Selfish Attacks Research Articles

Selfish attack detection and response using cooperative backoff adjustment in wireless sensor networks

In recent times, the proliferation of intelligent internet of things (IoT) and wireless sensor network (WSN) technologies has significantly enhanced both daily life and industrial operations. Despite their widespread application, these networks face challenges with resource monopolization by selfish nodes, which detrimentally impacts network efficiency. This study introduces a novel detection and response strategy for selfish nodes, utilizing backoff adjustment mechanisms to reduce throughput deterioration. The effectiveness of this approach was rigorously evaluated using the NetSim simulator, focusing on the impact of backoff adjustment on network throughput. Simulation results indicate that the implementation of this method in the presence of selfish nodes yields a noteworthy improvement in network throughput, with approximately more than twice. This advancement holds promise for enhancing the robustness and efficiency of IoT and WSN technologies against disruptive selfish node behaviors.

CR-IOT based selfish attack detection via RSSI-LSTM

Internet of Things-based technologies rely on cooperating between nodes to increase network capacity. A selfish or malicious node is a node that does not cooperate with other nodes in the network. Selfish nodes raise their interests by using facilities provided by other nodes. Selfish cognitive radio attacks significantly degrade the performance of Cognitive Radio networks, which pose a serious security threat and malicious nodes often abuse the network and damage its facilities. To overcome this issue, a novel Cognitive radio on-demand distance vector (CRODV) is proposed. The proposed CRODV technique utilize (Received Signal Strength Indicator) RSSI-based Long Short-Term Memory (LSTM) to detect attacks. This is accomplished by counting the number of active channels to and from the test node in a specific area. If the values don't match, the test node in question is self-centered or introverted. If both values are the same, the test node is normal. Simulations were carried out in MATLAB to test the proposed CRODV. A comparison is made between the suggested framework and existing methods in terms of packet delivery ratio, route dis-connectivity ratio, throughput, detection time, and end-to-end delay. The experimental results demonstrates that the CRODV technique reduce the end-to-end delay by 27.4%, 33.24%, and 40.14% when compared with UKF, HMM, and ALAD techniques.

A novel deep policy gradient action quantization for trusted collaborative computation in intelligent vehicle networks

The openness of the intelligent vehicle network makes it easy for selfish or untrustworthy vehicles to maliciously occupy limited resources in the mobile edge network or spread malicious information. However, most of the existing trust models rely on evaluating vehicles or data at the application level. For selfish or forgery attacks in intelligent vehicle networks, we propose a trusted deep reinforcement learning (DRL) cybersecurity approach for computation offloading to evaluate the safety and reliability performance in IoT edge networks, including our intelligent system model and a Deep Policy Gradient Action Quantization (DPGAQ) scheme. By introducing a reputation record table and designing a highly decisive communication trusted computing mode, we can accurately predict the untrusted selfish attack of vehicle in the task offloading of the Internet of things. Furthermore, in the multi-vehicle scenario, because the trusted offloading decision is a mixed integer programming problem, which leads to the dimension explosion of channel state and space, we propose a joint action-value quantization with attention mechanism to approximate the continuous actions values to a limited number of discrete values. Because it is not only inefficient but also unnecessary to generate high-dimensional decision actions in each time frame, we prune the infeasible action decisions by order preserving pruning to reduce the computational complexity of training and achieve efficient training on the premise of ensuring accuracy. To verify the feasibility and effectiveness of our proposed algorithm, millions of channels of edge vehicle networks are used as the input data. The simulation results show that compared with the benchmark trust model, DPGAQ achieves more than 72% reputation level, and improves 11%, 10% and 11% respectively in precision, recall and F-score.

