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Security is always a major concern in wireless sensor networks (WSNs). Identity based attacks such as spoofing and sybil not only compromise the network but also slow down its performance. This paper proposes a low complexity sybil attack detection scheme, that is, based on signed response (SRES) authentication mechanism developed for Global System...
Citations
... The scheme also avoids premature convergence with better convergence and higher accuracy. Saud Khan and Khan [28] presented Sybil attack detection using signed response authentication techniques for global mobile communication systems. They also discussed the probabilistic model to analyze Sybil attack detection performance. ...
Security enhancement in wireless sensor networks (WSNs) is significant in different applications. The advancement of routing attack localization is a crucial security research scenario. Various routing attacks degrade the network performance by injecting malicious nodes into wireless sensor networks. Sybil attacks are the most prominent ones generating false nodes similar to the station node. This paper proposed detection and localization against multiple attacks using security localization based on an optimized multilayer perceptron artificial neural network (MLPANN). The proposed scheme has two major part localization techniques and machine learning techniques for detection and localization WSN DoS attacks. The proposed system is implemented using MATLAB simulation and processed with the IBM SPSS toolbox and Python. The dataset is classified into training and testing using the multilayer perceptron artificial neural network to detect ten classes of attacks, including denial-of-service (DoS) attacks. Using the UNSW-NB, WSN-DS, NSL-KDD, and CICIDS2018 benchmark datasets, the results reveal that the suggested system improved with an average detection accuracy of 100%, 99.65%, 98.95%, and 99.83% for various DoS attacks. In terms of localization precision, recall, accuracy, and f-score, the suggested system outperforms state-of-the-art alternatives. Finally, simulations are done to assess how well the suggested method for detecting and localizing harmful nodes performs in terms of security. This method provides a close approximation of the unknown node position with low localization error. The simulation findings show that the proposed system is effective for the detection and secure localization of malicious attacks for scalable and hierarchically distributed wireless sensor networks. This achieved a maximum localization error of 0.49% and average localization accuracy of 99.51% using a secure and scalable design and planning approach.
... A review of various techniques designed for discovery and mitigation of Sybil attack in the network was analyzed in [9]. A low complexity Sybil attack discovery scheme was intended in [10] with help of signed response (SRES) authentication mechanism. The intrusion detection performance was poor. ...
Detection and isolation of Sybil and wormhole attack nodes in healthcare WSN is a significant problem to be resolved. Few research works have been designed to identify Sybil and wormhole attack nodes in the network. However, the detection performance of Sybil and wormhole attack nodes was not effectual as the false alarm rate was higher. In order to overcome such limitations, Delta Ruled First Order Iterative Deep Learning based Intrusion Detection (DRFOIDL-ID) Technique is proposed. The DRFOIDL-ID Technique includes two main phase namely attack detection and isolation. The DRFOIDL-ID Technique constructs Delta Ruled First Order Iterative Deep Learning in attack detection phase with aim of detecting the occurrence of Sybil and wormhole attacks in healthcare WSN. After detecting the attack nodes, DRFOIDL-ID Technique carried outs isolation process with the objective of increasing the routing performance. During the isolation phase, DRFOIDL-ID Technique keep always the identified Sybil and wormhole attack nodes through transmitting the isolation messages to all sensor nodes in healthcare WSN. Hence, DRFOIDL-ID Technique improves the routing performance with lower packet loss rate. The DRFOIDL-ID Technique conducts the simulation process using factors such as attack detection rate, attack detection time, false alarm rate and packet loss rate with respect to a diverse number of sensor nodes and data packets. The simulation result proves that the DRFOIDL-ID Technique is able to improve the attack detection rate and also reduces the attack detection time as compared to state-of-the-art works.
... Let E i be the preliminary energy of a nodule After the time period t, the energy spent by the nodule (E (t)) is provided by means of succeeding equation [4] E(t) = n tx * + n rx * (5) where n tx and n rx are the number of data packages transferred and obtained by the nodule after time t. ...
Since Mobile Ad hoc Network (MANET) has distributed network structure using wireless links, designing efficient security applications has become a critical need. Selfish nodes are nodes that refuse to forward the data from other nodes. The existence of selfish nodes will disturb the normal process of the network, and reduce the network performance. Intrusion Detection System (IDS) is a scheme for detecting any misbehaviors in the network operation by monitoring the traffic flow. Each monitoring node need to execute the IDS module. The common problems encountered by the monitoring nodes are energy depletion, link disconnection, mobility and coverage. Hence the selection of monitoring nodes plays an important role in IDS. This paper develops a technique for deployment and selection of monitoring nodes for detection of selfish attacks. In this technique, the whole network is virtually divided in smaller grid like zones. In each grid, the nodes with higher stability and better coverage are assigned a reward value. A cost metric is derived in terms of energy consumption and computational delay. Then the nodes with minimum cost and high reward are selected as monitoring nodes. By simulation results, it is shown that the proposed technique has reduced detection delay, energy consumption and detection overhead.
