Attacks In Wireless Sensor Networks Research Articles

Hybrid Encryption Algorithm to Detect Clone Node Attack in Wireless Sensor Network

Wireless sensor network (WSN) is the collection of sensor nodes which are distributed in a specific environment. WSN is resource-controlled network which is vulnerable to physical attacks i.e. clone node attack. An attacker acquires information from captured node and then creates the cloned node in the WSN. The main purpose of the cloned nodes is to control the entire network and increase the invasion in the sensor network. When communicating from legitimate node to other node, the message is not secure, so an attacker can modify it. Current asymmetric encryption methods and symmetric encryption methods can provide a level of security, but there are some limitations in it. The hybrid encryption method combining the asymmetric and symmetric methods which will offers high security with minimal key management. We have proposed a new hybrid secure encryption method in this paper which combines the asymmetric and symmetric cryptography method. The proposed efficient algorithm to protect communication from clone node attacks in wireless sensor network. The hybrid algorithm uses advanced cryptographic standards AES, BSDN and Dual RSA encryption techniques and MD5 hash functions. In this algorithm, message is divided into three parts and BSDN, AES and Dual RSA encryption are parallel implemented to the sub-message. The proposed algorithm is analyzed based on parameters such as encryption, decryption and execution time. The result shows that proposed encryption algorithm works better in terms of encryption and decryption time.

Impact Analysis of Network Layer Attacks in Real-Time Wireless Sensor Network Testbed

With the rapid increase in the demand for Wireless Sensor Network (WSN) applications. The intrusive activities are also raised. To save these networks from the intruders it is required to understand the implications of any malicious act. Most of the researchers have utilized simulated software to understand the impact of such intrusions, however, real network conditions vary from the simulated environment. Therefore, the current work focuses on analyzing the impact of network layer attacks in real-time WSN testbed. The contributions of this work are threefold. Firstly, it presents the deployment of a real-time experimental testbed using standardized sensor devices in a multi-hop topological arrangement. Secondly, it provides the implementation details of seven network layer attacks: Blackhole (BH), Dropping Node (DN), Drop Route Request (DRREQ), Drop Route Reply (DRREP), Drop Route Error (DRERR), Grayhole (GH) and Sinkhole (SH) in a single testbed. Finally, the testbed performance with and without each attack is monitored and compared in terms of network performance metrics to understand the attacks’ impact. This work will be helpful for the research community for proposing efficient attack detection and prevention solutions for these networks.

A Cluster Based Approach for Detection and Protection of Wormhole Attack in Wireless Sensor Network

Wireless Sensor Network (WSN) technology is progressing at a rapid pace and its demand is also increasing massively due to its cost-effective nature and less-challenging installation demands. However, due to its vulnerable wireless environment, WSNs are prone to various types of severely damaging attacks like wormhole, sinkhole, Denial of Service (DoS) etcetera. Therefore, it requires to be more secure and confidential to maintain the productive flow of communication between the nodes. One of the greatest threats which if placed strategically can seriously impair the network is the wormhole attack. In this paper, we proposed a cluster based technique to detect and protect WSN from wormhole attacks. The entire network is divided into clusters eventually at the end and each cluster is governed by a cluster head (CH). Based on Round Trip Time and hop count the security of the network has enhanced and nullified the wormhole attack in the network. A significant improvement in throughput, PDR, Delay has been observed and reported with proposed scheme.

Internet

Nowadays Internet of Things (IoT) is emerging and effective technology along with Wireless Sensor Networks (WSNs) in a few constant applications in which the human intervention significantly reduced along with better human life. In IoT enabled WSNs, the sensor nodes used to assemble the fragile data and communicate towards the sink hub and actuators for automotive remote monitoring process. However as the WSNs operating at free frequency band, it is powerless against different attacks at various layers of WSNs protocol stack in which attackers may try to hack and compromise the user’s personal information. There are different types of attacks in WSNs and several research works conducted to protect from these attacks in WSNs. Majority of security methods proposed are based layered technique. However, the layer approach is not enough to protect the WSNs effectively as the many attackers used the cross-layer information to perform the attacks. This paper presents the study over the layered security solutions and their research problems at first to justify the importance of cross-layer solutions. The review of different ways of designing the cross-layer security techniques for WSNs with their behaviour presented as well. The challenges of IoT enabled WSNs single layered security solutions presented and then the various cross-layer solutions reviewed in this paper. The comparative study of different cross-layer techniques to demonstrate the layers and their parameters involved to detect of security threats. The outcome of this review work is the research challenges noticed from the present cross-layer solutions.

