Black Hole Attack Research Articles

Safeguarding IoT networks: Mitigating black hole attacks with an innovative defense algorithm

The Internet of Things (IoT) and Wireless Sensor Networks (WSNs) have rapidly spread in recent decades, leading to remarkable innovation and integrated possibilities. The switch from IPv4 to IPv6, made possible by advancements in networking technology and the use of nanodevices, has further improved connectivity. This move allows for connecting a wider range of devices to servers. Nevertheless, the increasing interconnectivity has brought about difficulties in efficiently overseeing and analysing the enormous amount of data produced throughout all levels of the IoT. The requirement of comprehensive security management is particularly concerning for IoT devices due to their large quantity and small size. Within the layered architecture of IoT, the network layer assumes pivotal importance in ensuring security, bearing responsibility for storing routing information and executing corresponding decisions. The Black Hole attack is a frequently encountered and significant concern among the security attacks addressed. This paper thoroughly examines the consequences of the Black Hole attack on IoT networks, carefully analyzing its impact. Furthermore, it presents a novel mitigation algorithm designed to counter such threats efficiently. The research employs NS2 and Simulink to run extensive simulations, enabling the evaluation of network throughput and Packet Delivery Ratio (PDR). Applying the proposed mitigation strategy to a network affected by the Black Hole attack results in a significant improvement in throughput, which closely resembles that of an unaffected network. The observed Packet Delivery Ratio (PDR) is measured at 98.21%. This highlights the algorithm’s effectiveness in mitigating the detrimental effects of the Black Hole attack on IoT networks.

Fuzzy Heuristics for Detecting and Preventing Black Hole Attack

Mobile Ad-hoc Networks (MANET) is a set of computing nodes with there is no fixed infrastructure support. Every node in the network communicates with one another through wireless links. However, in MANET, the dynamic topology of the nodes is the vital demanding duty to produce security to the network and the black hole attacks get identified and prevented. In this paper, a novel fuzzy inference system is designed for black hole attack detection depending on the node authentication, trust value, Certificate Authority (CA), energy level, and message integrity. Before initiating the route discovery process in MANET, the proposed work mainly concentrates on node authentication. The simulation gets carried out using the Network Simulator (NS2), wherein the fuzzy inference system designed shows better performance by providing a certificate to only the trusted nodes. This helps the malicious nodes detection and prevents the black hole attack. The improvement in Packet Delivery Ratio (PDR) enhances throughput and the end to end delay gets reduced through better performance results. This proves that the system is more reliable and recovered to be used in military applications

Kolmogorov-Smirnov based method for detecting black hole attack in vehicular ad-hoc networks

The security of ad hoc networks continues to pose a major challenge in today's digital age. Threatened by unscrupulous users, especially in decentralized and open architectures, ad-hoc vehicular networks make protection against malicious attacks a difficult task. Vehicular networks, a specific subset of ad hoc networks, inherit security vulnerabilities from their parent network. One of the best-documented attacks is the black hole, in which a malicious user attempts to manipulate the routing mechanism to gain unauthorized access to the route, then intercepts and discards data packets. The result is an interruption of service to the intended recipient. In our research, we propose a black hole attack detection system based on the Kolmogorov-Smirnov statistical method. This system is designed to identify communication disruptions caused by such attacks without requiring structural modifications to the routing protocol. The results show that the proposed method can detect the presence of the attack by monitoring network activity. The results are tested and validated using SUMO (Simulation of Urban MObility) to generate microscopic road traffic, and NS-2 (Network Simulator) to generate network communication.

