Machine Learning Based Detection and a Novel EC-BRTT Algorithm Based Prevention of DoS Attacks in Wireless Sensor Networks

Wireless network with a merit of sensing and processing information is known as wireless sensor network (WSN). It consists of small sensor nodes with transducer, battery, microprocessor along with storage media. It is economical and simple solution for a variety of applications. Open nature of WSN leads it to defenseless for various security threats. Several security attacks black hole, wormhole attack, DDOS attack etc. are possible to compromise the information and sensor node in network. Distributed Denial of Service (DDOS) attack is such kind of attack which aims to disrupt the network by draining resource capability. Attacker not only sends worthless messages to increase network traffic but also degrades the life of node and network. In WSN life of network is directly proportional to battery capacity. Thus draining in battery energy directly degrades the life of node. This work observed it as severe problem and proposed a solution to overcome the problem of power draining due to DDOS attack. Qualnet 5.0 simulator has been used to simulate and evaluate the performance of proposed solution for AODV and DSR routing protocols in WSN.

Objectives: The main aim of this paper is security by applying Intelligent Intrusion detection System to detect one of the critical attack know as sinkhole attack in Wireless Sensor network. Methods/Analysis: The major problem occurs while transmitting the data is security. The malicious node attracts the packets from the other normal sensor nodes and drops the sensitive packets which may leads to selective forwarding attack and black hole attack. Findings: In the existing system, Leader based intrusion detection system is used to monitor the surrounding node. Here the leader can monitor the nodes only region wise and does not have any clustering technique. By using the clustering technique we can monitor the surrounding nodes in efficient manner and better solution. In the proposed system, an Intelligent Intrusion Detection System (IIDS) mechanism to detect the intruder in the network, which uses a Hybrid Approach to detect and prevent sinkhole attack, with two approaches such as Zone-Based Leader Election Method and Leader Based Monitoring. In Zone-Based Leader Election method, a set of nodes is located in different regions and a Cluster Based Zone Leader (CBZL) is allocated to every region in the network. In the Leader Based Monitoring Approach, when a node gets detected as a compromised node, it communicates the status of that node to the other leader within the WSN. Application/Improvement: This technique can identify the intruder node and improve the efficiency of the network. As a simulation results, we are implementing in Network Simulator. In environment monitoring and military applications the proposed approach can be deployed.

Wireless Sensor Networks (WSNs) are prone to various attacks in which Blackhole a kind of Denial of Service (DoS) attack is very difficult to detect and defend. In blackhole attack, an intruder captures and re-programs a set of nodes in the network to block the packets they receive instead of forwarding them towards the base station. As a result any information that enters the blackhole region is captured and not able to reach destination causing high end-to-end delay and low throughput. Previously little amount of work is done for detection and prevention of the Blackhole attack in the WSN making its detection and prevention very crucial as per network performance is concerned. In this paper initially the affect of Blackhole attack is measured on the network parameters followed by the proposal of a novel technique for the detection and prevention of Blackhole attack in WSN.

In today’s world, one of the most severe attacks that wireless sensor networks (WSNs) face is a Black-Hole (BH) attack which is a type of Denial of Service (DoS) attack. This attack blocks data and injects infected programs into a set of sensors in a group to capture packets before reached to the target. Therefore, raw data in the BH region is thwarted and is unable to reach its destination. The network is susceptible to various types of attacks as it is accessible to all types of users and minimizing the energy depletion without compromising the network lifetime is an NP-hard problem. Even though numerous protocols came into effect to overcome the BH attack and to enhance the security of packet delivery in WSNs, Simulated Annealing Black-hole attack Detection (SABD) based Enhanced Gravitational Search Algorithm (EGSA) is yet another implemented strategy to reduce the BH attacks. EGSA-SABD detects and isolates the BH infectors in WSNs. Initially, sensor nodes are hierarchically clustered using similar residual energy to reduce energy consumption. Then, the BH attack possibility in a deployed node is evaluated to find the existence of BH nodes in the region. In the end, EGSA-SABD is employed to detect and quarantine BH attackers in WSNs. The performance of EGSA-SABD is evaluated with certain metrics such as BH attack detection probability rate (BHatt_Prate), energy consumption (Ec), Duration of BH attack detection (Attduration), Packet delivery ratio (Pdr). Based on the experimental observations, the EGSA-SABD outperforms the BHatt_Prate by 13% and also reduces the energy consumption by 21%.

