Black Hole Attack Research Articles

Improving the IoT and Cloud Computing integration using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.

Black‐hole attack and counter measure in ad hoc networks using traditional routing optimization

SummaryAd hoc networks have constraints like energy, memory, computation power, and communication range which makes the nodes vulnerable for attacks. In this article, black hole attack detection mechanism is carried out on traditional ad hoc on demand distance vector routing (AODV) protocol. Initially the proposed work proceeded with black‐hole attack configured on conventional AODV protocol. Black‐hole node will give route reply with highest sequence number to entice the sender to create a route through it. By using intruder detection system threshold mechanism, the black‐hole nodes are identified and isolated. In this article, maximal destination sequence number is estimated by using linear regression technique. Performance comparison is carried out with normal, black hole based and black hole detected routing mechanisms. The simulation findings show that that incorporated method enhances network performance by improving QoS under black hole attack.

An Intrusion Detection Mechanism for MANETS based on Deep Learning Artificial Neural Networks (ANNS)

Mobile Ad-hoc Network (MANET) is a distributed, decentralized network of wireless portable nodes connecting directly without any fixed communication base station or centralized administration. Nodes in MANET move continuously in random directions and follow an arbitrary manner, which presents numerous challenges to these networks and make them more susceptible to different security threats. Due to this decentralized nature of their overall architecture, combined with the limitation of hardware resources, those infrastructure-less networks are more susceptible to different security attacks such as black hole attack, network partition, node selfishness, and Denial of Service (DoS) attacks. This work aims to present, investigate, and design an intrusion detection predictive technique for Mobile Ad hoc networks using deep learning artificial neural networks (ANNs). A simulation-based evaluation and a deep ANNs modelling for detecting and isolating a Denial of Service (DoS) attack are presented to improve the overall security level of Mobile ad hoc networks.

Enhanced On-demand Distance Vector Routing Protocol to prevent Blackhole Attack in MANET

Wireless networks are becoming increasingly popular. Mobile ad hoc networks are one category among the different types of wireless networks that transmit packets from the sender node to the receiver node without the use of abase station or infrastructure, as the nodes serve as both hosts and routers. These networks are referred to as mobile because they are movable. MANET is an ad-hoc network that can change positions at any time, and nodes can join or leave at any moment, making it vulnerable to attacks such as Blackhole. Existing solutions, in some ways, led to more memory space consumption, while others led to an overhead. This research proposes an Enhanced On-demand Distance Vector (AODV) routing protocol to prevent Blackhole attacks on MANETs using Diffie Hellman and Message Digest 5 (DHMD), implemented using Network Simulator 2 (NS2). The performance of the proposed protocol was evaluated using the following parameters: Packet Delivery Ratio, throughput, End to End (E2E)Delay, and routing overhead. It was concluded that DHMD has reduced network over head as it resulted to 23% while AODV resulted at 38%and memory consumption for DHMD gave 0.52ms compared to AODV that gave 0.81msdue to Blackhole prevention. This research will help to mitigate the effect of blackhole attacks in a network and increase network performance by reducing overhead and memory consumption.

An extended evaluation on machine learning techniques for Denial-of-Service detection in Wireless Sensor Networks

Despite the research efforts in the last years, Denial-of-Service (DoS) attacks detection still represent a challenge for the Internet of Things (IoT) scenarios composed of hard-constrained networks, as Wireless Sensor Networks (WSN). Machine Learning (ML) techniques have been presented as a promising alternative for detecting DoS attacks. Therefore, understanding the impact of the different ML techniques in a comprehensive way is quite important. In this work, we present an extended evaluation covering both supervised and unsupervised ML techniques considering three feature selection algorithms. Our experiments are based on Flooding, Grayhole, and Blackhole DoS attacks from a public WSN-based dataset. As an additional contribution, we investigate the adjustable parameter K to maximize the performance of unsupervised techniques. Experimental comparison is guided by the accuracy, recall, precision, F1-Score, and processing time metrics. Results reveal that, among the studied ML algorithms, the supervised techniques present better performance than the unsupervised ones: the highest F1-Score (95.69%) was obtained by the REPTree with the OneR feature selection algorithm for detecting blackhole attacks. In general, supervised techniques are faster than unsupervised. REPTree is the fastest, spending 0.931 μs on average to classify a sample.