RTO‐TV: Routed Tree Optimization and Trust‐Value‐Based Security Scheme to Prevent Black Hole Attack in MANET

RTO‐TV: Routed Tree Optimization and Trust‐Value‐Based Security Scheme to Prevent Black Hole Attack in MANET

Robust link-assessment-based approach for detection and isolation of blackhole attacker in resource constraint internet of things

Robust link-assessment-based approach for detection and isolation of blackhole attacker in resource constraint internet of things

Secure and efficient intelligent transport system based on cryptographic authentication mechanism for vehicular ad hoc networks

Vehicular Adhoc Networks (VANETs) are two of the future’s most commonly used communication systems. VANETs are designed to protect vehicle drivers by establishing autonomous contacts with nearby vehicles. Vehicles operate as intelligent mobile nodes in adhoc networks characterized by mobility and complexity. Cybersecurity in VANET is affected by malicious attacks, which must be detected and prevented. The overall performance of the proposed GPCR-MA with blackhole and Sybil attack is average latency, packet transmission ratio, energy consumption and throughput. It is predicted because the Blackhole attack vehicle sends answers instantly without checking its routing table. These changes in the metrics used contribute to the network efficiency deteriorating primarily in the face of an attack. Compared with other related schemes, our proposal provides privacy and security guarantees for VANETs while improving efficiency.

Retracted: A Low-Latency and High-Throughput Multipath Technique to Overcome Black Hole Attack in Mobile Ad Hoc Network (MTBD)

Retracted: A Low-Latency and High-Throughput Multipath Technique to Overcome Black Hole Attack in Mobile Ad Hoc Network (MTBD)

A GAN-based security strategy for WSN networks based on honeypot algorithm

One of the essential elements of the cyber physical system is wireless sensor network (WSN), which is a multi-hop, self-organizing wireless network made up of numerousfixed or moving sensors. It jointly senses, gathers, analyses, and transfer the data ofdetected objects in the network's service area before sending this data to the network' sowner. Blackhole, Grayhole, Flooding, and Scheduling attacks are just a few of theusual WSN attacks that could quickly harm the system. Owing to constrained resourceof sensor nodes, a significant level of redundancy, network data's higher correlation,intrusion detection schemes for wireless sensor networks also have the drawbacks ofpoor identification rate, high computation overhead, and higher false alarm rate. Tooverwhelm the above problems, in this manuscript, Self-Attention-Based ProvisionalVariational Auto-Encoder Generative Adversarial Network (SAPVAGAN) optimized withHoney Badger Algorithms proposed for enhancing Cyber security in Wireless sensornetwork(ECS-WSN-SAPVAGANHBA). Based on the optimum features, the intruders in WSN data are categorizedinto normal and anomalous data (Black hole attack, Gray hole attack, Flooding attackand Scheduling attacks) with the help of SAPVAGAN.In general, SAPVAGANnotexpose any adaption of optimization modes for determining better parameters toassure accurate detection of WSN intrusion. Hence, Honey Badger Algorithm (HBA) isproposed in this work to optimize the SAPVAGAN, which precisely detects the WSNintrusion.The proposed ECS-WSN-SAPVAGANHBA method is performed in Pythonutilizing the WSN-DS dataset. Here, the performance metrics like recall, precision, f-measure,specificity, accuracy, RoC and computation time is evaluated. The proposedmethod provides 23.56%, 12.64%, and 15.63% higher accuracy, 23.14%, 16.78% and20.04% lower computational time analyzed to the existing models,such as IntrusionDetection System in Wireless Sensor Network Using Light GBM method(ECS-WSNSLGBM),Intrusion Detection Scheme in Wireless Sensor Network utilizing RecurrentNeural Network(ECS-WSN-RNN) and Intrusion Detection Scheme for Wireless SensorNetworks utilizing Whale Optimized Gate Recurrent Unit (ECS-WSN-WOGRU)respectively.

An efficacious neural network and DNA cryptography-based algorithm for preventing black hole attacks in MANET

An efficacious neural network and DNA cryptography-based algorithm for preventing black hole attacks in MANET

Enhancing IoT Security with Trust-Based Mechanism for Mitigating Black Hole Attacks

Enhancing IoT Security with Trust-Based Mechanism for Mitigating Black Hole Attacks

Comprehensive Trust based Routing Protocol to Mitigate Black-hole attack in Wireless Sensor Networks

