Secure Internet Of Things Networks Research Articles

Ethereum Blockchain Technology for Internet of Things Authentication

The Internet of Things (IoT) technology has grown rapidly and continuously. The IoT ecosystem comprises an increasing number of smart devices that can sense, act, process, store, and communicate via the Internet. Data within such communication technology is continuously exposed to be hacked or attacked. Therefore, there should be methods to protect the issue of data authentication and increase the security of such large-scale networks. In this article, a method of using a Blockchain network is proposed. The proposed system uses the Ethereum blockchain and benefits from several critical technologies, including distributed consensus, smart contracts, digital signatures, and cryptographic hashes. The whitelist and blacklist are the two lists that are suggested. The approved users who have obtained their private keys are included on the whitelist. The blacklist, on the other hand, is intended to stop illegal users who are flagged as spammers or attacks. According to findings, the Blockchain can drastically save expenses and boost the security of IoT networks

Security and Internet of Things: Benefits, Challenges, and Future Perspectives

Due to the widespread use of the Internet of Things (IoT), organizations should concentrate their efforts on system security. Any vulnerability could lead to a system failure or cyberattack, which would have a large-scale impact. IoT security is a protection strategy and defense mechanism that protects against the possibility of cyberattacks that specifically target physically linked IoT devices. IoT security teams are currently dealing with growing difficulties, such as inventories, operations, diversity, ownership, data volume, threats, etc. This review examines research on security and IoT with a focus on the situation, applications, and issues of the present as well as the potential for the future. IoT network security has received greater attention from interdisciplinary and geographically scattered researchers in recent years. Data integrity, secrecy, authentication, and authorization should be guaranteed due to the large amount of data that flows across network devices. However, the area of IoT security still has a lot of room for growth.

Defective node detection by employing DS theory and RP model for secure internet of things networks

Internet of Things Networks (IOTN) are applied in all sectors as a result of their broad applicability. Even though the performance of IOTN is satisfactory, it still faces various obstacles, including energy constraints, dependability, and security. This work aims to improve the reliability of the network by identifying defective nodes. Defective nodes affect the normal functionality of the entire network; therefore, it is vital to detect the defective nodes, such that the Quality of Service (QoS) is improved. This work detects defective nodes by employing the Reward-and-Punishment Model (RPM) and the hypothetical analysis of Dempster-Shafer (DS) theory. The effectiveness of the proposed work is found to be satisfactory in terms of defective node detection, precision, energy consumption, and network lifetime.

The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data

<p>Due to the complexity and multifaceted nature of Internet of Things (IoT) networks/systems, researchers in the field of IoT network security complain about the rareness of real life-based datasets and the limitation of heterogeneous of communication protocols used in the datasets. There are a number of datasets publicly available such as DARPA, Twente, ISCX2012, ADFA, CIC-IDS2017, CSE-CIC-IDS2018, CIC-DDOS2019, MQTT-IoT-IDS-2020, and UNSW-NB15 that have been used by researchers to evaluate performance of the Intrusion Detection Systems (IDSs), nevertheless, the datasets creation are not based on real-life scenarios and rely only on one communication protocol. This paper produces a dataset that is created using real-life scenarios. The data are captured from an IoT test-bed network consists of six sensors running IEEE 802.11 (WiFi) and IEEE 802.15.4 (ZigBee) communication protocols and considering normal as well as attacks traffics. Furthermore, the robustness of the dataset for recognizing the types of data traffics is evaluated using Intrusion Detection Engine (IDE) with Naïve String Matching. The experiments on dataset robustness show promising results, i.e.: Accuracy level of 99.92%, Precision of 100%, False Positive Rate (FPR) of 0, and False Negative Rate (FPR) of 0.0869.</p> <p> </p>

