will be selected. Results: This paper surveys subjectively the intrusion and anomaly detection system in WSNs to determine potentials and challenges for further processing. Therefore, designing an efficient and optimal intrusion detection solution applicable to wireless sensor networks, IoT, and other ad-hoc networks has been a major challenge that will help the researcher to design or choose the best approach for their future research. Conclusion: This research also paves the way of interested researchers to find existing challenges and shortcomings for further processing.