Adaptive mining difficulty for blockchain to resist selfish mining attack

Aim: A blockchain provides data consistency and builds a fair mining environment for a network by using a consensus mechanism such as proof of work (PoW) and proof of stake. However, selfish mining is a well-known mining attack. It can reduce the fairness and destabilize the network, especially for a PoW-based blockchain. Therefore, in this paper, we propose a new approach, named the adaptive mining difficulty adjustment protocol, which can deter a selfish attack. Methods: We propose using the unit profit as an improved version of the relative revenue, because it is more flexible for calculating the miners’ profits in different periods and can be used to analyze repeated mining games. Based on the unit profit, we propose using the adaptive mining-difficulty adjustment protocol to reduce an attacker’s profit. Our protocol evaluates the effective hash power in a network more accurately and corrects the mining difficulty. Moreover, we introduce the discount factor and model the long-term profit to analyze the impact of a miner’s patience on its future profit. Results: We used an open-source simulator to simulate the competition between a selfish miner and an honest miner and determined their profits under different protocols. Our experimental results show that our protocol can effectively raise the attack threshold, but it reduces the total network profit if the attacker still attacks. However, the long-term profit model shows that a more patient attacker needs to invest more hash power and pay increased mining costs to maintain its attack. Conclusion: We conclude that, under our protocol, selfish attackers will tend to become honest miners if their hash power does not exceed the threshold, which means that our protocol can effectively deter selfish attacks and some variants of selfish attacks. Briefly, our protocol can correct the mining difficulty and leads to a more stable and fairer mining environment for a PoW-based blockchain.

Novel Link Establishment Communication Scheme against Selfish Attack Using Node Reward with Trust Level Evaluation Algorithm in MANET

Mobile network nodes are trustworthy nodes; the data access in transmitting and receiving the data packet is not efficient in different network procedures. The malicious nodes are available in routing path making the packet loss, in packet transmission for time instance. Since the regular mobile network must not verify every mobile node approved else illegal, subsequently the reserved level of nodes also was not considered, to injure the packet transmission procedure by link failure called connection loss. It minimizes the transmission rate and network lifetime and improves packet latency and energy usage. The proposed novel link establishment communication (NLEC) technique is used to find the dependable routing path against intruder node available in the network. This scheme selects genuine node for routing path production, by using the node reward with dependence level estimating algorithm to compute every node trust level and resource range, to disconnect higher trust level node and lower trust level node; higher trust level node is a genuine node which performs secure communication. Lower trust level node is selfish node and they are detected and ignored from path. It increases the lifespan of network and throughput and minimizes the end to end delay and energy consumption.

Bitcoin Selfish Mining Modeling and Dependability Analysis

Blockchain technology has gained prominence over the last decade. Numerous achievements have been made regarding how this technology can be utilized in different aspects of the industry, market, and governmental departments. Due to the safety-critical and security-critical nature of their uses, it is pivotal to model the dependability of blockchain-based systems. In this study, we focus on Bitcoin, a blockchain-based peer-to-peer cryptocurrency system. A continuous-time Markov chain-based analytical method is put forward to model and quantify the dependability of the Bitcoin system under selfish mining attacks. Numerical results are provided to examine the influences of several key parameters related to selfish miners’ computing power, attack triggering, and honest miners’ recovery capability. The conclusion made based on this research may contribute to the design of resilience algorithms to enhance the self-defense and robustness of cryptocurrency systems.

Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks

A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme.

GenSelfHolding: Fusing Selfish Mining and Block Withholding Attacks on Bitcoin Revisited

Due to the monetary value of Bitcoin, the most influential digital cryptocurrency in the world, Bitcoin has naturally become a valuable target of attacks, resulting in the emergence of many attack strategies on it. Among those attack strategies, selfish mining and block withholding attacks are two typical ones and attackers can obtain higher revenues under certain conditions than with an honest mining strategy. However, the combination of them will be a new type and more serious attack, which has not been analyzed in depth. In this paper, we propose GenSelfHolding, a general combined attack model with one selfish mining pool and random multiple honest pools on Bitcoin. Based on Markov chain, a general state transition graph and a general state distribution probability are presented to describe the internal features of our model. A general principle is then provided to calculate the attacker’s revenue. In addition, we give a detailed proof of the unique stable distribution of state transition probabilities. Such proof is an essential prerequisite for us to further present stable attacker revenue expressions under two specific scenarios, the GenSelfHolding model with two/three honest mining pools. Simulation results validate that the revenues of the attacker in these two specific models can reach up to 40% higher than those of classic selfish attackers in some cases.