One of the most common attacks against Wireless Sensor Networks is the wormhole attack. In this attack, the enemy deploys two malicious nodes in two different areas of the network and establishes a high-speed dedicated channel between these two. This will cause the normal nodes in two different areas wrongly think that they are two-hop neighbors. Therefore, this attack will greatly affect the routing algorithms. In this paper, a new distributed algorithm is provided to deal with the wormhole attack. The main idea of the proposed algorithm is to discover separate routes between pairs of two-hop neighboring nodes. The proposed algorithm was implemented and evaluated in terms of true and false detection rate by performing a series of experiments and the results were compared with the base algorithm. The test results showed that the proposed algorithm has desirable efficacy.

Wireless sensor networks (WSNs) are novel large-scale wireless networks that consist of distributed, self organizing, low-power, low-cost, tiny sensor devices to cooperatively collect information through infrastructure less wireless networks. These networks are envisioned to play a crucial role in variety of applications like critical military surveillance applications, forest fire monitoring, commercial applications such as building security monitoring, traffic surveillance, habitat monitoring and smart homes and many more scenarios. Node capture attack is one of the most dreadful security attack exist in wireless sensor networks. An adversary steals cryptographic key or other confidential information like node’s id etc from a captured node to compromise entire network. So, Security of wireless sensor network is an important issue for maintaining confidentiality and integrity of wireless links. Now-a-days, researchers are paying attention towards developing security schemes against Node capture attack. Our survey provides deep insights of existing techniques that enhance the attacking efficiency of the node capture attack in wireless sensor network. It also analyzes various detection and key predistribution schemes for inventing a new scheme to improve resilience against node capture attack.

Wireless sensor networks have been widely applied in many areas due to their unique characteristics. These have exposed them to different types of active and passive attacks. In the literature, several solutions have been proposed to mitigate these attacks. Most of the proposed solutions are too complex to be implemented in wireless sensor networks considering the resource-constraint of sensor nodes. In this work, we proposed a hierarchical trust mechanism based on clustering approach to detect and prevent denial of service attacks in wireless sensor networks. The approach was validated through simulation using Network Simulator (NS2). The following metrics were used to evaluation the proposed scheme: packet delivery ratio, network lifetime, routing delay, overhead, and number of nodes. The proposed approach is capable of detecting compromised sensor nodes vulnerable to a denial of service attacks. Moreover, it is able to detect all sensed data that have been compromised during transmission to the base station. The results show that our method can effectively detect and defend against denial of service attacks in sensor wireless sensor networks.

Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments.

As wireless communication networks continue to expand, the need for protection against attacks on the routing of these networks, especially Blackhole attack, has increasingly been recognized as one of the most critical needs of the era. This research involves detecting and classifying Blackhole attacks in wireless sensor networks using different machine learning algorithms. The labeled dataset was then built using normal and Blackhole traffic, and a comparative analysis of four classification models was made: Decision Tree, Random Forest, Logistic Regression and K-Nearest Neighbors. The proposed models exhibit high accuracy, as demonstrated by experimental results, with the Decision Tree classifier outperforming all others with an accuracy of 99.9981% and an F1 score of 0.9997. The F1 scores of 0.9995 and 0.9990 for the Random Forest and Logistic Regression models also indicate excellent performance. In comparison, despite being effective, K-Nearest Neighbors performance was slightly lower at an F1 score of 0.9510. The error rate is also clearly shown in the confusion matrix, which for the very best models includes zero false negatives and only 3 false positives! Overall, decision tree-based approaches have been able to classify Blackhole attacks with a high level of accuracy and robustness while keeping false classifications to a minimum. This paper also facilitates automated and intelligent intrusion detection systems that benefit the security of wireless networks.

Wireless Sensor Network (WSN) technology is progressing at a rapid pace and its demand is also increasing massively due to its cost-effective nature and less-challenging installation demands. However, due to its vulnerable wireless environment, WSNs are prone to various types of severely damaging attacks like wormhole, sinkhole, Denial of Service (DoS) etcetera. Therefore, it requires to be more secure and confidential to maintain the productive flow of communication between the nodes. One of the greatest threats which if placed strategically can seriously impair the network is the wormhole attack. In this paper, we proposed a cluster based technique to detect and protect WSN from wormhole attacks. The entire network is divided into clusters eventually at the end and each cluster is governed by a cluster head (CH). Based on Round Trip Time and hop count the security of the network has enhanced and nullified the wormhole attack in the network. A significant improvement in throughput, PDR, Delay has been observed and reported with proposed scheme.