A Novel Deep Learning-Based Intrusion Detection System for IoT Networks

The impressive growth rate of the Internet of Things (IoT) has drawn the attention of cybercriminals more than ever. The growing number of cyber-attacks on IoT devices and intermediate communication media backs the claim. Attacks on IoT, if they remain undetected for an extended period, cause severe service interruption resulting in financial loss. It also imposes the threat of identity protection. Detecting intrusion on IoT devices in real-time is essential to make IoT-enabled services reliable, secure, and profitable. This paper presents a novel Deep Learning (DL)-based intrusion detection system for IoT devices. This intelligent system uses a four-layer deep Fully Connected (FC) network architecture to detect malicious traffic that may initiate attacks on connected IoT devices. The proposed system has been developed as a communication protocol-independent system to reduce deployment complexities. The proposed system demonstrates reliable performance for simulated and real intrusions during the experimental performance analysis. It detects the Blackhole, Distributed Denial of Service, Opportunistic Service, Sinkhole, and Workhole attacks with an average accuracy of 93.74%. The proposed intrusion detection system’s precision, recall, and F1-score are 93.71%, 93.82%, and 93.47%, respectively, on average. This innovative deep learning-based IDS maintains a 93.21% average detection rate which is satisfactory for improving the security of IoT networks.

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.

Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security

The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called black box processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other frameworks, as evidenced by testing results showing an F1 score of 98.83 percent and an accuracy of 99.15 percent. These findings demonstrate that the architecture successfully resists a variety of destructive assaults on IoT networks. By integrating deep learning and methodologies with an emphasis on explainability, our approach significantly enhances network security in IoT use scenarios. The ability to assess and grasp IDS options provides the path for cybersecurity experts to design and construct more secure IoT systems.

Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment

Internet of Things (IoT) is an emerging technology and its applications are flattering amidst many users, as it makes everything easier. As a consequence of its massive growth, security and privacy are becoming crucial issues where the IoT devices are perpetually vulnerable to cyber-attacks. To overcome this issue, intrusion detection and mitigation is accomplished which enhances the security in IoT networks. In this paper, we proposed Blockchain entrenched Bi-level intrusion detection and graph based mitigation framework named as HybridChain-IDS. The proposed work embrace four sequential processes includes time-based authentication, user scheduling and access control, bi-level intrusion detection and attack graph generation. Initially, we perform time-based authentication to authenticate the legitimate users using NIK-512 hashing algorithm, password and registered time are stored in Hybridchain which is an assimilation of blockchain and Trusted Execution Environment (TEE) which enhances data privacy and security. After that, we perform user scheduling using Cheetah Optimization Algorithm (COA) which reduces the complexity and then the access control is provided to authorized users by smart contract by considering their trust and permission level. Then, we accomplish bi-level intrusion detection using ResCapsNet which extracts sufficient features and classified effectively. Finally, risk of the attack is evaluated, and then the attacks graphs are generated by employing Enhanced k-nearest neighbor (KNN) algorithm to identify the attack path. Furthermore, the countermeasures are taken based on the attack risk level and the attack graph is stored in Hybridchain for eventual attack prediction. The implementation of this proposed work is directed by network simulator of NS-3.26 and the performance of the proposed HybridChain-IDS is enumerated based on various performance metrics.

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day‐to‐day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality‐of‐service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning‐based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.

A hybrid deep learning-based intrusion detection system for IoT networks.

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Several researchers have focused on intrusion detection systems (IDS) that address only one layer of the three-layered IoT architecture, which limits their effectiveness in detecting attacks across the entire network. To address these limitations, this paper proposes an intelligent IDS for IoT networks based on deep learning algorithms. The proposed model consists of a recurrent neural network and gated recurrent units (RNN-GRU), which can classify attacks across the physical, network, and application layers. The proposed model is trained and tested using the ToN-IoT dataset, specifically collected for a three-layered IoT system, and includes new types of attacks compared to other publicly available datasets. The performance analysis of the proposed model was carried out by a number of evaluation metrics such as accuracy, precision, recall, and F1-measure. Two optimization techniques, Adam and Adamax, were applied in the evaluation process of the model, and the Adam performance was found to be optimal. Moreover, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. The results show that the proposed system achieves an accuracy of 99% for network flow datasets and 98% for application layer datasets, demonstrating its superiority over previous IDS models.

