Gray Hole Attack Research Articles

Recognition of grey hole attacks in wireless sensor networks using fuzzy logic in IoT

AbstractSince the devices in Internet of Things (IoT) are always interconnected with a stable Internet connection, they are prone to attacks. In the grey hole attack, a malicious node acts as a central controller to obtain data from all the nodes and it drops and alters the data packets as per its wish. In this way, the grey hole attack alters the core concept of the IoT, which enables different devices to communicate with each other. To prevent the grey hole attack and enable efficient communication between the IoT devices, a fuzzy concept is introduced in this article. Previous methods have not proficient in spotting uncountable kinds of grey hole attacks. The fuzzy engine identifies the suspicious activity that takes place in the network by the rules generated and identifies the malicious node and stops its function immediately. The simulation experimentation is carried out for accuracy, delay, energy consumption, packet delivery ratio and throughput, and the simulation contrasts with the proposed algorithm, analog behavioral modeling (ABM), and other previous techniques. The proposed system provides an accuracy rate of 45%, a packet delivery ratio of 78%, and reduced energy consumption of 35.6% compared to the previous ABM approach. Simulation outputs showed that the proposed fuzzy grey detection technique is a proficient scheme for detecting grey hole attacks and improving network capability.

Implementation of Black hole Attack for Random Mobility for Single and Multiple Connection in MANET

Mobile Ad hoc network is a temporary network. It helps to communicate two or more devices for short range. Routing Protocols are use to establish a communication in MANET. As it is a open network , it has many vulnerabilities from security point. Black hole Attack is one of the major concern in MANET. In this paper, we have implemented Black hole Attack in random mobility environment and analyse its impact on MANET using various parameters for single and multiple connection in MANET. Black hole attack disturbs one of the connection in the network while remaining connection are unaffected. During our analysis, we found that performance result of black hole attack in multiple connection network give a similar kind of output mentioned in various research paper related to gray hole attack, which will make difficult to analysis the type of attack in the network.

Multi-level trust-based intelligence schema for securing of internet of things (IoT) against security threats using cryptographic authentication

The internet of things (IoT) is able to provide a prediction of linked, universal, and smart nodes that have autonomous interaction when they present services. Because of wide openness, relatively high processing power, and wide distribution of IoT things, they are ideal for attacks of the gray hole. In the gray hole attack, the attacker fakes itself as the shortest path to the destination that is a thing here. This causes the routing packets not to reach the destination. The proposed method is based on the AODV routing protocol and is presented under the MTISS-IoT name which means for the reduction of gray hole attacks using check node information. In this paper, a hybrid approach is proposed based on cryptographic authentication. The proposed approach consists of four phases, such as the verifying node trust in the IoT, testing the routes, gray hole attack discovery, and the malicious attack elimination process in MTISS-IoT. The method is evaluated here via extensive simulations carried out in the NS-3 environment. The experimental results of four scenarios demonstrated that the MTISS-IoT method can achieve a false positive rate of 14.104%, a false negative rate of 17.49%, and a detection rate of 94.5% when gray hole attack was launched.

Identifying the Impacts of Active and Passive Attacks on Network Layer in a Mobile Ad-hoc Network: A Simulation Perspective

In this research, we attempted to investigate about the features and behaviors of network layer based active and passive attacks in Ad-hoc On-Demand Vector (AODV) routing protocol in Mobile Ad-hoc Networks (MANET). Through the literature survey, we try to understand the features of each attacks and examine the behaviors of these attacks through simulations via Network Simulator 2 (NS2). Blackhole, Grayhole and Wormhole attacks are used in this simulation study. Each attacks are introduced independently into the network to find the impacts on network performances that are evaluated through Packet Delivery Ratio (PDR), Average End-to-End Delay (AEED), Throughput, Average Data Dropping Rate (ADDR) and Simula-tion Processing Time at Intermediate Nodes (SPTIN). To obtain more accurate results, simulation parameters are maintained same in each simulation. A controller network is simulated to compare with each attack simulation. Simulations are repeated by changing the number of connected intermediate nodes (hops) in the network. We observed at collected data analysis, the lowest SPTIN in the network that contained a Blackhole or Grayhole attack out of these three attacks. The network which is affected by a Blackhole attack shows higher amount of ADDR than controller network. Furthermore data forwarding rate is higher in the network which is affected by a Wormhole attack. Finally, according to the simulation studies, we are able to understand that Blackhole and Grayhole attacks cause more damage to the network performances than Wormhole attacks.

Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm With Artificial Neural Network

Wireless technology and the latest developments in a mobile object, has led to a Mobile Ad Hoc network (MANET), which is a collection of mobile nodes that are communicating with each other without requiring any fixed infrastructure. Due to the dynamic nature with a decentralized system, these networks are susceptible to different attacks such as Black Hole Attack (BHA), Gray Hole Attack (GHA), Sink Hole Attack (SHA) and many more. Several researchers have worked for the detection and mitigation of individual attacks, either GHA or BHA nodes. But the protection of MANET against a dual-threat is scarce. In this paper, the protection against dual attacks has been presented for BHA and GHA by using the concept of Artificial Neural Network (ANN) as a deep learning algorithm along with the swarm-based Artificial Bee Colony (ABC) optimization technique. The performance of the system has been increased by the selection of appropriate and best nodes for data packets transmission which is explained in the result section of this paper. For the network designing and simulation purposes, MATLAB software is used with communication and neural network toolboxes. The examined results show that the presented protocol performs better in contrast to the existing work under black hole as well as gray hole attack condition. A mobile ad hoc network (MANET) is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure.

Prevention of Attacks in Mobile Ad Hoc Network using African Buffalo Monitoring Zone Protocol

Mobile ad hoc networks (MANET) can be utilized for communicating wirelessly. However, MANET is affected by many attacks and malicious activities. In MANET, the prevention approach is necessary to secure communication. MANET is easily affected by numerous attacks such as wormhole (WH) attack, Grey-hole (GH) attack, and black-hole (BH) attack in which the sender hubs can’t able to transmits the message to the target node due to the malicious behavior. To prevent the attacks in MANET, this research introduces a novel routing protocol as African Buffalo Monitoring Zone Protocol (ABMZP). This approach is utilized for preventing wormhole attack and other malicious activities in MANET. This mechanism monitors the communication channel continuously and identifies the attack detection. Sequentially, the ABMZP approach prevents the harmful nodes and finds the alternate path for communication. The simulation of this research is done with the use of Network Simulator 2 (NS-2) and finally, the efficiency of the projected ABMZP work outcomes are compared with the latest existing techniques and provides superior results.

Real Time Vehicular Traffic Simulation for Black Hole Attack in the Greater Detroit Area

Vehicular Ad-hoc Networks (VANETs) technology has recently emerged, and gaining significant attention from the research because it is promising technologies related to Intelligent Transportation System (ITSs) and smart cities. Wireless vehicular communication is employed to improve traffic safety and to reduce traffic congestion. Each vehicle in the ad-hoc network achieves as a smart mobile node categorized by high mobility and forming of dynamic networks. As a result of the movement of vehicles in a continuous way, VANETs are vulnerable to many security threats so it requisites capable and secure communication. Unfortunately, Ad hoc networks are liable to varied attacks like Block Hole attacks and Grey Hole attacks, Denial of service attacks, etc. Among the most known attacks are the Black Hole attacks while the malicious vehicle is able to intercept the data and drops it without forwarding it to the cars. The main goal of our simulation is to analyze the performance impact of black hole attack in real time vehicular traffic in the Greater Detroit Area using NS-2 and SUMO (Simulation of Urban). The simulation will be with AODV protocol.

Application of an Artificial Neural Network for Detection of Attacks in VANETs

This work contains results of developing an approach for detecting routing attacks in VANET networks using an artificial neural network. In the course of this work, such research methods as analysis and modeling were used to select the most promising approach for identifying routing attacks, as well as to develop a mock-up of a software system that detects Gray Hole attacks in VANETs. According to the results of experimental studies, the effectiveness of the developed software was evaluated.

An intrusion detection system for the prevention of an active sinkhole routing attack in Internet of things

Internet of things (IoT) is a complex and massive wireless network, where millions of devices are connected together. These devices gather different types of data from different systems that transform human daily lives by modernizing home appliances, business, medicine, traveling, research, and so on. Security is a critical challenge for a stable IoT network, for instance, routing attacks, especially sinkhole attack is a severe attack which has the capability to direct network data toward the intruder, and it can also disrupt and disconnect the devices from their network. The IoT needs multi-facet security solutions where network communication is protected with integrity, confidentiality, and authentication verification services. Therefore, the IoT network should be secured against intrusions and disruptions; the data exchanged throughout the network should be an encrypted form. In this article, an intrusion detection system for the prevention of an active sinkhole routing attack (PASR) in IoT is presented. The proposed PASR solves the problem of the sinkhole attack; for this purpose, the whole network is divided into the clusters of IoT. All the IoT devices are connected to their respective gateways. The gateway devices are equipped with an intrusion detection system. The intrusion detection system activates intrusion analyzer to detect anomalies in the context of ad hoc on-demand distance vector protocol. The base station is the main device that is responsible to receive data from all devices. Therefore, it detects and prevents sinkhole attacks; the base station keeps the record of all active devices and their possible links. The PASR is implemented and compared with the existing intrusion detection techniques ad hoc on-demand distance vector, and dual attack detection for black and gray hole attack. It was observed from the simulation results that the PASR outperforms in terms of data packet delivery, energy consumption, the detection rate of sinkhole attack, and routing overhead.

Gray Hole Attack as a Byzantine Attack in a Wireless Multi-Hop Network

Gray hole attack gets trickier, when only certain node’s packets are dropped intentionally, termed as the Selective Gray Hole Attack (SGHA). The literature so far has focused on the attack carried out on random packets, without any particular criterion. SGHA has enough potential to disrupt the communication of a particular node with the outside world or confine the communication of a network within itself. This could be detrimental in military reconnaissance applications, disaster warning systems, autonomous cars, e-health systems, home automation, etc. Hence, we propose a Quality of Service (QoS) aware algorithm to detect and mitigate such attacks, performed by a rational adversary.

Secure Group based Routing and Flawless Trust Formulation in MANET using Unsupervised Machine Learning Approach for IoT Applications

INTRODUCTION: Mobile Adhoc Network (MANET) is integrated with Internet of Things (IoT) in many application cases due to its flexibility and scalability. The dynamic nature of MANET introduces some security threats in IoT environment. In those threats, Blackhole attack and Grayhole attack are severe routing attacks that disrupts routing algorithm to crack transmission in the entire network. OBJECTIVES: Many security mechanisms are introduced in MANET based on trust computation schemes. However, computation of inaccurate trust value degrades the performance of mitigation schemes. Thus the major objective of this work is to design a novel security mechanism to protect the MANET-IoT from different adversaries. METHODS: in this paper we propose a novel group based routing algorithm with recommendation filtering supported by security monitors (SMs). Unsupervised machine learning algorithm is adapted for the purpose of recommendation filtering in the network. Initially the entire network is grouped by Secure Certificate based Group Formation (SCGF) algorithm. In each group, Recommendation Filtering by K-means algorithm (RF-K means) algorithm is employed to perform trust computation. For secure and optimal route selection, hybrid optimization algorithm that combines Genetic Algorithm and Fire Fly Algorithm (GA-FFA) is proposed. Data transmission is protected by novel Hash Message Authentication Code with Advanced Encryption Standard (HMAC-AES) algorithm in which hash function is integrated with cryptography function. RESULTS: The proposed work is validated in network simulator-3 environment and the obtained results show better performance in terms of packet delivery ratio (96.3%), throughput (135kbps), delay (3.26ms), detection rate (99%), and energy consumption (8.5%). CONCLUSION: The MANET-IoT network is secured by group formation and trust filtering approaches. Further, involvement of cryptography function ensures data security whereas hash function ensures data integrity.

An Effective Secure Mechanism using Fuzzy Hybrid DCR Technique for MANET

Due to various Denial-of-Service (DoS) attacks like blackhole and grayhole attacks, Mobile Ad-Hoc Networks (MANET) performance is degraded rapidly. These attacks have been detected and prevented separately by different techniques. In earlier research, hybrid black/grayhole attack detection was proposed in which blackhole and grayhole attacks were detected and prevented simultaneously based on the detection threshold. However, some malicious nodes are still present in the network by faking the threshold value and forwarding the fake message to the other nodes. Therefore, the hybrid black/grayhole attack detection is enhanced by integrating network metric measurements. In this paper, the Data-to-Control packet Ratio (DCR) is measured for removing malicious nodes from the network and also avoiding the false detection. In addition, fuzzy-based mobility and traffic measurement is integrated with a hybrid DCR detection technique for removing malicious node links. Moreover, the optimal path for packet transmission is selected by measuring the queue delay based on fuzzy logic optimization. Finally, the efficiency of the proposed hybrid blackhole/grayhole attack detection technique is illustrated through the simulation results based on the throughput, packet drop rate, packet delivery ratio and routing overhead.

Fuzzy logic determining multi-paths in Gray hole attack for improving the energy efficiency of sensor Networks

Fuzzy logic determining multi-paths in Gray hole attack for improving the energy efficiency of sensor Networks

Performance Examination of Black Hole and Gray Hole Attacks in MANETs

Security is a very difficult issue for ad hoc ad networks. The first step is to developa good security solution to understand the current attack method.The presence of malicious nodeswill affect the functionality and network integrity. In the Black hole attack, malicious nodes will lower the package instead of moving forward. Thus, Black hole attacks reduce networkperformance. Utimetly as a black hole this paper analysis over other attacks individually or inintegrated manner. It is quite easy to verify the behaviors of black hole attack on individual basis.But in case of gray hole attack it is too much difficut to analysis the nature and behaviors of the network. The quality of tarnsmision is unpredictable and the overall performance get badlyaffcted with the such kind of attack. These attacks is the basic of the earlier known attack butwhen it come in pair or more than its behaviorss is very hard to detected on the basis of thepresent knwledge. In this paper we analyse blackhole and Grayhole attack s that executed underthe NS2 plateform run on the linux operating system. The analysis formwithsome set of nodesand whole excution focused on three parameters i.e E2E, PDR and Throughput

Node pattern state and trust-rate base route selection for reliable data transmission in mobile ad hoc networks

Mobile ad hoc networks (MANET) have plethora of security issues related to its dynamic topology. Black-hole and gray-hole attacks are more commonly impacting issues in the data transmission between nodes in the selected route. This manuscript addresses the proposal of node pattern state and trust-rate (PSTR)-based reliable data transmission in MANET. The method PSTR allows detecting optimal route without the nodes compromised to block-hole and gray-hole attacks. The model is proposed as an extension to the earlier proposed model trust-rate-based cooperative neighbor selection for optimal routing in MANET. The proposed solution scales the optimality of the routes. The test results from the experimental study have shown that the results are very effective when compared to those of the contemporary models. The proposed model focuses on improving the scalability and detection of black-hole and gray-hole attacks under very minimal computational costs.

Mitigating Gray Hole attack in Mobile AD HOC Network using Artificial Intelligence Mechanism

A mobile ad hoc network (MANET) is a combination of multiple mobile nodes, which are interconnected by radio link. In MANET, sensor nodes are free to move, and each node can act as a host or router. Routing is one of the most challenging tasks because nodes move frequently. Therefore, in MANET, the routing protocol plays an important role in selecting the best route to efficiently transmit data from the source node to the destination node. In this paper, the best path with efficient Ad Hoc on Demand Distance Vector (AODV) routing protocol is chosen as the routing mechanism. The properties of each node are categorized using firefly algorithm. The Artificial Neural Network (ANN) is trained as per these properties and hence in case if the gray hole node is detected within the route, it is identified and the route between the source and the destination is changed. At last, to show how effectively the proposed AODV with Firefly and ANN works is computed in terms of performance parameters. The throughput and PDR is increased by 4.13 % and 3.15 % compared to the network which is affected by gray hole attack. The energy up to 44.02 % has been saved.

Design and development of new framework for detection and mitigation of wormhole and black hole attacks in VANET

VANET is the future of network technology. Due to its characteristics it does possess threats and one of those threats is its safety. The issues of safety in VANET are discussed several times over. Many researchers are working on increasing the security in VANET. However, still it possesses threats which can’t be overlooked such as Black Hole and Gray Hole Attack. Black hole attack is an attack where the node behave selfishly and will not forward the packets further but in Gray hole attack node dropped the packets and does not raise any indication that’s why it is not easy to find. If we look further these attacks have same concept and no work is done till now on the combine study of these network. In this paper we have implement the AODV, FA-AODV, BH-AODV and WH-AODV routing protocol and simulate the attack in different scenario. We have evaluated the performance of the different routing protocol with the PDR, End to End delay, throughput and other parameter. Result show that the PDR of FA-AODV protocol is lower than BHAODV and WH-AODV protocol

Detection Mechanisms for Gray-hole Attacks in MANET and their Limitations to Overcome Smart Gray-hole Attack : A Review

Detection Mechanisms for Gray-hole Attacks in MANET and their Limitations to Overcome Smart Gray-hole Attack : A Review

AN ANALYSIS ON COOPERATIVE ATTACKS IN MANETS

This paper presents a review on a major category of organized attacks i.e. cooperative blackhole / grayhole attack that are a serious threat to adhoc network security. In cooperative blackhole attack multiple nodes collude to hide the malicious activity of other nodes; hence such attacks are more difficult to detect. In this paper a survey of various security mechanisms have been proposed in the literature for diction of such attacks is presented.

Identification and Prevention of Joint Gray Hole and Black Hole Attacks

Ad-hoc networks consist of a set of autonomous communicating devices that can communicate with each other by establishing multi-hop radio connections, and these connections are maintained in a localized manner. In these types of networks, especially where the nature of communicating nodes is mobile, e.g., MANETs, maintaining security remains a serious challenge due to their wireless, open, and shared communication medium, reliance on cooperative algorithms, dynamically adaptable topologies, an absence of centralized watching points, etc. Most of the existing protocols, utilized for routing in MANETs are susceptible to diverse varieties of attacks. However, it is also susceptible to the well-known gray and black hole attacks. In these types of attacks, malicious nodes are incorrectly advertised as sensible ways to a destination node throughout the route discovery method. This attack becomes complicated when a bunch of malicious nodes are acting together. In this work, a novel mechanism is introduced to identify the malicious node or nodes that have tried to perform malicious activity. Here, identification is made by their increased data routing information (DRI).