Selective Forwarding Attacks Research Articles

A robust detection scheme against selective forwarding attacks in wireless sensor networks under variable harsh environments

Event-driven wireless sensor networks (EWSNs) often operate in harsh environments and are susceptible to selective forwarding attacks, where malicious nodes selectively drop packets. These attacks are difficult to detect due to similarities with normal node behavior under adverse conditions. To address this, we propose a detection scheme combining Variable Autoencoder (VAE) and Gaussian Mixture Model (GMM) with an Autoencoder (AE). The scheme extracts features from the node's single round forwarding rate (SFR), with VAE generating latent variables and GMM clustering them to differentiate behaviors. The AE model, trained on data representing normal behavior, uses reconstruction error to identify malicious nodes. Simulation results show that our scheme improves network throughput and achieves low False Detection Rate (FDR) and Missed Detection Rate (MDR), with both rates as low as 0.5% in normal conditions and below 2% in mobile harsh environments. With a computational complexity of O(n), this method is suitable for resource-constrained environments, demonstrating superior robustness compared to existing approaches.

FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy.

Detecting selective forwarding attacks on cloud

—The Internet of Things (IoT) technology is now ubiquitous in both daily life and industrial production. Wireless Sensor Networks (WSNs) are a crucial part of the IoT infrastructure, providing the underlying network. However, WSNs are vulnerable to attacks due to challenging environmental conditions and their open communication nature. One particularly elusive threat is the selective forwarding attack, where malicious nodes intentionally drop parts or all of the received data packets, causing information loss and delays. This paper addresses the transition from the limited resources of central processing units (CPUs) to the expansive capabilities of cloud platforms within the IoT framework. By harnessing the robust computing power of cloud platforms, we investigate the effectiveness of the Time Convolution Network (TCN) time series prediction algorithm in detecting malicious nodes. Comparative results demonstrate that our proposed scheme achieves a Miss Detection Rate (MDR) of below 1% and a False Detection Rate (FDR) of less than 5%.

OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things

OPSMOTE-ML: an optimized SMOTE with machine learning models for selective forwarding attack detection in low power and lossy networks of internet of things

DSAT-IDS: Dissimilarity and Adaptive Threshold-based Intrusion Detection system to mitigate selective forwarding attack in the RPL-based 6LoWPAN

DSAT-IDS: Dissimilarity and Adaptive Threshold-based Intrusion Detection system to mitigate selective forwarding attack in the RPL-based 6LoWPAN

GD3N: Adaptive clustering-based detection of selective forwarding attacks in WSNs under variable harsh environments

Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably challenging to detect. This difficulty arises from the ability of malicious nodes to imitate the behavior of normal nodes, and selectively drop data packets, which makes them virtually indistinguishable from normal ones, particularly under conditions of poor channel quality. To address this challenge with harsh environments, we introduce a novel methodology termed GD3N. This approach is underpinned by the design of a unique type of data point that encapsulates both short-term and long-term forwarding behaviors of nodes. It combines a refined version of the Gradient Diffusion Density-based Spatial Clustering of Applications with Noise (GD-DBSCAN) algorithm, with a novel Double-Parameter Neighbor Voting (DP-NV) method based on the data set. These innovations contribute to a significant enhancement in detection accuracy and a reduction in computational complexity when compared to traditional DBSCAN and NV methods. Simulation results show that our GD3N achieves a false detection rate (FDR) of less than 2%, a missed detection rate (MDR) of below 10%, and an overall detection accuracy rate (DAR) of over 95% across various testing scenarios.

A Combined Scheme Based on Artificial Immune System for Selective Forwarding Attack Detection in Event Driven Wireless Sensor Networks

A Combined Scheme Based on Artificial Immune System for Selective Forwarding Attack Detection in Event Driven Wireless Sensor Networks

AutoGMM-RWKV: A Detecting Scheme Based on Attention Mechanisms Against Selective Forwarding Attacks in Wireless Sensor Networks

AutoGMM-RWKV: A Detecting Scheme Based on Attention Mechanisms Against Selective Forwarding Attacks in Wireless Sensor Networks

Utilize DBN and DBSCAN to detect selective forwarding attacks in event-driven wireless sensors networks

Malicious nodes launching selective forwarding attacks jeopardize network reliability in event-driven wireless sensor networks. Swift detection and exclusion of these nodes are vital, especially in harsh environments where normal nodes also suffer from decreased forwarding rates due to poor channel conditions. To solve this problem, this paper proposes a deep belief network (DBN) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) based detection scheme against selective forwarding attacks under harsh environments. The DBN algorithm extracts and analyzes behavior features of nodes, while the DBSCAN clustering algorithm effectively distinguishes between malicious and normal nodes during selective forwarding attacks. Simulation results demonstrate the scheme’s effectiveness with a missed detection rate (MDR) of approximately 2% and a false detection rate (FDR) below 5% in harsh environments, providing a robust solution for ensuring the integrity and efficiency of data transmission in event-driven wireless sensor networks.