Wireless Sensor Network (WSN) is a collection of sensor nodes, that sense environmental data and send it to the administrator for further processing. Sensor nodes are wireless devices with limited battery and are vulnerable to security attacks such as black hole attack, gray hole attack, sink hole attack etc. Researchers have proposed many security mechanisms to mitigate security attacks. Trust based approaches have gained tremendous interest among researchers to embed security in WSNs. A Trust Based Secure Routing Protocol to mitigate black hole attack is presented in this paper. The protocol computes comprehensive trust value for each node and routes the packets only through the nodes with trust value > 0.5. The results of the proposed protocol are compared with TBSEER [10] protocol. The results show an improvement in Packet Delivery Ratio (PDR), throughput, End to End (EED), routing overhead and energy consumption.

Node authentication and encrypted data transmission in mobile ad hoc network using the swarm intelligence‐based secure ad‐hoc on‐demand distance vector algorithm

AbstractIn a mobile ad hoc network (MANET), all nodes are communicated with one another across wireless networks to create a temporary network without the support of centralised management. Due to dynamic topology in MANET, secure routing is a crucial issue. The existing secure routing protocol and their security concerns are analysed in this work. The suggested Swarm Intelligence‐based Secure Ad‐hoc On‐demand Distance Vector (SIS‐AODV) algorithm offers security by applying a secret key and hash mechanism to prevent the involvement of malicious nodes in routing operations. A secure routing system of MANET guards against internal and external network attacks. The proposed SIS‐AODV algorithm consists of two sections: the secret key value generated by applying Elliptical Curve Cryptography (ECC)‐based algorithm and the PRESENT algorithm to encrypt the data packets. Besides, authentication and non‐repudiation are applied using the H‐PRESENT 128 algorithm. The PRESENT algorithm and H‐PRESENT 128 hash function require less computational power. Centralised management is optional in this scheme, so overhead decreases. The second section of SIS‐AODV consists of Ant Colony Grey Wolf Optimization over the AODV algorithm to improve network performance while implementing a security algorithm over MANET. Analysis results show maximum performance with a packet delivery ratio of 98% and throughput of 85%. In addition, end‐to‐end delay is reduced by up to 25%, and routing overhead decreases by up to 20%. Keywords: AODV, Elliptical Curve, PRESENT, H‐PRESENT, Euclidean Algorithm, ACO, GWO, Blackhole attack.

Preventing Black Hole Attacks in AODV Using RREQ Packets

Ad hoc networks, formed by multiple wireless communication devices without any connection to wired or intermediary devices such as by access points, are widely used in various situations to construct flexible networks that are not restricted by communication facilities. Ad hoc networks can rarely use existing infrastructure, and no authentication infrastructure is included in these networks as a trusted third party. Hence, distinguishing between ordinary and malicious terminals can be challenging. As a result, black hole attacks are among the most serious security threats to Ad hoc On-demand Distance Vector (AODV) routing, which is one of the most popular routing protocols in mobile ad hoc networks. In this study, we propose a defense method against black hole attacks in which malicious nodes are actively detected to prevent attacks. We applied the proposed method to a network containing nodes engaging in black hole attacks, confirming that the network’s performance is dramatically improved compared to a network without the proposed method.

Secure localization techniques in wireless sensor networks against routing attacks based on hybrid machine learning models

The identification and localization of malicious nodes in wireless sensor networks (WSNs) is a hot area of research that can considerably extend the network's lifetime and make it more valuable. We use sensors whose positions are known, or anchor nodes, to make educated guesses about the positions of the unknown nodes. Several localization methods have been developed for precise estimation of the unknowable nodes. So, during the network setup process, finding suitable network parameters for node localization with the requisite accuracy in a short amount of time remains a tough task. Due to the fact that they manipulate network resources and routing protocols, routing assaults like wormhole attacks, Sybil attacks, blackhole attacks, and replay attacks are just a few examples of the types of attacks that have the potential to hinder the accuracy of localization and the quality of service provided by WSNs. This work proposes safe localization and detection of routing threats in wireless sensor networks by utilizing hybrid optimized machine learning approaches for optimal distance, position, and data communication. These approaches aim to find the optimal distance between sensors and the optimal position of sensors. Calculating the average localization accuracy and finding malicious nodes both need the use of the benchmark datasets CICIDS2017 and UNSW NB15. The machine learning algorithms that have been provided can be utilized with these datasets. The cluster labelling K-means clustering technique is applied to binary classification in the system that has been proposed. As a consequence, the system achieves an average detection accuracy of 100%. The findings of the simulation indicate that the proposed hybrid strategy is capable of achieving a higher level of localization accuracy of the unknown nodes, with an average localization error of 0.191.