ForkDec: Accurate Detection for Selfish Mining Attacks

Incentive mechanism is the key to the success of the Bitcoin system as a permissionless blockchain. It encourages participants to contribute their computing resources to ensure the correctness and consistency of user transaction records. Selfish mining attacks, however, prove that Bitcoin’s incentive mechanism is not incentive-compatible, which is contrary to traditional views. Selfish mining attacks may cause the loss of mining power, especially those of honest participants, which brings great security challenges to the Bitcoin system. Although there are a series of studies against selfish mining behaviors, these works have certain limitations: either the existing protocol needs to be modified or the detection effect for attacks is not satisfactory. We propose the ForkDec, a high-accuracy system for selfish mining detection based on the fully connected neural network, for the purpose of effectively deterring selfish attackers. The neural network contains a total of 100 neurons (10 hidden layers and 10 neurons per layer), learned on a training set containing about 200,000 fork samples. The data set, used to train the model, is generated by a Bitcoin mining simulator that we preconstructed. We also applied ForkDec to the test set to evaluate the attack detection and achieved a detection accuracy of 99.03%. The evaluation experiment demonstrates that ForkDec has certain application value and excellent research prospects.

A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs

Wireless sensor networks (WSNs) have been widely used for applications in numerous fields. One of the main challenges is the limited energy resources when designing secure routing in such networks. Hierarchical organization of nodes in the network can make efficient use of their resources. In this case, a subset of nodes, the cluster heads (CHs), is entrusted with transmitting messages from cluster nodes to the base station (BS). However, the existence of selfish or pollution attacker nodes in the network causes data transmission failure and damages the network availability and integrity. Mainly, when critical nodes like CH nodes misbehave by refusing to forward data to the BS, by modifying data in transit or by injecting polluted data, the whole network becomes defective. This paper presents a secure protocol against selfish and pollution attacker misbehavior in clustered WSNs, known as (SSP). It aims to thwart both selfish and pollution attacker misbehaviors, the former being a form of a Denial of Service (DoS) attack. In addition, it maintains a level of confidentiality against eavesdroppers. Based on a random linear network coding (NC) technique, the protocol uses pre-loaded matrices within sensor nodes to conceive a larger number of new packets from a set of initial data packets, thus creating data redundancy. Then, it transmits them through separate paths to the BS. Furthermore, it detects misbehaving nodes among CHs and executes a punishment mechanism using a control counter. The security analysis and simulation results demonstrate that the proposed solution is not only capable of preventing and detecting DoS attacks as well as pollution attacks, but can also maintain scalable and stable routing for large networks. The protocol means 100% of messages are successfully recovered and received at the BS when the percentage of lost packets is around 20%. Moreover, when the number of misbehaving nodes executing pollution attacks reaches a certain threshold, SSP scores a reception rate of correctly reconstructed messages equal to 100%. If the SSP protocol is not applied, the rate of reception of correctly reconstructed messages is reduced by 90% at the same case.

A security‐driven network architecture for routing in industrial Internet of Things

AbstractIndustrial Internet of Things (IIoT), as the integration of industrial network and IoT, has attracted a lot of attention from both academia and industry. However, with such integration deepens, many issues suffered by the industrial networks are exposed and magnified. The secure routing is a key focus, since the industrial environment is extremely vulnerable and many potential threats (eg, distributed denial of service and selfish attacks) are introduced due to the connection to the Internet. In order to prevent the threats from the Internet and embrace the benefits of IIoT, a security‐driven network framework for routing is particularly designed. Specifically, we first leverage the technologies of software‐defined networking (SDN), network function virtualization (NFV), and blockchain to create the overall framework which is flexible, programmable, and secure. On one hand, the three‐level structure of SDN/NFV is applied to each domain, based on which we can flexibly control and program the underlying forwarding devices in this domain for the purpose of calculating the optimal routing policies. On the other hand, the blockchain is applied among different SDN controllers to create a trust and untamperable environment. Then, under this framework, a secure routing mechanism is proposed as well to prevent the attacks on the basis of node identity authentication and node behavior authentication. The simulation experiments are carried out on the OMNet++ platform and the experimental results indicate that the proposed mechanism outperforms the other state‐of‐the‐art methods in terms of scalability and robustness especially when encountering malicious attacks.