A Unified Federated Framework for Internet-of-Things Security Challenges


 The protection of sensitive information is among the top priorities of organizations that are involved in manufacturing. Because of this, businesses are afraid of sharing their data with other parties so that they may construct predictive and prognostic models. In such a situation, it is difficult to construct complete models to forecast the breakdown of assets since data from a single firm would not provide the needed range of operating regimes and failure types. Recently, the Internet of Things (IoT) has gotten a lot of interest because of the vast variety of applications it has in numerous fields that communicate across multiple levels of the Internet infrastructure. The Internet of Things is composed of three layers: the physical layer, the network layer, and the application layer. This paper discusses security threats and responses for each layer of the IoT. The research examines different current state-of-the-art IoT security frameworks and suggests a unified IoT network security framework name "A Unified Federated Security Framework." The fuzzy cognitive maps used in the proposed framework are used to represent and evaluate trust connections between entities in federated identity management systems. For Internet of Things networks, the unified federated security architecture suggested in this paper offers comprehensive security characteristics. It also allows for the accurate categorization of all assaults and the capture of the different dangers, which allows for the development and implementation of improved defenses.

Integrating Blockchain with Artificial Intelligence to Secure IoT Networks: Future Trends

Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to IoT technology, the security of IoT networks has become a primary concern, especially with the lack of intrinsic security mechanisms regarding the limited capabilities of IoT devices. Therefore, many studies have been interested in enhancing the security of IoT networks. IoT networks need a scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research.

Using Machine Learning in Detecting IOT Cyber Attacks

Abstract: The Internet of Things (IoT) combines hundreds of millions of devices that are able to interact with each other with minimal user interaction. IoT is one of the fastest growing areas of computing; however, the reality is that in the extremely hostile environment of the Internet, IoT is vulnerable to many types of cyberattacks. Practical countermeasures to secure IoT networks, such as network anomaly detection, need to be implemented to address this issue. Regardless of the fact that attacks cannot be completely avoided forever, early detection of an attack is essential for practical defense. As IoT devices have low storage capacity and low computing power, traditional high-end security solutions are not suitable for IoT system protection. IoT devices are also now connected without human intervention for longer periods of time. This means that intelligent networkbased security solutions such as machine learning solutions need to be developed. Although many studies in recent years have discussed the application of Machine Learning (ML) solutions to attack detection problems, little attention has been paid to attack detection specifically in IoT networks. In this study, we aim to contribute to the literature by evaluating various machine learning algorithms that can be used to quickly and effectively detect IoT network attacks. A new Bot-IoT dataset is used to evaluate different detection algorithms. Seven different machine learning algorithms were used in the implementation phase and most of them achieved high performance. New features were extracted from the Bot-IoT dataset during implementation and compared with literature studies, and the new features provided better results

Directional Modulation for Secure IoT Networks via Accurate Phase Response Control

This article presents an accurate phase response control (APRC) algorithm to realize directional modulation (DM), improving the physical layer security of Internet of Things (IoT) networks. Specifically, the concept of normalized array response control is developed to realize the phase response control, and the designed weight vector is constructed as the sum of an initial weight vector and an appended weight vector. The precondition of precise phase response control is theoretically derived, and a geometric interpretation of phase response adjustment is given. The APRC algorithm can derive an analytical solution to realize the precise phase response control and normalized magnitude response control jointly, and the computational complexity of the APRC algorithm is much lower than the existing approaches. Moreover, a flexible DM scheme based on the APRC algorithm is presented to realize the specific PSK symbol synthesis. Different from the existing works that are only feasible for the single direction PSK modulation design, the APRC algorithm is applicable for the more general multipath and quasistatic block Rayleigh channels. The synthesis results with fixed and random initial weights are illustrated to show the wide applicabilities of the APRC algorithm, and the bit error rate simulations are given to validate the APRC algorithm.

