A novel intrusion detection framework for wireless sensor networks

Abstract Vehicle cloud is a new idea that uses the benefits of wireless sensor networks (WSNs) and the concept of cloud computing to provide better services to the community. It is important to secure a sensor network to achieve better performance of the vehicle cloud. Wireless sensor networks are a soft target for intruders or adversaries to launch lethal attacks in its present configuration. In this paper, a novel intrusion detection framework is proposed for securing wireless sensor networks from routing attacks. The proposed system works in a distributed environment to detect intrusions by collaborating with the neighboring nodes. It works in two modes: online prevention allows safeguarding from those abnormal nodes that are already declared as malicious while offline detection finds those nodes that are being compromised by an adversary during the next epoch of time. Simulation results show that the proposed specification-based detection scheme performs extremely well and achieves high intrusion detection rate and low false positive rate

A Survey: Intrusion Detection System for Vehicular Ad-Hoc Networks (VANETs)

In recent years, the security issues on Vehicular ad hoc networks (VANETs) have become one of the primary concerns. Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles that influence the stability of communication. Furthermore, VANETs are vulnerable against attacks so this can directly lead to the corruption of networks and then possibly provoke big losses of time, money, and even lives. This paper presents a survey of VANETs attacks and solutions in carefully considering other similar works as well as updating new attacks and categorizing them into different classes. Keywords: Intrusion Detection System DOI: 10.7176/ISDE/11-4-02 Publication date:August 31st 202

A Study on Intrusion Detection System in Wireless Sensor Networks

The technology of Wireless Sensor Networks (WSNs) has become most significant in present day. WSNs are extensively used in applications like military, industry, health, smart homes and smart cities. All the applications of WSN require secure communication between the sensor nodes and the base station. Adversary compromises at the sensor nodes to introduce different attacks into WSN. Hence, suitable Intrusion Detection System (IDS) is essential in WSN to defend against the security attack. IDS approaches for WSN are classified based on the mechanism used to detect the attacks. In this paper, we present the taxonomy of security attacks, different IDS mechanisms for detecting attacks and performance metrics used to assess the IDS algorithm for WSNs. Future research directions on IDS in WSN are also discussed

Detecting Prominent Features and Classifying Network Traffic for Securing Internet of Things Based on Ensemble Methods

abstract: Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.Dissertation/ThesisMasters Thesis Computer Science 201

Security in Wireless Sensor Networks

Wireless sensor network (WSN) is an emerging important research area. The variety in and number of applications is growing in wireless sensor networks. These wireless sensor nodes are tiny devices with limited energy, memory, transmission range, and computational power. Because WSNs in general and in nature are unattended and physically reachable from the outside world, they could be vulnerable to physical attacks in the form of node capture or node destruction. These forms of attacks are hard to protect against and require intelligent prevention methods. It is necessary for WSNs to have security measures in place as to prevent an intruder from inserting compromised nodes in order to decimate or disturb the network performance. Intrusion detection in wireless sensor networks is a much needed security measure. In this thesis we present an intrusion detection framework for wireless sensor networks which does not require prior knowledge of network behavior or a learning period in order to establish this knowledge. We have taken a more practical approach and constructed this framework with small to middle-size networks in mind, like home or office networks. The proposed framework is also dynamic in nature as to cope with new and unknown attack types. This framework is intended to protect the network and ensure reliable and accurate aggregated sensor readings. Theoretical simulation results indicate that compromised nodes can be detected with high accuracy and low false alarm probability when as much as 25% compromised nodes is present in the network. Theoretical simulation results regarding data aggregation indicates that compromised nodes will be limited in their influence on the aggregated data even with as much as 40% compromised nodes in the network. We have only simulated the framework theoretically in a mathematics program and evaluated the theoretical properties of the algorithms. The results are promising and the framework should be simulated in a network simulator for further evaluation