Design of Clustering Enabled Intrusion Detection with Blockchain Technology

The Internet of Things (IoT) is emerging as a new trend mainly employed in developing numerous vital applications. These applications endure on a federal storage framework primarily concerned with multiple issues. Blockchain technology (BC) is one of the supportive methods for developing IoT-based applications. It is employed to solve the problems encountered in IoT applications. The attack Distributed Denial of Service (DDoS) is one of the leading security attacks in IoT systems. Attackers can effortlessly develop the exposures of IoT gadgets and restrain them as fragments of botnets to commence DDoS threats. The IoT devices are said to be resource-constrained with computing resources and restricted memory. As a developing technology, BC holds the possibility of resolving security problems in IoT. This paper proposes the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) method. The main intention of the MOBCF-ADDLM method is to deliver an effective method for detecting DDoS threats in an IoT environment using advanced techniques. The BC technology is initially applied to mitigate DDoS attacks by presenting decentralized security solutions. Furthermore, data preprocessing utilizes the min-max scaling method to convert input data into a beneficial format. Additionally, feature selection (FS) is performed using the Aquila optimizer (AO) technique to recognize the most relevant features from input data. The attack classification process employs the deep belief network (DBN) technique. Finally, the red panda optimizer (RPO) model modifies the hyper-parameter values of the DBN model optimally and results in higher classification performance. A wide range of experiments with the MOBCF-ADDLM approach is performed under the BoT-IoT Binary and Multiclass datasets. The performance validation of the MOBCF-ADDLM approach portrayed a superior accuracy value of 99.22% over existing models.

In recent days, advancements in the Internet of Things (IoT) and cloud computing (CC) technologies have emerged in different application areas, particularly healthcare. The use of IoT devices in healthcare sector often generates large amount of data and also spent maximum energy for data transmission to the cloud server. Therefore, energy efficient clustering mechanism is needed to effectively reduce the energy consumption of IoT devices. At the same time, the advent of deep learning (DL) models helps to analyze the healthcare data in the cloud server for decision making. With this motivation, this paper presents an intelligent disease diagnosis model for energy aware cluster based IoT healthcare systems, called IDDM-EAC technique. The proposed IDDM-EAC technique involves a 3-stage process namely data acquisition, clustering, and disease diagnosis. In addition, the IDDM-EAC technique derives a chicken swarm optimization based energy aware clustering (CSOEAC) technique to group the IoT devices into clusters and select cluster heads (CHs). Moreover, a new coyote optimization algorithm (COA) with deep belief network (DBN), called COA-DBN technique is employed for the disease diagnostic process. The COA-DBN technique involves the design of hyperparameter optimizer using COA to optimally adjust the parameters involved in the DBN model. In order to inspect the betterment of the IDDM-EAC technique, a wide range of experiments were carried out using real time data from IoT devices and benchmark data from UCI repository. The experimental results demonstrate the promising performance with the minimal total energy consumption of 63% whereas the EEPSOC, ABC, GWO, and ACO algorithms have showcased a higher total energy consumption of 69%, 78%, 83%, and 84% correspondingly.

The design and development of the Internet of Things (IoT) have a remarkable advancements in recent years. An “Internet of Things” (IoT) is a term used to describe an interconnection of physical objects with a network that incorporates software, sensors, and other devices to exchange information. Because patient health records (PHR) are valuable and significant, security is the most crucial component of encryption over the Internet. Sensitive data is typically secured by a third party during transmission in an IoT setting, which leads to complex and sometimes deadly problems. To address security issues and do away with third parties’ involvement in an IoT environment, blockchain technology is the modern solution. The idea of a secure blockchain for Internet of Things (IoT) health applications is presented in this paper. Electronic health records (EHRs), which are used to deliver healthcare directly to patients while reducing physical contact between patients and healthcare providers, are the health information that is collected and saved electronically for educational purposes in today’s healthcare. Decision makers can enhance the healthcare industry and gain from the current e-government cloud computing infrastructure by using the provided framework. As a result, the proposed Blockchain work enhances the dependability of IoT systems. It also has the potential to reduce costs and boost efficiency.

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.