Threshold Based Algorithm for the Detection of DDOS Attack in Wireless Sensor Networks

The self-configuring type of network in which the sensor node are deployed in such a manner that they can join or leave the network when they want is known as wireless sensor network. The nodes start communicating with each other in order to transmit important information within the network. As this type of network is decentralized in nature there are numerous malicious nodes which might enter the network. There are so many attacks possible on WSN, in Distributed Denial of Service (DDOS) attacks, malicious nodes adapts many attacks such as flooding attack, black hole attack and warm hole attack, to halt the overall functioning of network. The risks are even more when we talk about military and industrial applications. The DDoS is an active type of attack. When the DDoS attack occurs in the network, it minimizes the lifetime of the network and also increases the overall energy consumption of the network. In order to detect the malicious nodes from the network which cause the DDoS attack, a novel approach is to be proposed in this research work.

Exploration of Detection Method of Clone Attack in Wireless Sensor Network

Wireless sensor networks have a lot of sensor nodes that are small, cheap and resource-constraints, but are often used to perform various monitoring operations in unmanned and demanding environments. Networks are vulnerable to different application-based and application-independent attacks. We examine node replication attacks, which are typical threats in the sensor network. In this attack, the enemy generates its own sensor node using stealing sensor from network. The attacker physically occupies the node, takes his secret credentials, and duplicates a large number of nodes with some controlled counterparts. The defense against clone node attacks has become an important research element in the safety of the sensor network. In this study, we classify and examine the different proposals in each category. We also compare the memory and communication cost of different clone node detection approach.

A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor Networks

It is well known that the safety, reliability and energy efficiencies are the prime concerns while designing and deploying wireless sensor networks. Therefore, unattained features and inability to recharge the nodes makes wireless sensor networks more susceptible to attack. To successfully deploy efficient usage of power resources, sensor networks from time to time keep nodes to sleep. They awake when some activity is noticed. MAC protocols playa very crucial role to save the power consumption during communication. Attacker is not adhering the communication protocols by taking the advantage of unattended scenario. The communication protocols are designed in such a manner that they are assuring the optimal consumption of battery or other power sources. By indulging the nodes in unnecessary communication adversary applies the repudiation of sleep attack by applying repudiation of service attack. This work implements the effect of denial of service attack which leads to the denial of sleep attack in wireless sensor networks using support vector machine learning. To achieve better result, model is developed and tested with the help of various kernel functions (linear, sigmoidal and redial functions). After the intensive execution of experiments, final outcomes have been drawn. On the basis of achieved results and validation of model we can conclude that the developed system performs outstanding throughput for detecting denial of sleep strike attack in WSN.

An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.

Active Defense Strategy against Jamming Attack in Wireless Sensor Networks

Active Defense Strategy against Jamming Attack in Wireless Sensor Networks

Identity based Scheme A gainst Sybil Attacks in Wireless Sensor Networks

Wireless sensor networks are insecure against various security attacks. One such harmful, yet easy to promote an attack is the sybil attack which creates multiple identities to achieve access to the wireless sensor networks. A new identity based scheme to provide security against Sybil attacks is proposed in this paper. It detects as well as broadcast information about the attackers to all the nearby sensor nodes. Expensive MapToPoint and pairing operations are not used in this scheme to reduce overheads. It also provides other essential security features. Aggregate-verification to verify several messages at the same time is also proposed in this paper. The performance of the proposed scheme is compared with other recent schemes and the results show that the proposed scheme has reduced amount of overheads and better performance.

Detection of Blackhole Attacks in Wireless Sensor Networks

Information storage and security is one of key areas where much research is been done in this digital world where we communicate the data over using third party devices such as cloud by using smart devices such as mobiles so the security is an quite challenging factor where we access our data across the globe and with the social media coming into factor for the storage and accessibility of the data so there are many risk factors coming into process so we need to implement a smart and secure system for the authentication threats so here in this paper we implement a smart system in which face recognition aunthication system is implement between the cloud and mobile activity which give more security in terms of data storage and communication and then we evaluate using different graphs and also analyses the attacksThe Remote Sensor Networks (WSN) are subjected to different assaults in which Blackhole a sort of Denial of Service attack(DoS) assaults is exceptionally hard to distinguish and secure. Inblackholeattack,theviolater catches and reprograms lot of hubs in system to hinder bundles they get as opposed to sending them towards the base station.The data ensures the malicious node and when it enters the zone it will be known and cannot be allowed into the field and very less output may occur. The lesser amount measure of work is improved the situation location and anticipation of blackhole assault in the WSN making its recognition and aversion extremely critical according to organize perfomance is assured.In our concept at first the effect of blackhole assault to be estimated on the system limitations pursued by proposition of a publication procedure for discovery & avoidance of blackhole assault in WSN.

