scholarly journals Anomaly Detection in Wireless Sensor Networks: A Proposed Framework

2020 ◽  
Vol 14 (10) ◽  
pp. 150
Author(s):  
Dina M. Ibrahim ◽  
Nada M. Alruhaily
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Research Problem ◽  
Sensor Nodes ◽  
Base Stations ◽  
Sensor Data ◽  
Wireless Sensor

With the rise of IOT devices and the systems connected to the internet, there was, accordingly, an ever-increasing number of network attacks (e.g. in DOS, DDOS attacks). A very significant research problem related to identifying Wireless Sensor Networks (WSN) attacks and the analysis of the sensor data is the detection of the relevant anomalies. In this paper, we propose a framework for intrusion detection system in WSN. The first two levels are located inside the WSN, one of them is between sensor nodes and the second is between the cluster heads. While the third level located on the cloud, and represented by the base stations. In the first level, which we called light mode, we simulated an intrusion traffic by generating data packets based on TCPDUMP data, which contain intrusion packets, our work, is done by using WSN technology. We used OPNET simulation for generating the traffic because it allows us to collect intrusion detection data in order to measure the network performance and efficiency of the simulated network scenarios. Finally, we report the experimental results by mimicking a Denial-of-Service (DOS) attack. <em> </em>

scholarly journals Intrusion detection using ant colony approach in wireless sensor networks

2018 ◽  
Vol 7 (4.5) ◽  
pp. 657
Author(s):  
Jayashree Agarkhed ◽  
Gauri Kalnoor
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Malicious Attacks ◽  
Multiple Sensing ◽  
Multi Agents

Design of an intrusion detection system in the sensor network to improve the behavior of the network is the major challenge is theVariety of intrusion detection mechanisms are being used now a days, to provide security in Wireless Sensor networks (WSN). Since WSN works with set of tiny nodes called as sensor nodes, there are high chances of intrusions for malicious attacks. WSN is deployed in medium open to many users wherever possible. A multiple sensing environment of WSN consists of sensors which acts as agents called as multi agents system for detecting an intruder. Ant colony is an effective approach where each agent communicate with each other for updating the information of intruder to the colony administration. The multi agents based system is best phenomenon suitable for optimization of ant colony. In this approach, the ants form a colony where it goes for search continuously until an intruder is found and once searched, it returns back with the best shortest path available with path traces stored in its database for its future reference. An optimized multi agent approach using ant colony is proposed for detection of lightweight intruders for WSN to protect against harmful malicious attacks.  

scholarly journals Intrusion Detection Algorithm for Packet Loss Minimization in Wireless Sensor Networks

2019 ◽  
Vol 8 (6) ◽  
pp. 69-74
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Packet Loss ◽  
Detection System ◽  
Detection Algorithm ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Abnormal Node

Wireless Sensor Network consists of a greater number of sensor nodes and recent advance is in wireless communications and it serves a backbone for controlling the real time applications. It consists of group of sensor nodes and that is sense the information from the event area and it is passes through the base station and which it reacts according to environment and to provide a large-scale monitoring and sensor measurement in a high temporal and the spatial resolution. The researcher uses a different algorithm in that they use a distributed energy fuzzy logic to reduce a packet loss. Wireless Sensor Networks are unprotected to many kinds of the security threats which can decrease the performance of network and cause the sensors to send wrong data to destination. The hostile node in the network is working as an attacker node and it takes all the information packets which is delivered through them. In this paper we propose an intrusion detection system algorithm against the packet dropping. Intrusion detection algorithm solves the problem by analyzing the network by detecting the abnormal node. Then the abnormal node is corrected into normal node with the help of intrusion detection algorithm.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

Sign in / Sign up

Export Citation Format

Share Document