Toward Secure IoT Networks in Healthcare Applications: A Game-Theoretic Anti-Jamming Framework

The Internet of Things (IoT) is used to interconnect a massive number of heterogeneous resource-constrained smart devices. This makes such networks exposed to various types of malicious attacks. In particular, jamming attacks are among the most common harmful attacks to IoT networks. Therefore, an anti-jamming power allocation (PA) strategy is first proposed in this article for health monitoring IoT networks by exploiting the game theory to minimize the worst case jamming effect under multichannel fading. This strategy uses an iterative algorithm based on gradient descent to identify the Nash Equilibrium (NE) of the game. An artificial neural network (ANN) model is also proposed to accelerate the convergence of the algorithm making it more suitable for IoT networks. Furthermore, novel data population (DP), extension, and balancing techniques are proposed to enhance the efficiency of the proposed strategy in combating jamming attacks even for network configurations that were never used in the training phase. In addition, time and spatial diversities are exploited using a heterogeneous iterative algorithm to enhance the security of the network.

A Lightweight Energy-Efficient Algorithm for mitigation and isolation of Internal Rank Attackers in RPL based Internet of Things

Internet of Things (IoT) has become a pivotal technology for connecting various physical things or devices to the internet. Despite the potential advantages, security in IoT network continues to be a major concern due to enormous volume of the data generated and transmitted. Since the devices in IoT are resource-constrained and heterogeneous, it is becoming harder to maintain security in routing protocols such as Routing Protocol for Low-power and Lossy networks (RPL). RPL uses control packets for the construction of tree topology and it is prone to various attacks such as flood, version, rank and so on. Rank attack leads to the formation of loop, increased latency in packet delivery, energy depletion in nodes, decreased delivery ratio of packets and so on. Since the impact of the rank attack is high on the network, a lightweight energy-efficient algorithm is needed for the detection and isolation of rank attackers. The proposed Rank Attack Detection (RAD) algorithm uses a non-cryptographic hash algorithm to preserve the integrity of control packets. It uses random sampling to reduce energy consumption. Once the rank attacker is detected, an alarm is generated against the attackers. The alarm is not a separate control packet; it is appended to the control message itself. The proposed algorithm is analysed using different topologies by comparing it with the already existing algorithms in terms of control packet overhead, delivery ratio, latency for delivery of data packets, accuracy and energy consumption. It achieves better accuracy in grid-centered topology and the average energy consumption is reduced in random topology.

Using manufacturer usage descriptions for IoT network security: An experimental investigation of smart home network devices

The Internet of Things (IoT) has shown significant growth in the past decades. Recently, IoT networks have been subject to cybersecurity threats on multiple fronts. A lack of improvement in IoT infrastructures' cybersecurity may result in challenges with a broad impact, such as DDoS attacks that target global DNS services. This paper reviews existing solutions to mitigate attacks on and from IoT networks. As a specific mitigation approach, we propose the use of a standardized whitelisting method, namely Manufacturer Usage Description (MUD), which provides enhanced explainability over machine learning-based approaches and is complementary to them. For evaluating the use of MUD in IoT networks, we report a case study, which we conducted through traffic analysis of two IoT devices by detecting recognizable and distinctive traffic patterns, which demonstrate the feasibility of MUD-based intrusion prevention.