A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs.

Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.

Proposed Naïve Bayes- Genetic algorithm to detect black hole attacks in MANETs.

The threats and attacks on mobile ad hoc networks (MANETs) are significant, making it difficult for traditional security systems to provide complete protection. Therefore, an efficient hybrid clustering approach must be designed to construct an intrusion detection system (IDS) that is suitable for this network. IDSs are crucial in MANETs due to the presence of black hole threats, which are the most significant vulnerabilities in this network.Our suggestion is to use a hybrid classifier to detect black hole attacks in MANETs. This can be achieved by using the Naïve Bayes algorithm for clustering to select the cluster head, and modify the Genetic Algorithm to identify the node responsible for a black hole attack on the optimal path. Finally, the confidence server instructs the destination node. If permitted, it alerts the collection head; otherwise, it identifies the node as a malicious one in the black hole attack within each cluster. The results of our proposed technique show that it has improved package damage rate, quantity, package distribution ratio, whole network interruption, and standardized directing capacity parameters compared to current black hole detection approaches.
 The simulation was done in KDD cup 99 to carry out black hole attack and trace file obtained is used as dataset for training and testing purpose using visual basic.

Machine Learning for Intrusion Detection in Ad-hoc Networks: Wormhole and Blackhole Attacks Case

This paper addresses the security concerns associated with Mobile Ad-hoc Networks (MANET) and proposes a new method for detecting and preventing attacks using machine learning. The study involved the creation of a MANET with 26 nodes in NetSim (Network Simulator) software, followed by the implementation of wormhole and blackhole attacks. A dataset was generated from the network traffic obtained during the simulations, and a machine-learning model was designed to predict and detect these attacks. The model achieved high sensitivity, accuracy and f1 scores of 99%. The effectiveness of the model was tested by developing a real-time application. This method can be applied to any wireless network and is particularly relevant for companies that use Ad-hoc networks for communication.

EOEEORFP: Eagle Optimized Energy Efficient Optimal Route-Finding Protocol for Secure Data Transmission in FANETs

This FANETs (Flying Adhoc NETworks) are supported by UAVs (Unmanned Aerial Vehicles) which carry out the task of transmitting and receiving data. Transmission and routing are complicated by the highly mobile AUVs. Node links are temporary and may result in transmission related issues. FANETs are prone to network attacks, especially black hole attacks leading to heavy data loss. This may lead to packet drops which might compromise the transmitted data. The proposed Eagle Optimized Energy Efficient Optimal Route-Finding Protocol (EOEEORFP) selects an efficient route towards the destination. Factors like trust degree, priority queue, network delay, hop count and energy consumption levels are taken into consideration in designing the routing strategy. These factors help in identifying the nodes that form the trusted nodes in the routing path. This is achieved by employing the biological behavior of eagles in finding their prey. The suggested protocol works in two stages. In the initial stage, it sets up an optimal route with selective nodes (trusted nodes). In the second stage, the network security is fortified by eradicating black hole attacks by using RREP (Route Reply) packets strategically. The EOEEORFP protocol assures the successful data delivery by employing a trusted routing path. The proposed EOEEORFP protocol is assessed against Adhoc On demand Distance Vector (AODV) and AODV-V2 protocols to measure its performance. EOEEORFP achieves very good PDR and throughput while conserving the network energy by minimizing the network delay.

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

DT-AODV: a dynamic threshold protocol against black-hole attack in MANET

DT-AODV: a dynamic threshold protocol against black-hole attack in MANET