A Survey on Wireless Sensor Network Security

Wireless sensor networks (WSNs) have recently attracted a lot of interest in the research community due their wide range of applications. Due to distributed nature of these networks and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their proper functioning. This problem is more critical if the network is deployed for some mission-critical applications such as in a tactical battlefield. Random failure of nodes is also very likely in real-life deployment scenarios. Due to resource constraints in the sensor nodes, traditional security mechanisms with large overhead of computation and communication are infeasible in WSNs. Security in sensor networks is, therefore, a particularly challenging task. This paper discusses the current state of the art in security mechanisms for WSNs. Various types of attacks are discussed and their countermeasures presented. A brief discussion on the future direction of research in WSN security is also included.Comment: 24 pages, 4 figures, 2 table

Primary User Emulation Attacks in Cognitive Radio - An Experimental Demonstration and Analysis

Cognitive radio networks rely on the ability to avoid primary users, owners of the frequency, and prevent collisions for effective communication to take place. Additional malicious secondary users, jammers, may use a primary user emulation attacks to take advantage of the secondary user\u27s ability to avoid primary users and cause excessive and unexpected disruptions to communications. Two jamming/anti-jamming methods are investigated on Ettus Labs USRP 2 radios. First, pseudo-random channel hopping schemes are implemented for jammers to seek-and-disrupt secondary users while secondary users apply similar schemes to avoid all primary user signatures. In the second method the jammer uses adversarial bandit algorithms to avoid channels already heavily disrupted from primary user communications and concentrate efforts on channels heavily populated by secondary user communications. In addition the secondary users apply similar methods to avoid channels heavily occupied by jammers and primary users. The performance of these users is compared with and without the algorithm through channel delay, impact of algorithm on probability density functions, and user collision rate. Conclusions on made on the effectiveness of each technique

Intrusion Detection System based on time related features and Machine Learning

The analysis of the behavior of network communications over time allows the extraction of statistical features capable of characterizing the network traffic flows. These features can be used to create an Intrusion Detection System (IDS) that can automatically classify network traffic. But introducing an IDS into a network changes the latency of its communications. From a different viewpoint it is possible to analyze the latencies of a network to try to identifying the presence or absence of the IDS. The proposed method can be used to extract a set of phisical or time related features that characterize the communication behavior of an Internet of Things (IoT) infrastructure. For example the number of packets sent every 5 minutes. Then these features can help identify anomalies or cyber attacks. For example a jamming of the radio channel. This method does not necessarily take into account the content of the network packet and therefore can also be used on encrypted connections where is impossible to carry out a Deep Packet Inspection (DPI) analysis