Wireless sensor network (WSN) is an indispensible part of Internet of Things that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology, and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly toward security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this paper field based on our thorough survey and conclude this paper with possible future research directions.

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Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

As a typical application of Internet of Things (IoT) in the field of transportation, Internet of Vehicles (IoV) aims at achieving an integrated intelligent transportation system to enhance traffics efficiency, avoid accidents, ensure road safety, and improve driving experiences by using new IoT technologies. Different from other Internet, it is characterized by dynamic topological structures, huge network scale, non-uniform distribution of nodes, and mobile limitation. Due to these characteristics, IoV systems face various types of attacks, such as authentication and identification attacks, availability attacks, confidentiality attacks, routing attacks, data authenticity attacks, etc., which result in several challenging requirements in security and privacy. Many security scientists made numerous efforts to ensure the security and privacy for the Internet of Vehicles in recent years. This paper aims to review the advances on issues of security and privacy in IoV, including security and privacy requirements, attack types, and the relevant solutions, and discuss challenges and future trends in this area.

Attacks and countermeasures in the internet of vehicles

In wormhole attacks, attackers create a low-latency link between two points in the network. This can be achieved by either compromising two or more sensor nodes of the network or adding a new set of mali-cious nodes to the network. Once the link is established, the attacker collects data packets on one end of the link, sends the data packets using the low-latency link and replays them at the other end. Wormhole attacks result in alterations in network data flow thereby deceiving the base station. Although implementing a wormhole attack is relatively simple, detecting it is not a trivial task as the replayed information is usually valid. This paper focuses on wormhole attacks and presents the state-of-the-art in wormhole attack detection in wireless sensor networks. The existing wormhole detection protocols are presented in detail and, based on the existing research, the open research areas and future research directions in wormhole attack detection are provided.

A Survey of Wormhole-based Attacks and their Countermeasures in Wireless Sensor Networks

One of the major reasons to address the security aspects in MANETS is the usage of wireless transmission medium, which is highly susceptible or vulnerable to attacks. This paper proposes a way to identify parallelly different types of attacks in MANETS. This approach is highly secure as it essentially concentrates on identifying misbehaving links, number of significant packets dropped and malicious nodes parallelly. This paper shows the implementation of identification and prevention of malicious nodes launching packet dropping and message tampering attacks, using a semantic security mechanism. This security scheme is highly impossible to break, thereby making it a highly secured approach. The evaluation results demonstrate that the approach effectively detects and prevents such nodes and links in networking sessions.

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A Highly Secured Approach against Attacks in MANETS

Underwater sensor networks (UWSNs) can be deployed for sensing the environment in oceanographic columns and other water bodies in which they are deployed. The peculiar characteristic of the underwater medium, coupled with the queer nature of the sound waves in water, poses an enigmatic problem for UWSN researchers. In this study, the authors focus on the problem of UWSN jamming, which is a popular type of denial-of-service attack. The existing jamming detection solutions for sensor networks are primarily targeted towards the terrestrial ones. In this work, the authors study the unique characteristics of jamming in UWSN, and propose a protocol, known as underwater jamming detection protocol (UWJDP), to detect and mitigate jamming in underwater environments. The results show that if the packet delivery ratio (PDR) is less than or equal to 0.8, the authors have the maximum probability of detecting jamming. The jamming detection ratio is around 2-11- more for the said PDR.

Jamming in underwater sensor networks: detection and mitigation

The nature of wireless ad hoc and sensor networks make them very attractive to attackers. One of the most popular and serious attacks in wireless ad hoc networks is wormhole attack and most proposed protocols to defend against this attack used positioning devices, synchronized clocks, or directional antennas. This paper analyzes the nature of wormhole attack and existing methods of defending mechanism and then proposes round trip time (RTT) and neighbor numbers based wormhole detection mechanism. The consideration of proposed mechanism is the RTT between two successive nodes and those nodes’ neighbor number which is needed to compare those values of other successive nodes. The identification of wormhole attacks is based on the two faces. The first consideration is that the transmission time between two wormhole attack affected nodes is considerable higher than that between two normal neighbor nodes. The second detection mechanism is based on the fact that by introducing new links into the network, the adversary increases the number of neighbors of the nodes within its radius. This system does not require any specific hardware, has good performance and little overhead and also does not consume extra energy. The proposed system is designed in ad hoc on-demand distance vector (AODV) routing protocol and analysis and simulations of the proposed system are performed in network simulator (ns-2). Keywords—AODV, Wormhole attacks, Wireless ad hoc and

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Wormhole Attack Detection in Wireless Sensor Networks

Education plays a vital role in all areas of society and is responsible for developing civilisation. This research is carried out to determine the features related to the high dropout rate among primary education students in Rakhine State using feature selection methods in machine learning. Analysis of correlations between dependent factors and the dropout rate The purpose of this research article is (1) to identify factors that affect the dropout rate of primary education students in Rakhine State, (2) to identify which factor has the most significant influence on student dropout rates, and (3) to find out the relationship between selected features. In statistical analysis, descriptive and inferential statistics were applied to analyse the data. In the hypothesis testing, nine factors comprise two exciting variables. We calculated the Dropout Rate from the hypothesis testing results of the dependent factor pairs. The study found that the increasing dropout rate of students has the most significant influence on multi-variables.

Zaw Tun

Papers

Wormhole Attack Detection in Wireless Sensor Networks

Student Dropout Rate Analysis using Education Data of Rakhine State