Evaluation of Traffic Models under Multiple Black hole Attack in Wireless Mesh Network

Wireless networks are the networks in which data communication is done through the wireless channels and wireless nodes. In such type of the networks, cooperation of the all nodes is required. Due to which, chances of security attacks is possible and network performance and security is badly affected. Black hole attack is a security attack comes under the active, internal as well as external attacks. In black hole attack, malicious node presents a suitable, shortest and low cost path. If the above path is selected for data transmission, malicious node drops the received packets and as a result performance of the network is degraded. In this paper, impact of Random Direction (RD) and Reference Point Group Mobility (RPGM) models have been analyzed for wireless mesh network in the presence of multiple black hole attacks. By varying the number of network connections and node density, (Ad hoc on demand distance vector) AODV routing protocol is evaluated. Normalized routing load, average end to end delay, average throughput, and total number of packets received are considered as performance metrics. Bonn-motion tool has been used to create the scenario of mobility models. In the presence of black hole attack, and under RPGM mobility model, AODV routing protocol presents better results in respect of average throughput, normalized routing load. Simulation work has been carried out on network simulator version NS-2.35.

Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A Machine Learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector Machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively.

Avoid Suspicious Route of Blackhole Nodes in MANET’s: Using A Cooperative Trapping

Mobile Ad hoc Network (MANET) is decentralized wireless network and can communicate without existing infrastructure in many areas. MANET is vulnerable to various attacks that affect its performance such as blackhole attack. Blackhole attacker, inject fault routing information to persuade the source node to select the path with malicious node as the shortest path. To eliminate malicious nodes from launching any collaborative attack. A cooperative Trapping Approach (CTA) was proposed based on modifying Ad-hoc On-demand Distance Vector (AODV) routing protocol and trapping the malicious nodes by responding to the trap request message. The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack. The approach realizes a backward tracking mechanism to perform the elimination process. The proposed algorithm (CTA) was executed using NS-2 network simulator. The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput, end to end delay, packet delivery ratio, and consuming energy. The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

AODV-based Defense Mechanism for Mitigating Blackhole Attacks in MANET

Mobile Ad hoc Networks (MANETs) are decentralized and self-configuring networks composed of mobile devices that communicate without a fixed infrastructure. However, the open nature of MANETs makes them vulnerable to various security threats, including blackhole attacks, where malicious nodes attract and discard network traffic without forwarding it to its intended destination. Mitigating blackhole attacks is crucial to ensure the reliability and security of communication in MANETs. This paper focuses on the development and evaluation of AODV (Ad hoc On-Demand Distance Vector)-based defence mechanisms for effectively mitigating blackhole attacks in MANETs, while simultaneously addressing energy efficiency and environmental sustainability. AODV is a widely used routing protocol in MANETs due to its on-demand nature and low overhead. However, it lacks built-in security mechanisms, making it susceptible to attacks. We incorporate energyaware route selection, solar-powered routing, collaborative energy sharing, energy-efficient intrusion detection, green routing optimization, and energy harvesting from environmental sources. By considering energy consumption and environmental factors in the route selection process, our defense mechanism not only enhances the security of the network but also contributes to energy conservation and reduced environmental impact. To evaluate the effectiveness of the proposed defence mechanisms, extensive simulations and performance analyses are conducted using network simulation tools. Through simulation-based evaluations, we demonstrate the effectiveness of our approach in achieving robust blackhole attack mitigation while extending the network’s lifetime and minimizing its carbon footprint. Our research offers valuable insights into the development of energy-efficient and environmentally sustainable solutions for securing MANETs in the face of evolving security threats.

Dipper Throated Optimization for Detecting Black-Hole Attacks in MANETs

In terms of security and privacy, mobile ad-hoc network (MANET) continues to be in demand for additional debate and development. As more MANET applications become data-oriented, implementing a secure and reliable data transfer protocol becomes a major concern in the architecture. However, MANET’s lack of infrastructure, unpredictable topology, and restricted resources, as well as the lack of a previously permitted trust relationship among connected nodes, contribute to the attack detection burden. A novel detection approach is presented in this paper to classify passive and active black-hole attacks. The proposed approach is based on the dipper throated optimization (DTO) algorithm, which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’ quality of service. A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron (DTO-MLP), and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical (LEACH) clustering technique. MLP is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights of minor features. This hybrid method is primarily designed to combat active black-hole assaults. Using the LEACH clustering phase, however, can also detect passive black-hole attacks. The effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested approach. For diverse mobility situations, the results demonstrate up to 97% detection accuracy and faster execution time. Furthermore, the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