Security Issues and Various Attacks in Wireless Sensor Network: A Survey

Security Issues and Various Attacks in Wireless Sensor Network: A Survey

Advanced Extremely Efficient Detection of Replica Nodes in Mobile Wireless Sensor Networks

Today, wireless sensor networks (WSNs) are widely used in many applications including the environment, military, and explorations. One of the most dangerous attacks against these networks is node replication. In this attack, the adversary captures a legal node of the network, generates several copies of the node (called, replica nodes) and injects them in the network. Various algorithms have been proposed to handle replica nodes in stationary and mobile WSNs. One of the most well-known algorithms to handle this attack in mobile WSNs is eXtremely Efficient Detection (XED). The main idea of XED is to generate and exchange random numbers among neighboring nodes. The XED has some drawbacks including high communication and memory overheads and low speed in the detection of replica nodes. In this paper, an algorithm is presented to improve XED. The proposed algorithm is called Advanced XED (AXED) in which each node observes a few numbers of nodes and whenever two nodes meet, a new random number is generated and exchanged. The efficiency of the proposed algorithm is evaluated in terms of the memory and communication overheads and its results are compared with existing algorithms. The comparison results show that the proposed algorithm imposes lower overheads to the nodes. In addition, the proposed algorithm is simulated and the simulation results show that the proposed algorithm is able to detect replica nodes faster than XED.

Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)

Wireless sensor network (WSN) is a group of a huge number of low price, low control, and self-organizing specialized sensor nodes. WSN is very much vulnerable to different types of physical attacks due to limited resource capacity and screened to external atmosphere for circulating data. The node capture attack is one of the major attacks in WSN in which the intruder physically captures the node and remove the secret information from the node’s memory. We propose a Fruit Fly Optimization Algorithm (FFOA) that is based on multiple objectives node capture attack algorithm which consists of several objectives: maximum node contribution, maximum key contribution, and least resource expenses to discover optimal nodes. It will influence an inclusive tool to demolish maximum part of the network along with effective cost and maximum attacking efficiency. The simulation result illustrates that FFOA obtains a maximum fraction of compromised traffic, lower attacking rounds, and lower energy cost as compared with Genetic Algorithm (GA) and other node capture attack algorithms. Therefore, FFOA gives maximum attacking efficiency than GA and other algorithms by capturing minimum nodes that compromise the whole network.

Mitigation of Selective Forwarding attacks in Wireless Sensor Network

Security issue in Wireless Sensor Networks (WSNs) is a major problem while dealing with WSNs. Therefore, WSNs are susceptible to various kinds of safety assaults. The restricted capability of sensor nodes is one reason for attacks in sensor networks. In WSNs, on the network layer, there are different kinds of safety attack detection methods. There are also many severe limitations in sensor nodes such as energy efficiency, reliability, scalability that affect WSN safety. As sensor nodes have restricted ability for the majority of the limitations, a selective forwarding attack in the networks is hard to identify. In selective forwarding attack, malicious nodes function as a ordinary nodes. However, it tries to find and crash messages prior to forwarding the packet to further nodes. For keeping this sort of attack aside from networks, we suggest a multi-layer strategy, Selective Forwarding Detection (SFD) that maintains the safe transmission of information among sensor nodes at the same time as detecting the selective forwarding attack. In addition, energy efficiency, reliability and scalability are part of the approach.