Ubiquitous and pervasive applications, where the Wireless Sensor Networks are typically deployed, lead to the susceptibility to many kinds of security attacks. Sensors used for real time response capability also make it difficult to devise the resource intensive security protocols because of their limited battery, power, memory and processing capabilities. One of potent form of Denial of Service attacks is Wormhole attack that affects on the network layer. In this paper, the techniques dealing with wormhole attack are investigated and an approach for wormhole prevention is proposed. Our approach is based on the analysis of the two-hop neighbors forwarding Route Reply packet. To check the validity of the sender, a unique key between the individual sensor node and the base station is required to be generated by suitable scheme.

The black hole attack is an adverse issue in Wireless Sensor Networks (WSNs). Research for detection and circumvention of the black hole attack is underway. However, the false alarm rate, amount of time required to identify the black hole attack nodes in network has not reduced. To overcome such limitations, the Jensen–Shannon Divergence Based Independent Component Analysis (JDICA) technique is proposed in this paper. This technique is introduced with the application of Jensen–Shannon Divergence estimation in Independent Component Analysis model on the contrary to existing works, in order to achieve higher black hole detection accuracy in healthcare WSN. The JDICA technique identifies the black hole attack by analyzing the physiological data gathered from biomedical sensors. The proposed JDICA technique carries out attack detection based on sensor nodes behaviors such as energy, trust and cooperative count. It determines the dependence among the nodes, based on the independent probability distribution functions and mutual probability function by using the Jensen–Shannon Divergence. The divergence result enables JDICA technique to detect black hole attacks with greater accuracy, and helps to quarantine the malicious node from the network by broadcasting the isolation message to all sensor nodes in the network. Hence, JDICA technique enhances the detection of black hole attack nodes as compared to state-of-the-art works, thereby increasing the packet delivery ratio and reducing delay. The JDICA technique simulation is done considering the metrics such as detection rate, detection time, false alarm rate, and packet delivery ratio with respect to a varied number of sensor nodes and data packets. Simulation results makes it apparent that the JDICA technique improves the detection rate and minimizes the detection time of the black hole attack when compared to state-of-the-art works.

<p>Wireless sensor networks (WSNs) are susceptible to a variety of attacks, such as data tampering attacks, blackhole attacks, and grayhole attacks, that can affect the reliability of communication. We proposed a reputationenhanced two-way hybrid algorithm (RCHA) that uses cryptographic hash functions and reputation-based trust management to detect and de-escalate attacks accurately. The RCHA algorithm implements two hash functions RACE integrity primitives’ evaluation message digest (RIPEMD) and secure hash algorithm (SHA-3), to initiate the integrity check for the entire packet sent across the network. Every node in the WSN tracks a reputation score for each neighbor the node is connected to, and this score is dynamically updated based on the behavior of each neighbor. If a neighboring node’s reputation drops below a threshold, the node is sent a maliciousness designation. At that time, the node will broadcast an alert message to its neighboring nodes and begin to reroute its data through one of its trusted neighbors to ensure the reliability of the communication. The simulation results reported that the RCHA algorithm improved the accuracy of the attack detection rate and the number of packets delivered compared to traditional attack detection methods. The RCHA algorithm was able to maintain low computational and energy overhead for the WSN, making it an attractive option for a resource-constrained application in a WSN. Given the trends towards more collaborative networks, the reputation mechanism in the RCHA algorithm improves the overall reliability and capabilities of the WSN, regardless of adversaries.</p>

The article comprises issues of analytical modeling of attacks in wireless sensor networks. A proposed attack model specifies possible types of unauthorized influences at the network level aimed at compromising network nodes, data transmitted over the network, and information services provided by the wireless sensor network. A developed graph-based analytical model of a wireless sensor network aims at detecting network layer attacks by processing actual events received from network nodes and deciding whether the network is running under an attack. The correctness of the models is confirmed by their testing by using an example of a scenario for detecting a wormhole attack aimed at disrupting routing processes in a wireless sensor network.