Fuzzy Reputation Based Trust Mechanism for Mitigating Attacks in MANET

Mobile Ad-hoc Networks (MANET) usage across the globe is increasing by the day. Evaluating a node’s trust value has significant advantages since such network applications only run efficiently by involving trustable nodes. The trust values are estimated based on the reputation values of each node in the network by using different mechanisms. However, these mechanisms have various challenging issues which degrade the network performance. Hence, a novel Quality of Service (QoS) Trust Estimation with Black/Gray hole Attack Detection approach is proposed in this research work. Initially, the QoS-based trust estimation is proposed by using a Fuzzy logic scheme. The trust value of each node is estimated by using each node’s reputation values which are determined based on the fuzzy membership function values and utilizing QoS parameters such as residual energy, bandwidth, node mobility, and reliability. This mechanism prevents only the black hole attack in the network during transmission. But, the gray hole attacks are not identified which in turn increases the packet drop rate significantly. Hence, the gray hole attack is also detected based on the Kullback-Leibler (KL) divergence method used for estimating the statistical measures. Additional QoS metrics are considered to prevent the gray hole attack, such as packet loss, packet delivery ratio, and delay for each node. Thus, the proposed mechanism prevents both black hole and gray hole attacks simultaneously. Finally, the simulation results show that the effectiveness of the proposed mechanism compared with the other trust-aware routing protocols in MANET.

Network security attacks for wireless mobile ad hoc networks using hybrid routing protocol

Mobile Ad Hoc Networks (MANETs) are decentralized networks where mobile nodes communicate through radio waves without fixed infrastructure. However, they are susceptible to security attacks like the black hole attack, where malicious nodes claim optimal routes to intercept packets. This paper focuses on detecting black hole attacks within MANETs using a hybrid routing protocol. Specifically, the Modified-Zone-Based Hierarchical Link State (M-ZHLS) routing protocol is proposed and evaluated against existing protocols using simulations in NS2. The proposed method employs on-demand topology information transmission and utilizes techniques like expanding ring flooding for route discovery. Simulation results demonstrate that M-ZHLS outperforms existing protocols by reducing average delay by 5%, lowering routing overhead by 4%, and increasing delivery ratio by 4.5%. Future work aims to further enhance network performance in real-time environments.

A Review on the Security of IoT Networks: From Network Layer’s Perspective

Internet of Things (IoT) has revolutionized the world in the last decade. Today millions of devices are connected to each other utilizing IoT technology in one way or the other. With the significant growth in IoT devices, the provision of IoT security is imperative. Routing protocol for low power and lossy networks (RPL) is a network layer protocol, specially designed for routing in IoT devices. RPL protocol faces many attacks such as selective forwarding attacks, blackhole attacks, sybil attacks, wormhole attacks, and sinkhole attacks. All these attacks pose great threats to IoT networks and can substantially affect the performance of the network. In this work, a comprehensive review of internal attacks on the network layer is presented. Specifically, we focus on the literature that considers presenting solutions for the detection and prevention of sinkhole attacks. We reviewed the state-of-the-art works and different performance parameters like energy consumption, scalability, threshold value, packet delivery ratio, and throughput. Moreover, we also present a detailed analysis of machine learning-based algorithms and techniques proposed for the security of RPL protocol against internal attacks.

Swarm Intelligence Based Routing with Black Hole Attack Detection in MANET

Mobile Ad hoc Network (MANET) possesses unique characteristics which makes it vulnerable to security threats. In MANET, it is highly challenging to protect the nodes from cyberattacks. Power conservation improves both life time of nodes as well as the network. Computational capabilities and memory constraints are critical issues in the implementation of cryptographic techniques. Energy and security are two important factors that need to be considered for improving the performance of MANET. So, the incorporation of an energy efficient secure routing protocol becomes inevitable to ensure appropriate action upon the network. The nodes present in a network are limited due to energy constraints and secure communication protocols. Hence, the current study proposed an energy-efficient defense scheme using swarm intelligence approach. The functioning of the proposed method was validated under NS2 simulation. The experimental results confirmed that the proposed work outperformed existing methods in terms of packet delivery ratio, average end-to-end delay and throughput.

Reputation-Based Opportunistic Routing Protocol Using Q-Learning for MANET Attacked by Malicious Nodes