A Novel Prevention Mechanism for Replay Attack in Distance Vector - HOP Localization Scheme

Securing the process of node localization in Wireless Sensor Network (WSN) is an important area of research. In this paper, we focus on securing Distance Vector (DV)-Hop localization scheme from Replay Attack. The DV-Hop localization algorithm is a limit free hop based reference Wireless Sensor Network architecture. It faces a couple of issues like a route discovery mechanism and threat prevention issues. This paper focuses on the establishment of a prevention mechanism for DV-Hop scheme against a Replay attack. The proposed architecture uses an Artificial Bee Colony (ABC) and Neural Network in order to prevent the attack. To prove the efficacy of the proposed scheme, the network scenario without replay attack, with replay attack and with attack after prevention mechanism is considered. The simulations are conducted for 200 iterations using MATLAB. The evaluation is done on the basis of parameters like localization error and transmission loss. Localization error of the proposed framework is similar to localization error without an attack which is about 0.57, and the maximum localized error is about 0.92. The maximum transmission loss without attack and after prevention is very close and varies by only 2 %, which is very good. The experimental results reveal that the proposed technique successfully prevents replay attack in WSN

Detecting Denial of Service Attack in Wireless Sensor Network using Energy Efficient Extreme Learning Neural Network (EEELNN)

One of the effective communication technology is wireless sensor network technology which helps to monitor the surrounding information by sensed nodes. The effective utilization of sensed nodes is utilized in different applications such as military, health information, environmental monitoring, disaster relief and target analyze. The application requires the collection of information which may be collected from one location and transferred to the other location for making their process so easier. During the information transformation process, the network may affect by several intermediate attack, in which denial of service is one of the serious attack because it affects the entire network resources such as network energy, power, bandwidth. The unavailability of the resources reduces the entire sensor network performance. For managing the attack related issues, in this paper introduces the Energy Efficient Extreme Learning Neural Network (EEELNN) approach for overcoming the attack related issues. Initially the network transmitted zone is computed along with energy, power, bandwidth, neighboring node information and lifetime for eliminating the attack in sensor network. The computed information is processed and trained by extreme learning neural network that successfully predict the attack related data, node and network zone with effective manner that leads to improve the overall network performance. At last system efficiency is evaluated using simulation results such as detection rate, classification accuracy, false alarm rate and detection time.

A light-weight solution for blackhole attacks in wireless sensor networks

A light-weight solution for blackhole attacks in wireless sensor networks

Artificial bee colony based sinkhole detection in wireless sensor networks

Sinkhole attack in wireless sensor networks (WSN) is most vulnerable attack in WSN that prevents the base station from gathering complete and unmodified data from its origin. A simple authentication mechanism is not adequate to prevent WSN from sinkhole attacks as signed routing can also be easily done by compromised nodes. Hence in this paper we addressed the problem sinkhole attack detection in WSN using swarm-based algorithm namely artificial bee colony algorithm. This algorithm finds the compromised node by comparing the node ID’s defined in the rule set. ABC reduces the overall time complexity taken to find the compromised node which turns to reduces the packet loss percentile and increases the packet delivery ratio. The performance of the proposed algorithm is evaluated and compared with the existing methodologies. The results show that the proposed algorithm outperforms the existing methodologies in terms of packet loss, packet delivery ratio and energy consumption.

Intelligent temporal classification and fuzzy rough set-based feature selection algorithm for intrusion detection system in WSNs

At present, Internet-based information processing systems are challenged by different kinds of threats, which lead to various types of damages that in turn result in significant loss of information in Wireless Sensor Networks (WSNs). Moreover, the stream of data communication through WSNs is continuously increasing in volume. Such huge amounts of data are targeted by attackers frequently, and thus it is crucial to provide high-level security to ensure safe and effective communication of information through the Internet. In order to secure data communication over the Internet through WSNs, Intrusion Detection Systems (IDSs) must be provided as an essential component in networks, databases and cloud systems. Over the previous decade, various approaches were utilized for developing IDSs. Unfortunately, however,none of these systems are completely flawless due to uncertainty surrounding network traffic created by normal users and attackers. Hence, the need to develop efficient IDSs has increased continuously. The present study proposes an adaptive IDS based on Fuzzy Rough sets for attribute selection and Allen’s interval algebra, which is applied on network trace datasets in order to select a huge number of attack data for effective prediction of attacks in WSNs. In addition, a fuzzy and rough set based nearest neighborhood algorithm (FRNN) is proposed in this article for effective classification of network trace dataset. This model uses a biased dataset that has 50:50 normal and attack data as opposed to the conventional datasets that have 80:20 normal and attack data. The efficiency of the proposed IDS is increased due to the use of biased data. The combination of feature selection, temporal-based dataset selection, and classification using a biased dataset reduces the false alarm rate and increases detection accuracy.