Secure blockchain enabled Cyber–physical systems in healthcare using deep belief network with ResNet model

Devices connected to the Internet can easily exchange information thanks to the Internet of Things (IoT), a networked system that functions via established protocols. IoT's decentralized architecture presents serious security, privacy, data integrity, and system stability issues despite its revolutionary potential. Although technological innovations like artificial intelligence, the Internet of Things, and big data have significantly raised people's quality of life, they have also increased the likelihood of increasingly complex and serious cyberattacks. By creating a machine learning model for the identification and categorization of intrusion threats in Internet of Things networks, this study seeks to address these issues. In particular, a hybrid strategy that combined Support Vector Machine (SVM) and fuzzy logic techniques was used to improve intrusion detection systems' efficacy in Internet of Things settings. We trained and tested the model using the NSL-KDD dataset from Kaggle. Key performance indicators, such as true positive rate, false positive rate, accuracy, F1 score, precision, and recall, were used to assess the suggested model's performance. With an accuracy of 99% on an imbalanced dataset and 98% on a balanced dataset, the results showed that the hybrid Fuzzy Logic-SVM model performed well across all data splits. These results demonstrate how the model may be used.

Ensuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in traffic categorization and intrusion detection. This study proposes a ML-based network traffic detection model (MLNTDM) to enhance IoT application layer attack detection. The proposed architecture for the MQTT protocol is evaluated based on its effectiveness in detecting malicious attacks and how these affect various MQTT brokers. This study focuses on low-power-consuming ML algorithms for detecting IoT botnet offenses and identifying typical attacks and their responses. With this framework, each network flow provides information that can help identify the source of generated traffic and network assaults. Results from our approach, as shown in the experiment, prove more accuracy.

Currently, Internet of Things (IoT) is extensively adopted in number of applications that its prominence is extending in day-to-day lives. Also, it is being utilized as E-health application on divergent characteristics like earlier diagnosis of medical issues, computer-assisted rehabilitation, and emergency notification. Due to misdiagnoses of disease that increased recently in a scarily manner, many research workers dedicated their effort and deployed technology to enhance the medical diagnoses process and reduce the resulted risk. This paper majorly concentrates on the design of artificial fish swarm algorithm (AFSA) with deep belief network (DBN) model for disease detection and classification in the IoT environment. The proposed AFSA-DBN model recognizes the presence or absence of disease on the healthcare data. The presented AFSA-DBN model encompasses three major subprocesses. At the initial stage, the min–max normalization approach is applied to scale the input data into useful format. Followed by, the DBN method can be exploited for the identification and classification of diseases. At last, the AFSA is utilized to optimally choose the hyperparameter values related to the DBN method. To assure the enhanced performance of the AFSA-DBN model, a wide-ranging experimental analysis was conducted. The comparative results assured the improved outcomes of the AFSA-DBN algorithm over recent techniques with maximum accuracy of 0.9111.KeywordsDisease diagnosisInternet of ThingsDeep learningMachine learningData classification

Recent emerging technologies are being utilized for the quench of connectivity in real time scenarios. A push is coming to make the information available to humans from the real-time environmental data collected through small sensing devices. Wireless ad-hoc network is a base architecture for Internet of Things (IoT), Unmanned Arial Vehicle (UAV) and drones etc. In this series, IoDTIoDT (Internet of Drone Things) came as the future of drones backend via the Internet of Things, smart vision, cloud computing, enhanced communication, big data, and advanced security approaches. Rapid growth in sensing devices connected to the Internet with intelligence and capabilities also opens the door for attackers because more devices are connected means more chances of security vulnerabilities. Since data authentication is handled only by the central station, which may lead to the chances of device spoofing and false authentication brings less reliability after all. Blockchain (BC) technology is introduced to address such security concerns by eliminating the role of central authority. Blockchain Technology gives decentralized and non-tamperable solutions for the most demanding security service i.e. Authentication. This paper starts with unique characteristics and security challenges in such IOT environment and further covers the authentication process by blockchain with its potential benefits. The multilayer ecosystem is illustrated to fulfill the requirement of the UAV IoT environment, where multiple devices are equally working in a cooperative manner to perform an authorized action. The paper presented a complete study about the integration of blockchain in IoT enabled UAV.