LSTM-NV: A combined scheme against selective forwarding attack in event-driven wireless sensor networks under harsh environments

Wireless Sensor Networks (WSNs) are susceptible to selective forwarding attacks, which can lead to reduced network efficiency and compromise the integrity of transmitted data. The indistinguishability between malicious behavior and normal packet loss in harsh environments exacerbates the challenge. To address this, in this paper, we provide a combined detection scheme called LSTM-NV, consisting of a training stage and a detecting stage. During the training stage, we integrate variational mode decomposition (VMD) with a long short term memory (LSTM) model to learn normal nodes’ forwarding behavior and then predict errors for each node. During the detecting stage, dynamic thresholds are determined to identify local anomaly points and a novel neighbor voting method is employed to differentiate between malicious and normal nodes. Our scheme demonstrates superior performance with a low average missed detection rate (MDR) of 0.6% and a low average false detection rate (FDR) of 3.3% compared to other effective methods, while also offering lower detection algorithmic complexity.

A distributed adaptive routing against selective forwarding attack in scale-free network considering cascading failure

Abstract To address the issues of data insecurity and unreliable transmission, redundancy-based data recovery can guarantee data security, but the increase of redundant data will reduce the robustness of network in the face of cascading failures. A distributed adaptive routing method in scale-free network is proposed to improve network resilience against selective forwarding attacks and the robustness against cascading failures. Based on the polynomial principle, the proposed routing method slices packets, adds redundancy reasonably and adopts multipath sequential routing method to completely send data to the destination node. The ability to resist selective forwarding attacks and robustness against cascading failures is investigated and analysed throughout the entire network operation. Simulation results show that our proposed routing method is not restricted by the number of disjoint paths, can maintain a higher data recovery ratio and resist effectively selective forwarding attacks, and also balances the network load well. Moreover, this routing method has a lower end-to-end latency for data transmission and is highly resistant to cascading failures under random and intentional attacks.

RPLAD3: anomaly detection of blackhole, grayhole, and selective forwarding attacks in wireless sensor network-based Internet of Things.

Routing protocols transmit vast amounts of sensor data between the Wireless Sensor Network (WSN) and the Internet of Things (IoT) gateway. One of these routing protocols is Routing Protocol for Low Power and Lossy Networks (RPL). The Internet Engineering Task Force (IETF) defined RPL in March 2012 as a de facto distance-vector routing protocol for wireless communications with lower energy. Although RPL messages use a cryptographic algorithm for security protection, it does not help prevent internal attacks. These attacks drop some or all packets, such as blackhole or selective forwarding attacks, or change data packets, like grayhole attacks. The RPL protocol needs to be strengthened to address such an issue, as only a limited number of studies have been conducted on detecting internal attacks. Moreover, earlier research should have considered the mobility framework, a vital feature of the IoT. This article presents a novel lightweight system for anomaly detection of grayhole, blackhole, and selective forwarding attacks. The study aims to use a trust model in the RPL protocol, considering attack detection under mobility frameworks. The proposed system, anomaly detection of three RPL attacks (RPLAD3), is designed in four layers and starts operating immediately after the initial state of the network. The experiments demonstrated that RPLAD3 outperforms the RPL protocol when defeating attacks with high accuracy and a true positive ratio while lowering power and energy consumption. In addition, it significantly improves the packet delivery ratio and decreases the false positive ratio to zero.

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.

Security Challenges of Selective Forwarding Attack and Design a Secure ECDH-Based Authentication Protocol to Improve RPL Security

Today, we could describe the Internet of Things (loT) as the pervasive and global network that provides a system for monitoring, controlling, processing, and analyzing the data generated by IoT devices. The huge amount of data generated by IoT devices when transported and routed through the internet presents several challenges. One of the common routing protocols in IoT networks is RPL (Routing Protocol for Low Power and Lossy Networks), but it is prone to security issues and attacks. Due to the presence of sensitive data in IoT and its exchange in the open network, issues of privacy and security in this network should be given special attention. In addition, the nodes in the Internet of Things have limited resources, and the symmetric encryption key is used to encrypt the data of all nodes, which has security weaknesses. Therefore, an efficient and secure authentication scheme is needed so that IoT nodes can authenticate each other and share a secure session key. In this article, we review security aspects of RPL protocols focusing on selective forwarding attacks. Further, we propose a key agreement and authentication mechanism based on ECDH (Elliptic-Curve Diffie–Hellman). We show that our design is very secure, that it meets security requirements, and that it can withstand known attacks while having low costs for computation and communication.

Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

Recently, Wireless Sensor Network (WSN) becomes most potential technologies for providing improved services to several data gathering and tracking applications. Because of the wireless medium, multi-hop communication, absence of physical protectivity, and accumulated traffic, WSN is highly vulnerable to security concerns. Therefore, this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis. It is challenging to determine if packet loss is caused by a collision in the medium access path, poor channel quality, or a selective forwarding assault. Identifying misbehaving nodes at the earliest opportunity is an acceptable solution for performing secure routing in such networks. As a result, in this study effort, we present a unique Modified Ad Hoc On-Demand Distance Vector (AODV) Routing protocol depending upon the One time password (OTP) method that employs the RSA algorithm. Finally, a trust evaluation process determines which approach is the most optimal. According to the simulation findings of the suggested routing protocol and comparison with existing routing protocols provided in this article, the proposed work is both efficient and cost-effective.

Selective forwarding attack detection and network recovery mechanism based on cloud-edge cooperation in software-defined wireless sensor network

A software-defined wireless sensor network can dynamically configure the nodes in a network according to the demand of the application layer. In practical applications, such as environmental monitoring, the nodes in a wireless sensor network(WSN) are deployed in the field environment on a large scale, and the data rely on multihop transmission to reach the sink node. The data extremely easy to selective forwarding attacks during data transmission. Therefore, this study analyzes the models of selective forwarding attacks and proposes an abnormal node detection method, which includes a node behavior measurement scheme and trust-value evaluation mechanism. In addition, the application of a software-defined network (SDN) presents increasing network delay. Hence, herein a network recovery mechanism was designed based on cloud-edge cooperation to ensure the rapid recovery of the network after identifying the abnormal nodes. Moreover, experiments were conducted using simulation software and actual hardware. We verified the effectiveness of the proposed scheme. The experimental results revealed that the proposed method can effectively identify abnormal nodes, reduce the packet dropping ratio and shorten the network recovery delay by 77.2%. The research in this paper solves the security problem of SDWSN.

The Detection Scheme Against Selective Forwarding of Smart Malicious Nodes With Reinforcement Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.

Retraction Note to: A provenance based defensive technique to determine malevolent selective forwarding attacks in multi-hop wireless sensor networks

Retraction Note to: A provenance based defensive technique to determine malevolent selective forwarding attacks in multi-hop wireless sensor networks

Identify Selective Forwarding Attacks Using Danger Model: Promote the Detection Accuracy in Wireless Sensor Networks

Wireless sensor networks (WSNs) are prone to attack due to open work conditions and broadcast communication. The selective forwarding attack, one of the inner attacks in WSNs, is the hardest attack to detect among denial of service (DoS) attacks. The malicious nodes which launch the selective forwarding attack will drop part of or all the data packets they received. Many proposed detection methods against selective forwarding attacks have low accuracy or high algorithm complexity, especially when the attacker works its way in the network along with several attacks, such as blackhole, wormhole, and Distributed Denial of Service (DDoS). Here, an artificial immune system based on the danger model is established to detect network attacks. To promote detection accuracy and reduce computation, we have proposed a screen-confirm scheme. In the screening phase, the scheme first obtains the danger signals from nodes’ energy consumption, forwarding rate, connect duration, transmission frequency, and transmission time, then it screens out the suspected selective forwarding attacks from DoS attacks using a support vector machine (SVM). After that, in the confirming phase, we select an optimal danger threshold and calculate the outputs of the suspected ones, then confirm them by comparing outputs and threshold. The simulation results show that our scheme’s missing detection rate (MDR) is close to 1.3% and keeps the false detection rate (FDR) lower than 4.3% when the malicious ratio is 10%. Moreover, compared with other related work, our proposed method offers a lower algorithm complexity.

Improved Intrusion Detection System using Quantal Response Equilibrium-based Game Model and Rule-based Classification

Wireless sensor network has large number of low-cost tiny nodes with sensing capability. These provide low cost solutions to many real world problems such as such as defence, Internet of things, healthcare, environment monitoring and so on. The sensor nodes of these networks are placed in vulnerable environment. Hence, the security of these networks is very important. Intrusion Detection System (IDS) plays an important role in providing a security to such type of networks. The sensor nodes of the network have limited power and, traditional security mechanisms such as key-management, encryption decryption and authentication techniques cannot be installed on the nodes. Hence, there is a need of special security mechanism to handle the intrusions. In this paper, intrusion detection system is designed and implemented using game theory and machine learning to identify multiple attacks. Game theory is designed and used to apply the IDS optimally in WSN. The game model is designed by defining the players and the corresponding strategies. Quantal Response Equilibrium (QRE) concept of game theory is used to select the strategies in optimal way for the intrusion’s detection. Further, these intrusions are classified as denial of service attack, rank attack or selective forwarding attacks using supervised machine learning technique based on different parameters and rules. Results show that all the attacks are detected with good detection rate and the proposed approach provides optimal usage of IDS.