Link Stability Inspired Hopping Technique for Detection and Alleviation of Malicious Adversaries

In multi-hop ad hoc network, the network organization exhaustively relies on the trust relationship that exists between the participating mobile nodes of the network. Secure and trustworthy mechanisms are necessary for enforcing collaboration between the mobile nodes. Further, the malicious adversaries that intentionally injure or compromise network functionality during the process of multi-hop communication need to be detected and isolated from the network. Specifically, malicious adversaries like selfish nodes and root node attacks that exploits the network resources without rendering cooperation in routing or packet forwarding is a major challenge that need to be handled. The core aim of this proposed Link Stability Inspired Hopping Technique concentrates on the classification of mobile nodes into malicious and benevolent nodes based on the computation of link stability factor. This discrimination between the mobile nodes is achieved by the computation of Contention count, Signal strength, Hop determination, Link energy availability and normalized multi-objective optimization function for facilitating the elimination of malicious adversaries so as to improve the rate of data dissemination in the network. The interactions between the mobile nodes are periodically monitored for testing its link stability based on which the mobile nodes may be identified as co-operative and malicious.

Deployment and Selection of Monitoring Nodes for Detection of Selfish Attacks in MANET

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.

Taxonomy of Reputation Based Defending Mechanisms Against Types of Attacks in Delay Tolerant Networks

Delay tolerant networks (DTNs) have been standardised as a solution for wireless networking scenarios with intermittent connectivity. Limited buffer space and limited battery power in DTNs can give rise to malicious nodes. These malicious nodes misuse network resources and exhibit malicious behaviour which can cripple the network. This paper discusses various forms of malicious behaviour: flooding attack, black hole attack, and selfish attack. Several reputation based defending mechanisms against these attacks have been proposed by the DTN research community. In this paper, a taxonomy of these reputation-based defending mechanisms is defined, in terms of how reputations are calculated and disseminated. The mechanisms are categorised as source-based, peer-based, trusted-authority based or destination-based, depending on which node takes the decision to assign reputation. Under each category, the mechanisms are further classified as either node-centric (the node itself), or node-in-contact-centric (contacting node), based on which node keeps evidence of the job performed. This paper presents a review of all these reputation mechanisms and compare them in terms of computation and other parameters. Destination-based reputation mechanism seems to be a better approach.

To attack or not to: an evolutionary game model to study the dynamics of selfish PUEA attackers in cognitive radio networks

The current paradigm shift towards heterogeneous wireless networks is being spearheaded by 5G technology for which cognitive radio networks (CRNs) have evolved as a promising enabling technology. CRNs are susceptible to primary user emulation attacks (PUEAs). This current work is an attempt to use evolutionary game theory (EGT) to study the dynamics of selfish SUs (PUEA) and normal SUs in a CRN. The game formulation and the analysis thereof, are conclusive of the fact that in a CRN running an intrusion detection system (IDS) with a very high rate of detection of the attackers, the winning strategy turns out to be the normal strategy. However, if the IDS's detection rate is kept relatively high (60%), the ultimate winning strategy depends on the percentage of the initial population, where the more populous one continues to influence others to convert, and finally leading to become the dominant strategy.

A Machine Learning Approach for Backoff Manipulation Attack Detection in Cognitive Radio

Cognitive radio (CR) is a promising paradigm that comes to address the scarcity of the radio spectrum by providing opportunistic utilization of the underutilized licensed channels to attain higher spectrum efficiency. The efficient use of the vacant portion of the spectrum depends directly on the medium access control (MAC) layer that coordinates fairly the access of the CR nodes to the idle spectrum. However, the MAC layer is vulnerable to several attacks driven by malicious nodes. One of those attacks is the backoff manipulation attack (BMA), where the selfish attacker deviates from the defined contention mechanism to gain inequitable access to the available channels. This unfair access presents some specific characteristics of an attacker, which can be considered as an input to the supervised machine learning algorithm for classification. In this paper, we propose a support vector machine (SVM) based model in order to distinctively identify the attacker depending on the throughput and the average packet delay to classify/predict an eventual attack. Finally, theoretical predictions and simulation results are presented to validate the proposed framework while giving useful insights into CR systems’ performance, vulnerable to BMA attacks.