The growing number of Internet of Things (IoT) objects and the operational and security challenges in IoT systems are encouraging researchers to design suitable IoT architecture. Enormous data generated in the IoT environment face several kinds of security and privacy challenges. IoT system generally suffers from several issues like data storage, safety, privacy, integrity, transparency, trust, and single point of failure. IoT environment is emerging with several solutions to resolve these problems. The main objective of this paper is to design a cloud-blockchain-based secure IoT architecture that provides advanced and efficient storage and security solutions to IoT ecosystem. Blockchain technology appears to be a decent choice to resolve such kinds of problems. Blockchain technology uses a hash-based cryptographic technique for information security and integrity. Cloud computing provides advanced storage solutions with several remote services to store, compute and analyze the data. The proposed IoT architecture is based on the integration of cloud and blockchain services, which aim to provide transparent, decentralized, and trustworthy and secure storage solutions. In addition to the standard layers (perception layer, network layer, processing layer, and application layer) the proposed IoT architecture in the present paper includes a service layer, a security layer, and a parallel management and control layer, which focus on the security and management of the entire IoT infrastructure.

The ability to deploy such systems throughout a region, or perhaps a nation, will increase as IoT infrastructure develops. Although Internet of Things (IoT) devices are capable of offering intelligent services, the massive amounts of data they gather and analyze pose a severe security threat. Researchers have spent a lot of work building sophisticated NIDS to prevent the exploitation of IoT data in smart applications. However, present methods may be vulnerable to attack, particularly new attacks, due to a lack of sufficient and evenly distributed attack data used to train the detection strategy. This study introduces a original hierarchical adversarial attack (HAA) generation strategy, which in turn targets the convolutional neural network-based intrusion detection in IoT devices with a limited budget, and therefore realizes the level-aware black-box adversarial attack approach. To generate adversarial instances with little disturbance, an intelligent approach is developed that relies on a saliency map methodology. To do this, a shadow CNN framework has been constructed. Using a random walk having restart based hierarchical node selection approach, a subset of the targeted IoT network's nodes with the lowest structural strength are selected. The proposed HAA generation technique is evaluated in comparison to three reference methods using the publicly available data set UNSW-SOSR2019. In a comparison of the two most sophisticated convolutional neural network (CNN) designs, GCN and JK-Net, it was shown that the classification accuracy for NIDS in IoT settings may be reduced by more than 30%.

The Internet of Things (IoT) continues to attract attention in the context of computational resource growth. Various disciplines and fields have begun to employ IoT integration technologies in order to enable smart applications. The main difficulty in supporting industrial development in this scenario involves potential risk or malicious activities occurring in the network. However, there are tensions that are difficult to overcome at this stage in the development of IoT technology. In this situation, the future of security architecture development will involve enabling automatic and smart protection systems. Due to the vulnerability of current IoT devices, it is insufficient to ensure system security by implementing only traditional security tools such as encryption and access control. Deep learning and blockchain technology has now become crucial, as it provides distinct and secure approaches to IoT network security. The aim of this survey paper is to elaborate on the application of deep learning and blockchain technology in the IoT to ensure secure utility. We first provide an introduction to the IoT, deep learning, and blockchain technology, as well as a discussion of their respective security features. We then outline the main obstacles and problems of trusted IoT and how blockchain and deep learning may be able to help. Next, we present the future challenges in integrating deep learning and blockchain technology into the IoT. Finally, as a demonstration of the value of blockchain in establishing trust, we provide a comparison between conventional trust management methods and those based on blockchain.

With the advent of the Internet of Things (IoT), the security of the network layer in the IoT is getting more and more attention. The traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of IoT. For the deep learning algorithm of intrusion detection, a neural network structure may have fine detection accuracy for one kind of attack, but it may not have a good detection effect when facing other attacks. Therefore, it is urgent to design a self-adaptive model to change the network structure for different attack types. This paper presents an intrusion detection model based on improved genetic algorithm (GA) and deep belief network (DBN). Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate with a compact structure. Finally, the NSL-KDD dataset was used to simulate and evaluate the model and algorithms. The experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the neural network structure.

A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchain’s permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.