Irrespective of whether the environment is wired or wireless, routing is an important challenge in networks. Since mobile ad hoc networks (MANETs) are flexible and decentralized wireless networks, routing is very difficult. Furthermore, malicious nodes existing in the MANET can damage the routing performance of the network. Recently, reinforcement learning has been proposed to address these problems. Being a reinforcement learning algorithm, the Q-learning mechanism is suitable for an opportunistic routing approach because it not only adapts to changing networks, but also mitigates the effect of malicious nodes on packet transmission. In this study, we propose a new reinforcement learning routing protocol for MANETs called reputation opportunistic routing based on Q-learning (RORQ). Using this protocol, which works based on game theory, a reputation system can detect and exclude malicious nodes in a network for efficient routing. Thus, our method can find a routing path more effectively in an environment attacked by malicious nodes. The simulation results showed that the proposed method could achieve superior routing performance compared with other state-of-the-art routing protocols. In addition, compared to other algorithms, the proposed method demonstrated gains of up to 55% in terms of packet loss, up to 82% in terms of average end-to-end delay, and up to 28% in terms of energy efficiency in the blackhole attack scenario and up to 73% in terms of packet loss, up to 35% in terms of average end-to-end delay, and up to 12% in terms of energy efficiency in the gray hole attack scenario.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network (MANET) is a group of low-power consumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization. The primary aim of MANETs is to extend flexibility into the self-directed, mobile, and wireless domain, in which a cluster of autonomous nodes forms a MANET routing system. An Intrusion Detection System (IDS) is a tool that examines a network for malicious behavior/policy violations. A network monitoring system is often used to report/gather any suspicious attacks/violations. An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks, sending out alerts whenever it detects malicious nodes. The impact of Dynamic Source Routing (DSR) in MANETs challenging blackhole attack is investigated in this research article. The Cluster Trust Adaptive Acknowledgement (CTAA) method is used to identify unauthorised and malfunctioning nodes in a MANET environment. MANET system is active and provides successful delivery of a data packet, which implements Kalman Filters (KF) to anticipate node trustworthiness. Furthermore, KF is used to eliminate synchronisation errors that arise during the sending and receiving data. In order to provide an energy-efficient solution and to minimize network traffic, route optimization in MANET by using Multi-Objective Particle Swarm Optimization (MOPSO) technique to determine the optimal number of clustered MANET along with energy dissipation in nodes. According to the research findings, the proposed CTAA-MPSO achieves a Packet Delivery Ratio (PDR) of 3.3%. In MANET, the PDR of CTAA-MPSO improves CTAA-PSO by 3.5% at 30% malware.

Protecting MANETs from Black and Gray Hole Attacks Through a Detailed Detection System

Protecting MANETs from Black and Gray Hole Attacks Through a Detailed Detection System

Black Hole Detection Using Modified Sequence Number in Vehicular Ad-hoc Network

Vehicular Ad-hoc Network (VANET) is a type of wireless network with Dedicated Short-Range Communication (DSRC) that enables communication between vehicles (V2V) and communication between vehicles to infrastructure around them (V2I). VANET has several security requirements to consider in order to maintain the network functionality. Availability is the most important security requirement due to its responsibility of maintaining the functionality of the network, attack on availability may cause the lack of availability and reduce the efficiency of VANET. One of the attack that threat the availability of VANET is black hole. In this paper, we address the problem of black hole attack in VANET, using Modified Sequence Number (MSN) as a detection method. The simulation is performed using NS-2 as a simulator and AODV as a routing protocol. Detection Rate (DR) and False Alarm Rate (FAR) are used to evaluate the performance of MSN algorithm in detecting black hole attack. Evaluation with variation in the number of CBR packets shows that MSN algorithm successfully detects black hole attacks with DR values reaching 69.0909% at 10 CBR packets and FAR values reaching 0.0037 at 20 CBR packets. We also evaluate the performance of MSN algorithm with variations of node density. The evaluation shows that MSN algorithm successfully detects black hole attack with DR values reaching 100% with a density of 10 and 20 nodes, with the percentage of FAR values reaching 0% in all numbers of node density.

Improvement of the Secure Integration of IoT and Cloud Computing using Hybrid Encryption

Wireless Sensor Network (WSN) is an essential technology in many Internet of Things (IoT) applications, and since sensor nodes suffer from limited resources, it has become possible to overcome storage capacity problems using cloud computing, the integration of Internet of Things (IoT) with cloud computing (CC) seeks to achieve new levels of efficiency in service delivery. Security and privacy are key factors that slow down the rapid and widespread adoption and deployment of both IoT and cloud computing. In the proposed model, an integrated IoT system with cloud computing was developed starting from the analysis, and design, to the implementation to connect IoT devices with the cloud, the security is achieved by using a hybrid encryption mechanism which provides the performance advantages of symmetric and asymmetric encryption algorithms. Where the Elliptic Curve Cryptography (ECC) algorithm is used for key generation and AES (Advanced Encryption Standard) algorithm is used for encryption and decryption of the sensors' data to provide a reliable computing environment. We have implemented the proposed system and showed the results of using CONTIKI COOJA 3.0 that connected with the cloud service provider, Evaluate a set of performance metrics such as power consumption, packet delivery ratio, and the algorithm execution time, in addition to verifying network immunity against the black hole attack.

Collaborative Detection of Black Hole and Gray Hole Attacks for Secure Data Communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques.