Attacks In Wireless Networks Research Articles

The Optimization Method of Wireless Network Attacks Detection Based on Semi-Supervised Learning

The Optimization Method of Wireless Network Attacks Detection Based on Semi-Supervised Learning

A Modular IoT Hardware Platform for Distributed and Secured Extreme Edge Computing

The hardware of networked embedded sensor nodes is in continuous evolution, from those 8-bit MCUs-based platforms such as Mica, up to powerful Edge nodes that even include custom hardware devices, such as FPGAs in the Cookies platform. This evolution process comes up with issues related to the deployment of the Internet of Things, particularly in terms of performance and communication bottlenecks. Moreover, the associated integration process from the Edge up to the Cloud layer opens new security concerns that are key to assure the end-to-end trustability and interoperability. This work tackles these questions by proposing a novel embedded Edge platform based on an EFR32 SoC from Silicon Labs with Contiki-NG OS that includes an ARM Cortex M4 MCU and an IEEE 802.15.4 transceiver, used for resource-constrained low-power communication capabilities. This IoT Edge node integrates security by hardware, adding support for confidentiality, integrity and availability, making this Edge node ultra-secure for most of the common attacks in wireless sensor networks. Part of this security relies on an energy-efficient hardware accelerator that handles identity authentication, session key creation and management. Furthermore, the modular hardware platform aims at providing reliability and robustness in low-power distributed sensing application contexts on what is called the Extreme Edge, and for that purpose a lightweight multi-hop routing strategy for supporting dynamic discovery and interaction among participant devices is fully presented. This embedded algorithm has served as the baseline end-to-end communication capability to validate the IoT hardware platform through intensive experimental tests in a real deployment scenario.

DSLA: Defending against Selective Forwarding Attack in Wireless Sensor Networks using Learning Automaton

Selective forwarding attacks (SFAs) can harm the mission of critical applications such as military surveillance and forest fire monitoring. In these attacks, malicious nodes behave like normal nodes most of the time, but selectively drop sensitive packets, such as a packet that reports on the movement of opposing forces, and therefore, detection of this kind of attack is hard. In this paper, a fully distributed, dynamic, intelligent, lightweight algorithm based on learning automata is proposed in order to defend against the selective forwarding attack. In this algorithm, an overhearing mechanism, along with the learning automata model, is used to select secure routes for forwarding packets in a multi-hop routing algorithm. Each node is equipped with a learning automaton, which helps the node select the next hop for forwarding its data towards the base station. The proposed algorithm is simulated using J-SIM. Simulation results show the superiority of the proposed algorithm over existing algorithms, such as the single path forwarding algorithm, the multi-hop acknowledge–based algorithm, the multi-data–flow algorithm, the multi-path algorithm, and the neighbor watch system–based algorithm, in packet delivery rate, packet drop rate by malicious nodes, communications overhead, and energy consumption.

Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems.

This paper considers the state estimation problem of intelligent connected vehicle systems under the false data injection attack in wireless monitoring networks. We propose a new secure state estimation method to reconstruct the motion states of the connected vehicles equipped with cooperative adaptive cruise control (CACC) systems. First, the set of CACC models combined with Proportion-Differentiation (PD) controllers are used to represent the longitudinal dynamics of the intelligent connected vehicle systems. Then the notion of sparseness is employed to model the false data injection attack of the wireless networks of the monitoring platform. According to the corrupted data of the vehicles’ states, the compressed sensing principle is used to describe the secure state estimation problem of the connected vehicles. Moreover, the L1 norm optimization problem is solved to reconstruct the motion states of the vehicles based on the orthogonaldecomposition. Finally, the simulation experiments verify that the proposed method can effectively reconstruct the motion states of vehicles for remote monitoring of the intelligent connected vehicle system.

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.

A simple, lightweight, and precise algorithm to defend against replica node attacks in mobile wireless networks using neighboring information

Node replication attack (also known as replica attack) is one of the common attacks in Wireless Sensor Networks (WSNs). In this attack, the adversary captures the sensor node(s) and extracts all important information, properties, and functions. Then, it disrupts the network operation by generating several malicious nodes having the same ID(s) as that of the captured node(s). Since the malicious nodes (i.e., replica nodes) have valid keying material, they can intentionally interfere with the network operation. Motivated by this, a three-step mechanism is proposed to detect replica nodes. The proposed mechanism benefits from watchdog nodes and is based on the idea that all nodes in the network (which are identified by their unique IDs) should have the same chance to be met by watchdog nodes. Accordingly, watchdog nodes monitor the network traffic and silently overhear the channel to see if there is any node with an abnormally higher chance of being met. If so, the ID of the node is advertised as the replica node.To evaluate the performance of the proposed mechanism in terms of probability of detecting replica nodes and false detection probability, a comprehensive set of simulations has been carried out using the J-SIM simulator. The simulation results reveal that the proposed mechanism detects almost all replica nodes while the false detection probability is below 0.005%. It outperforms state-of-the-art protocols with imposing ignorable processing and memory overhead.

A Machine-Learning-Based Cyber Attack Detection Model for Wireless Sensor Networks in Microgrids

In this article, an accurate secured framework to detect and stop data integrity attacks in wireless sensor networks in microgrids is proposed. An intelligent anomaly detection method based on prediction intervals (PIs) is introduced to distinguish malicious attacks with different severities during a secured operation. The proposed anomaly detection method is constructed based on the lower and upper bound estimation method to provide optimal feasible PIs over the smart meter readings at electric consumers. It also makes use of the combinatorial concept of PIs to solve the instability issues arising from the neural networks. Due to the high complexity and oscillatory nature of the electric consumers’ data, a new modified optimization algorithm based on symbiotic organisms search is developed to adjust the NN parameters. The high accuracy and satisfying performance of the proposed model are assessed on the practical data of a residential microgrid.

Secure co-operative neighbour-based approach for detection and prevention of black hole attack in wireless mobile ad-hoc networks

Mobile ad-hoc networks are infrastructure-less, temporary networks which are exposed to routing attacks. Black hole attack is a routing attack formed by dropping all the received packets by a malicious node. To ensure the security and strength of MANETs a secure and effective mechanism is obligatory. This paper presents Secure Cooperative Neighbour-Based Approach in AODV to perceive and circumvent the black hole attack from the start of the network. This approach enables every node to exchange information with its neighbour using a secure, efficient and quick detection process against routing attacks by taking an alternative route on finding an attacker. This approach is able to avoid the black hole nodes and improve the performance of the system as observed in the simulations under ns2.

Cluster based Detection and Reduction Techniques to Identify Wormhole Attacks in Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSN) is widely used in variety of applications but none of the applications have taken network security into considerations. Deployment of underwater network is a challenging task and because of the hash underwater environment, the network is vulnerable to large class of security attacks. Recent research on underwater communication focuses mainly on energy efficiency, network connectivity and maximum communication range. The nature of underwater sensor network makes it more attractive for the attackers. One of the most serious problems in underwater networks is wormhole attack. In this research work we concentrate on providing security to the underwater network against wormhole attacks. We introduce the wormhole attack in the network and propose a solution to detect this attack in underwater wireless networks. Energy Efficient Hybrid Optical - Acoustic Cluster Based Routing Protocol (EEHRCP) is incorporated and using the round trip time and other characteristics of wormhole attack, the presence of the wormhole attack in the network is identified. The simulation results depicts that the proposed wormhole detection mechanism increases throughput by 26%, reduces energy consumption by 3%, reduces end to end delay by 13% and increases packet delivery ratio by 3%.

Secure co-operative neighbour-based approach for detection and prevention of black hole attack in wireless mobile ad-hoc networks

Mobile ad-hoc networks are infrastructure-less, temporary networks which are exposed to routing attacks. Black hole attack is a routing attack formed by dropping all the received packets by a malicious node. To ensure the security and strength of MANETs a secure and effective mechanism is obligatory. This paper presents Secure Cooperative Neighbour-Based Approach in AODV to perceive and circumvent the black hole attack from the start of the network. This approach enables every node to exchange information with its neighbour using a secure, efficient and quick detection process against routing attacks by taking an alternative route on finding an attacker. This approach is able to avoid the black hole nodes and improve the performance of the system as observed in the simulations under ns2.

Hybrid Encryption Algorithm to Detect Clone Node Attack in Wireless Sensor Network

Wireless sensor network (WSN) is the collection of sensor nodes which are distributed in a specific environment. WSN is resource-controlled network which is vulnerable to physical attacks i.e. clone node attack. An attacker acquires information from captured node and then creates the cloned node in the WSN. The main purpose of the cloned nodes is to control the entire network and increase the invasion in the sensor network. When communicating from legitimate node to other node, the message is not secure, so an attacker can modify it. Current asymmetric encryption methods and symmetric encryption methods can provide a level of security, but there are some limitations in it. The hybrid encryption method combining the asymmetric and symmetric methods which will offers high security with minimal key management. We have proposed a new hybrid secure encryption method in this paper which combines the asymmetric and symmetric cryptography method. The proposed efficient algorithm to protect communication from clone node attacks in wireless sensor network. The hybrid algorithm uses advanced cryptographic standards AES, BSDN and Dual RSA encryption techniques and MD5 hash functions. In this algorithm, message is divided into three parts and BSDN, AES and Dual RSA encryption are parallel implemented to the sub-message. The proposed algorithm is analyzed based on parameters such as encryption, decryption and execution time. The result shows that proposed encryption algorithm works better in terms of encryption and decryption time.

A Direct Discrimination Packet Flow Based Improving Security against Reactive Traffic Attacks in Wireless Communication

The wireless networks is most difficult of selective filling attacks. If jammer localizations and resistance routing are left alone, both are very promising, and the service overhead is still below the real-time requirements. To propose a new Direct Discriminant Packet Flow Exploration [DDPFE] algorithm based on network utility maximization (NUM) to resolve the centralized reaction disturbance optimization problem in multi-source network without any lose to send the data. The impact of the networks is estimated through interference and combine these estimates with the ability to assign problems in this type of attacks. To overcome this type of physical-layer characterization of cryptographic primitives attacks using a Cooperative Crypto Riddle Hiding Algorithm (CCRHA) for the control channel jamming problem in-network, which takes advantage of the transfer the data using the Ad-hoc network. The resolution to detect the schema attacks and isolate the nodes for the threshold. CCRHA Presenting to find selectively invasive attackers in wireless networks. Multiple metrics are measured to detect areas of interference of the wireless network. Multi-measurement method considered Packet Delivery Ratio (PDR) and signals strength variation as parameters to detect the selective jamming attacks.

An Improved Authentication Protocol for Wireless Body Sensor Networks Applied in Healthcare Applications

Wireless body sensor networks are now very popular for modern healthcare applications like health monitoring, remote healthcare, emergency healthcare etc. This healthcare application mainly used for constant monitoring of health specific data while doing regular activities. The wireless body area network is consisted of small wearing sensors which are implanted in human body for capturing medical data and send to a medical server through a network connector to base station. A major issue is secure transmission of patient’s medical healthcare information to the medical server through wireless communication. Also the data collected in this network is very sensitive because on the basis of this clinical data, further treatment will be occurred. Therefore, security requirements such as confidentiality, integrity, authenticity should be guaranteed during communication. This paper proposes a strong mutual authentication protocol based on public key cryptography for satisfying all security requirements. The proposed authentication approach resists the major vulnerable attacks in wireless body sensor networks with low computational and communicational load.

A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor Networks

It is well known that the safety, reliability and energy efficiencies are the prime concerns while designing and deploying wireless sensor networks. Therefore, unattained features and inability to recharge the nodes makes wireless sensor networks more susceptible to attack. To successfully deploy efficient usage of power resources, sensor networks from time to time keep nodes to sleep. They awake when some activity is noticed. MAC protocols playa very crucial role to save the power consumption during communication. Attacker is not adhering the communication protocols by taking the advantage of unattended scenario. The communication protocols are designed in such a manner that they are assuring the optimal consumption of battery or other power sources. By indulging the nodes in unnecessary communication adversary applies the repudiation of sleep attack by applying repudiation of service attack. This work implements the effect of denial of service attack which leads to the denial of sleep attack in wireless sensor networks using support vector machine learning. To achieve better result, model is developed and tested with the help of various kernel functions (linear, sigmoidal and redial functions). After the intensive execution of experiments, final outcomes have been drawn. On the basis of achieved results and validation of model we can conclude that the developed system performs outstanding throughput for detecting denial of sleep strike attack in WSN.

An Enhanced Trust Mechanism with Consensus-Based False Information Filtering Algorithm against Bad-Mouthing Attacks and False-Praise Attacks in WSNs

To defend against insider attacks in wireless sensor networks (WSNs), trust mechanisms (TMs) using the notion of trust in human society have been proposed and are still actively researched. In the WSN with a trust mechanism (TM), each sensor node evaluates the trustworthiness of its neighbor sensors based on their behaviors, for example packet forwarding, and collaborates only with trustworthy neighbors while removing untrustworthy neighbor from its neighbor list. The reputation system (RS) is an advanced type of trust mechanism that evaluates the trustworthiness of a node by additionally considering neighbor nodes’ observations or evaluations about it. However, intelligent inside attackers in WSNs can discover the security vulnerabilities of trust mechanisms by examining the operations of TM (or RS), because the software modules of the TM (or RS) are installed and operating in their local storage and memory, and thus, they can avoid detection by the trust mechanisms. Bad-mouthing attacks and false-praise attacks are well-known examples of such intelligent insider attacks. We observed that existing trust mechanisms do not have effective countermeasures to defend against such attacks. In this paper, we propose an enhanced trust mechanism with a consensus-based false information filtering algorithm (TM-CFIFA) that can effectively defend against bad-mouthing attacks and false-praise attacks. According to our experiment results, compared with an existing representative RS model, our TM-CFIFA shortened the detection time of a packet drop attacker, which is supported by a false-praise attacker by at least 83%, and also extended the lifetime of a victim sensor node that is under bad-mouthing attacks by at least 15.8%.

On Jamming and Black Hole Attacks in Heterogeneous Wireless Networks

The proliferation of radio-enabled equipment, including sensors, controllers, actuators, mobile handhelds, etc., into the today's heterogeneous wireless network (HWN) has led to the support of different vertical services on-top of the same physical infrastructure. Such an approach increases the potential for enhanced multiplexing gains due to the joint utilization of multiple network tiers and their resources. However, it also links the availability of new services to the robustness of the HWN infrastructure against malicious attacks. In this paper, we study the impact of two prominent denial of service (DoS) attacks, the jamming and the black hole attack, on network coverage of multi-tier HWNs. In particular, we consider a HWN model of multiple tiers, where the locations of nodes belonging to each tier are modeled by a homogeneous Poisson point process (PPP) with known intensity. Each tier consists of regular nodes, jammers (disrupting downlink communications by transmitting at constant power), and black holes (appearing as regular nodes but without forwarding received packets to their legitimate destination). The type of a node for a given network tier is determined by a prescribed probability, whereas jammer and black hole nodes are considered to act independently from each other. We further focus on the challenging scenario where end users can access a limited number of network tiers, while we also allow different tiers to potentially utilize different spectrum bands. Assuming that successful service reception from a node belonging to a given network tier requires a minimum received signal quality threshold to be attained, we derive exact expressions and performance bounds on the coverage probability of random multi-tier HWNs with joint jamming and black hole attacks, which depend on the capability of network nodes to detect and avoid association with malicious nodes (i.e., jammers or black holes). Detailed numerical results highlight the usefulness of the proposed analysis, providing valuable insights on system design and parameterization towards enhanced network robustness.

A Bayesian game model and network availability model for small cells under denial of service (DoS) attack in 5G wireless communication network

Nowadays small cells have become the integral part of 5G wireless communication network (WCN). However, the incorporation of small cells in 5G WCN creates chances for attackers to intrude into the network and this makes the network highly susceptible to attacks. Among various possible attacks, denial of service (DoS) attack is one of the most common network security attacks, and hence in this paper we consider a bandwidth spoofing attack which is a type of DoS attack that is carried out by an attacker during the communication between a macro base station and a small cell base station in a 5G WCN. We then propose a Bayesian game model to capture the interactions between the attacker and the network defender under the bandwidth spoofing attack and obtain the best strategies for defending the DoS attack. Further, since the DoS attack entails unavailability of the network, we address this issue by developing an availability model of the 5G WCN based on a stochastic reward net. Numerical results are presented which give insights into how the availability is affected by the DoS attack. The proposed model is also validated via simulation.

An efficient trbl-cdrrp method to detect jamming attacks in multichannel multiradio wireless networks

Wireless network is a flexible and scalable data communications system, which uses electromagnetic waves or infrared waves to broadcast data and receive data from one end to another without use physical link. Limited processing speed, energy efficiency, limited memory power, and many more constraints are affecting wireless networks. In these constraints security is a main concern. Protecting wireless networks from different kinds of attack is not a simple task, as the security violates are varies. To recognize this difficulty, we have to discuss and evaluate different methods for jamming in multi channel multi-radio wireless networks. Jamming attacks in wireless networks is defined as by minimizing the noise ratio at receiver side by the way of transmission of interfering wireless signals. In this discussion Transmission Range Based Localization-Constant Deceptive Random Reactive Protocol (TRBL-CDRRP) is proposed for detecting the different types of jamming attacks in multichannel multi-radio wireless networks. Also the suggested technique is utilized to find location and transmission range for every node. The performance metrics like Packet Delivery Ratio (PDR), End-to-End delay, Security, and throughput are to be resolute and also related with the existing algorithms. The execution is done in a NS-2 simulator.

Preventing & Isolating Distributed Denial of Service (DDOS) attack in Wireless Mesh Networks (WMN's)

Wireless Mesh networks (WMN’s) are prone to a number of attacks & these attacks compromise the security of these networks. Attaining security in these networks is a challenging task. It is logical to consider that there are many types of scripts in the internet. The virus can either be a key logger or somebody else's mischief. With this script we can steal any information. Since the existence of virus cannot be ignored, therefore the authors have tried to present their work on first detecting it and later on fixing it. With the help of different protocols present in the Application Layer, a hacker takes information out of the script. The authors have used Covert Channel, which has been mentioned in many essays. Now with the help of this channel, the information will go to all and it will not go to any of the informatics. This research proposal envisions a methodology to first detect the selfish node in the network & later on provides a technique for mitigation of the same.NS2 simulator has been used to simulate & analyze the performance of our proposed methodology for Open Shortest Path First (OSPF) protocol in WMN’s.

A light-weight solution for blackhole attacks in wireless sensor networks

A light-weight solution for blackhole attacks in wireless sensor networks