... The performance of the game theory-based security algorithm is significantly affected by the network environment. Reference [24] proposes a low-complexity sybil attack detection mechanism which can be implemented in both hierarchical and centralized wireless sensor networks. Extensive simulations prove that the proposed scheme is able to detect sybil attacks with higher probability and lesser computational cost and power consumption as compared to existing schemes. ...
For the severe impact of limited energy and network attacks caused by open transmission channels on data transmission, this paper presents a low-power and secure multihop routing mechanism based on the Markov state transition theory. The random selection of transmission paths enables the network to resist typical attacks such as interference and interception, thus ensuring the security of data transmission. Meanwhile, the proposed algorithm can reduce the overall energy consumption of the network and balance the load according to the residual energy of each path. Simulation results prove that the routing mechanism proposed in this paper can improve the energy efficiency and the security of the wireless ad hoc network.
... Although various metrics to study a Sybil attack are present in the literature ( [37,38]), we mainly focused on the following: ...
A Sybil attack is one of the main challenges to be addressed when securing peer-to-peer networks, especially those based on Distributed Hash Tables (DHTs). Tampering routing tables by means of multiple fake identities can make routing, storing, and retrieving operations significantly more difficult and time-consuming. Countermeasures based on trust and reputation have already proven to be effective in some contexts, but one variant of the Sybil attack, the Spartacus attack, is emerging as a new threat and its effects are even riskier and more difficult to stymie. In this paper, we first improve a well-known and deployed DHT (Chord) through a solution mixing trust with standard operations, for facing a Sybil attack affecting either routing or storage and retrieval operations. This is done by maintaining the least possible overhead for peers. Moreover, we extend the solution we propose in order for it to be resilient also against a Spartacus attack, both for an iterative and for a recursive lookup procedure. Finally, we validate our findings by showing that the proposed techniques outperform other trust-based solutions already known in the literature as well.
... Therefore, Sybil users can attack the systems while the formulas still try to accumulate enough information to detect Sybil users. Probabilistic models to calculate the success probability of Sybil attack is also implemented in various networks, i.e. file sharing networks [7], wireless sensor networks [8], and networks using distributed hash tables [9]. However, there is no voting mechanism proposed in these letters. ...
This paper proposes to derive the success probability of Sybil attack in online social networks with the multiplechoice majority voting. The resultant SybilVote formulas produce outputs that are consistent with the Monte-Carlo simulation and more accurate than the existing formula based on the multinomial distribution tail estimate. The computational complexity of SybilVote exact formulas is O � (n + S)k where n; k; S are the number of real users, choices, and Sybil users, respectively. The accurate approximation formula of Sybilvote is also presented with O (k) complexity by using a normal distribution approximation available when n; k are large in a large-population user voting condition. Finally, effects of parameters on the success probability of Sybil attack have been investigated to highlight usefulness of the formulas.
Wireless sensor networks (WSNs) consist of hundreds, or thousands of sensor nodes distributed over a wide area and used as the Internet of Things (IoT) devices to benefit many home users and autonomous systems industries. With many users adopting WSN-based IoT technology, ensuring that the sensor’s information is protected from attacks is essential. Many attacks interrupt WSNs, such as Quality of Service (QoS) attacks, malicious nodes, and routing attacks. To combat these attacks, especially on the routing attacks, we need to detect the attacker nodes and prevent them from any access to WSN. Although some survey studies on routing attacks have been published, a lack of systematic studies on detecting WSN routing attacks can be seen in the literature. This study enhances the topic with a taxonomy of current and emerging detection techniques for routing attacks in wireless sensor networks to improve QoS. This article uses a PRISMA flow diagram for a systematic review of 87 articles from 2016 to 2022 based on eight routing attacks: wormhole, sybil, Grayhole/selective forwarding, blackhole, sinkhole, replay, spoofing, and hello flood attacks. The review also includes an evaluation of the metrics and criteria used to evaluate performance. Researchers can use this article to fill in any information gaps within the WSN routing attack detection domain.
The rapid developments in wireless multimedia sensor networks (WMSNs) have
increased the demand for an efficient method of safeguarding multimedia data
from attackers. As data are transmitted over a wireless medium, the
authentication process needs to be provided with some efficient detection
and prevention methods. The Sybil attack is one of the most common and
involves replicating the identity of an original node in the network and
behaving like a true node in order to retrieve/destroy information using
this fake identity. An efficient enhanced random password comparison
technique is proposed to detect and prevent Sybil attacks. The results of
simulations indicate that the proposed method detects this type of attack
more efficiently than existing methods. In addition to early detection, our
application increases the throughput and reduces the average delay with an
enhanced true detection rate. The identification of this malicious activity
in its initial phases increases the efficiency of the system in terms of the
data transmission process.
