Detection techniques of selective forwarding attacks in wireless sensor networks: a survey

The wireless sensor network has become a hot research area due its wide range of application in military and civilian domain, but as it uses wireless media for communication these are easily prone to security attacks. There are number of attacks on wireless sensor networks like black hole attack, sink hole attack, Sybil attack, selective forwarding attacks etc. in this paper we will concentrate on selective forwarding attacks In selective forwarding attacks, malicious nodes behave like normal nodes and selectively drop packets. The selection of dropping nodes may be random. Identifying such attacks is very difficult and sometimes impossible. In this paper we have listed up some detection techniques, which have been proposed by different researcher in recent years, there we also have tabular representation of qualitative analysis of detection techniquesComment: 6 Page

A Survey on Spoofing and Selective Forwarding Attacks on Zigbee based WSN

The main focus of WSN is to gather data from the physical world. It is often deployed for sensing, processing as well as disseminating information of the targeted physical environments. The main objective of the WSN is to collect data from the target environment using sensors as well as transmit those data to the desired place of choice. In order to achieve an efficient performance, WSN should have efficient as well as reliable networking protocols. The most popular technology behind WSN is Zigbee. In this paper a pilot study is done on important security issues on spoofing and selective forwarding attack on Zigbee based WSN. This paper identifies the security vulnerabilities of Zigbee network and gaps in the existing methodologies to address the security issues and will help the future researchers to narrow down their research in WSN.Keywords: Zigbee, WSN, Protocol Stack, Spoofing and Selective Forwarding

A Multi-Layer Approach For Detection Of Selective Forwarding Attacks In Wireless Sensor Networks

Wireless sensor networks (WSNs) are increasingly used due to their broad range of important applications in both military and civilian domains. Security is a major threat in WSNs. WSNs are prone to several types of security attacks. Sensor nodes have limited capacities and are deployed in dangerous locations; therefore, they are vulnerable to different types of attacks, including wormhole, sinkhole, and selective forwarding attacks. Security attacks are classified as data traffic and routing attacks. These security attacks could affect the most significant applications of WSNs, namely, military surveillance, traffic monitoring, and healthcare. Therefore, many approaches were suggested in literature to detect security attacks on the network layer in WSNs. The network layer is of paramount significance to the security of WSNs to prevent exploitation of their confidentiality, privacy, availability, integrity, and authenticity. Reliability, energy efficiency, and scalability are strong constraints on sensor nodes that affect the security of WSNs. Because sensor nodes have limited capabilities in most of these areas, selective forwarding attacks cannot be easily detected in networks. In this dissertation, an approach to selective forwarding detection (SFD) is suggested. The approach has three layers: MAC pool IDs, rule-based processing, and anomaly detection. It maintains the safety of data transmission between a source node and base station while detecting selective forwarding attacks. Furthermore, the approach is reliable, energy efficient, and scalable

Detecting Selective Forwarding Attacks In Wireless Sensor Networks

Wireless sensor networks are being used in a wide variety of applications ranging from home automation to military surveillance. Security of a sensor network must be ensured for proper functioning of the many applications that depend on them. Due to the low computational capabilities and resource constrained nature of sensor nodes, sensor networks are prone to many attacks like selective forwarding. In this thesis we propose a novel and low power consuming approach to accurately detect selective forwarding attacks and trace back the attacker using an acknowledgement based scheme. We also make use of a probability based metric to increase the accuracy of detection and to reduce the undetected attacker rate in the sensor network. We performed experiments on a network of real sensor motes and showed that our approach has higher accuracy of detection and lower communication overhead compared to previous work.Computer Science Departmen

A SOLUTION TO SELECTIVE FORWARD ATTACK IN WIRELESS SENSOR NETWORK

Purpose of Study: Wireless mesh network represents a solution to provide wireless connectivity. There are some attacks on wireless sensor networks like black hole attack, sinkhole attack, Sybil attack, selective forwarding, etc. In this paper, we will concentrate on a selective forwarding attack. Selective Forwarding Attack is one of the many security threats in wireless sensor networks that can degrade network performance. An adversary on the transmission path selectively drops the packet. The adversary same time transfers the packet, while on a few occasions it drops the packet. It is difficult to detect this type of attack since the packet loss may be due to unreliable wireless communication. The proposed scheme is based on the trust value of each node. During data transmission, a node selects a downstream node that has the highest trust value, which is updated dynamically based on the number of packets a node has forwarded and dropped. Methodology: A comparative methodology is used in all existing schemes. We compared our scheme with the existing scheme and found that the packet loss in the proposed scheme is much less than the existing scheme. Result: We showed that our scheme essentially detects malicious nodes for each possible scenario. Regarding communication overhead, our scheme is more efficient than typical multipath schemes. Also, by utilizing an existing routing protocol which is secure against sinkhole attacks, our scheme also provides security against sinkhole attacks

A REVIEW OF SELECTIVE FORWARDING ATTACKS IN WIRELESS SENSOR NETWORKS

ABSTRAC

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

LEDS - An innovative corridor of data security in WSN

Recently, WSNs have drawn a lot of attention due to their broad applications in both military and civilian domains. Data security is essential to the success of WSN applications, exclusively for those mission-critical applications working in unattended and even hostile environments which may be exposed to several attacks. This inspired the research on Data security for WSNs. Attacks due to node compromise include Denial of service (DoS) attacks such as selective forwarding attacks and report disruption attacks. Nearby many techniques have been proposed in the literature for data security. Hop-hop security works well when assuming a uniform wireless communication pattern and this security designs provides only hop-hop security. Node to sink communication is the dominant communication pattern in WSNs and hop-hop security design is not sufficient as it is exposed to several attacks due to node compromise. Location aware end-end data security (LEDS) provides end-end security. DOI: 10.17762/ijritcc2321-8169.15025

RPL routing protocol performance under sinkhole and selective forwarding attack: experimental and simulated evaluation

To make possible dream of connecting 30 billion smart devices assessable from anywhere, anytime and to fuel the engine growth of Internet of things (IoT) both in terms of physical and virtual things, Internet Engineering Task Force (IETF) came up with a concept of 6LoWPAN possessing characteristics like low power, bandwidth and cost. To bridge the routing gap and to collaborate between low power private area network and the outside world, IETF ROLL group proposed IPv6 based lightweight standard RPL (Routing protocol for low power and lossy networks). Due to large chunks of random data generated on daily basis security either externally or internally always remain bigger threat which may lead to devastation and eventually degrades the quality of service parameters affecting network resources. This paper evaluates and compare the effect of internal attacks like sinkhole and selective forwarding attacks on routing protocol for low power and lossy network topology. Widely known IoT operating system Contiki and Cooja as the simulator are used to analyse different consequences on low power and lossy network