Design of an Agent Based Analysis Approach for Secure Communication Against the Existing of Selfish Node Attack

The internal participating nodes in mobile network can be affects fully or partially by selfish nature to improve its own communication rate. This nature can degrade the forwarding rate and affects the overall performance of the network. The detection of such selfish nodes is required to improve the communication throughput and network performance. In this paper, an Agent based analysis approach is defined to detect the selfish node in the network. The fuzzy based zonal analysis method is suggested to perform the zonal evaluation on the mobile network. The distributed Agents performed the analysis on the fuzzy based communication parameters. The parameter specific evaluation method is able to separate the good and selfish nodes. Once the selfish nodes are identified, the preventive route formation is done for safe communication. In this paper, the Agent adaptive model is presented for detection and prevention of selfish nodes. The proposed Agent adaptive model is simulated in NS2 environment. The comparative results are generated against token based, agent based and watchdog methods. The analysis results show that the proposed protocol has achieve the 17% improvement in packet communication, 37% reduction in communication loss and 53ms reduction in packet delay.

Semi-supervised machine learning for primary user emulation attack detection and prevention through core-based analytics for cognitive radio networks

Cognitive radio networks are software controlled radios with the ability to allocate and reallocate spectrum depending upon the demand. Although they promise an extremely optimal use of the spectrum, they also bring in the challenges of misuse and attacks. Selfish attacks among other attacks are the most challenging, in which a secondary user or an unauthorized user with unlicensed spectrum pretends to be a primary user by altering the signal characteristics. Proposed methods leverage advancement to efficiently detect and prevent primary user emulation future attack in cognitive radio using machine language techniques. In this paper novel method is proposed to leverage unique methodology which can efficiently handle during various dynamic changes includes varying bandwidth, signature changes etc… performing learning and classification at edge nodes followed by core nodes using deep learning convolution network. The proposed method is compared with that of two other state-of-art machine learning-based attack detection protocols and has found to significantly reduce the false alarm to secondary network, at the same time improve the overall detection accuracy at the primary network.

A Survey of Efficient Trust Management Schemes in Mobile Ad-Hoc Network

Mobile Ad Hoc Networks (MANETs) are comprised of an arrangement of self-sorting mobile hosts furnished with wireless interaction devices gathered in groups without the need of any settled framework as well as centralized organization to maintain a system over radio connections. Every mobile node can react as a host and also, the router freely utilizes the wireless medium inside the correspondence range to deal with the interaction between huge quantities of individual mobile nodes by framing a correspondence system and trading the information among them without using any described group of the base station. A trust-based model in MANET estimates and sets up trust relationship among objectives. Trust-based routing is utilized to keep away data from different attackers like a wormhole, DOS, black-hole, selfish attack and so forth. Trust can be executed in different steps like reputation, subjective rationale and from the supposition of the neighboring node. A trust estimation approach not just watches the behavior of neighbor nodes, additionally it screens the transmission of the information packet in the identification of the route for exact estimation of trust value. A survey is carried out to find some of the limitations behind the existing works which has been done by the researchers to implement various approaches thus to build the trust management framework. Through the survey, it is observed that existing works focused only on the authenticated transmission of the message, how it transmits packets to the destination node securely using a trust-based scheme. And also, it is observed that the routing approach only focused on the key management issues. Certain limitation observed in the implemented approaches of existing work loses the reliability of framework. Thus, to withstand these issues it is necessary to establish a reliable security framework that protects the information exchanged among the users in a network while detecting various misbehaving attacks among the users. Confidentiality, as well as the integrity of information, can be secured by combining context-aware access control with trust management. The performance parameters should be evaluated with the previous works packet delivery ratio, packet drop, detection accuracy, number of false positives, and overhead.

An Efficient Genetic Based Broadcast Stateless Group Key Management Scheme with Dynamic Rekeying in Mobile Ad-Hoc Networks

This paper addresses the issue in managing a group key among dynamic group of nodes in Mobile Ad hoc Networks (MANETs), where the participants frequently miss the group key update, commonly known as rekeying. In this paper, we propose a broadcast stateless and distributed Group Key Management framework: Genetic based Group Key Agreement scheme, for supporting dynamic rekeying mechanism in MANET. The rekeying scheme operates with hash functions and Lagrange interpolation polynomial implementation over finite area for group key establishment. Moreover, to provide a strong security mechanism, a revocation system is presented to gather accurate rate of node misbehaviours. The simulation results show that the proposed group key management scheme achieved higher performance and security compared with other existing key management schemes. Moreover, the scheme detects both selfish and malicious attacks fairly at lower overhead.