A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer

Abstract The Internet of Things (IoT) is widespread in our lives these days (e.g., Smart homes, smart cities, etc.). Despite its significant role in providing automatic real-time services to users, these devices are highly vulnerable due to their design simplicity and limitations regarding power, CPU, and memory. Tracing network traffic and investigating its behavior helps in building a digital forensics framework to secure IoT networks. This paper proposes a new Network Digital Forensics approach called (NDF IoT). The proposed approach uses the Owl optimizer for selecting the best subset of features that help in identifying suspicious behavior in such environments. The NDF IoT approach is evaluated using the Bot IoT UNSW dataset in terms of detection rate, false alarms, accuracy, and f-score. The approach being proposed has achieved 100% detection rate and 99.3% f-score and outperforms related works that used the same dataset while reducing the number of features to three features only.

Enhanced Elman spike neural network fostered blockchain framework espoused intrusion detection for securing Internet of Things network

AbstractIn general, due to the complexity and limited computation capabilities, the security issues occur in Internet of Things (IoT). Security protocols are required to increase the security of the system. Therefore, in this article, an enhanced Elman spike neural network (EESNN) with green proof of work consensus algorithm (GPoW) is proposed for enhancing the security of IoT network. Initially, the generalized security mechanism as EESNN approach is proposed for the IoT network by categorizing the devices into malicious and benign. Then, the GPoW consensus algorithm is used for enhancing the security of the devices from malicious attacks. Subsequently, a coalition formation (CF) algorithm is used for reducing the excess energy consumption in a network. The proposed EESNN‐GPoW‐CF approach has effectively classified the malicious attacks and enhances the security of the IoT network. The simulation of this work is done in Python. From the simulation, the proposed EESNN‐GPoW‐CF approach attains high efficiency outcomes in terms of accuracy, recall, precision, PDR, PLR, throughput, overhead, computation time, and delay. Moreover, the proposed EESNN‐GPoW‐CF approach attains 3.1%, 5.3%, 7.4% high accuracy rate, and 7.5%, 12.5%, 14.7% lower computation time with 4.8%, 2.3%, 5.7% lower energy utilization than the existing methods, such as deep learning based blockchain for IoT security, deep reinforcement learning based blockchain for IoT security, and deep blockchain‐based trustworthy privacy preserving secured framework in IoT, respectively.

An intellectual intrusion detection system using Hybrid Hunger Games Search and Remora Optimization Algorithm for IoT wireless networks

Smart devices are connected to the internet, called the Internet of Things (IoT); it is a wireless network that plays a vital role in modernizing people’s interaction by improving agility, mobility, efficiency and effectiveness. Although IoT offers various advantages, it also raises privacy and security problems in IoT networks. The security of such integrated networks still remains an active area of research. Information transported across a wireless network must be monitored and implemented efficiently to ensure its integrity, validity and confidentiality. In order to minimize the rate of security problems in IoT networks, an intrusion detection model is suggested. For that reason, various intrusion detection based techniques are introduced in existing works to improve the security of IoT networks. But existing methods do not provide an efficient result in securing IoT data. The main contribution of using intrusion detection techniques in the proposed work is to improve the security of IoT networks. In this work, a hybrid Hunger Games Search and Remora Optimization Algorithm (HHGS-ROA) is proposed to address the security problems. The proposed approach uses a real-world Aegean Wi-Fi Intrusion Dataset (AWID) for an IoT intruder detection system. Initially, the AWID raw captured traffic is fed into the pre-processing stage to increase the speed of feature selection. In the proposed approach, a hybrid HGS-ROA model is used to extract the features from the AWID dataset. Finally, the SVM classifier is proposed to classify network traffic as normal or malicious attacks. Therefore proposed approach achieves a low false-positive rate (FPR) with an enhanced detection rate. The proposed work is implemented in the PYTHON platform. Compared with existing methods in terms of accuracy, F1 score, detection rate, FAR, computation times, false alarm rate, MCC, and precision has proven to be more effective. The proposed approach performs better than existing algorithms in terms of evaluating metrics such as accuracy (99.16%), precision (99.76%), recall (99.40%), F1 score (99.58%), false alarm rate (0.00005%), MCC (99.97%) and false-positive rate (0.20%).