Internet Of Things Devices In Networks Research Articles

A Cross-Layer Secure and Energy-Efficient Framework for the Internet of Things: A Comprehensive Survey

This survey delves into cross-layer energy-efficient solutions and cutting-edge security measures for Internet of Things (IoT) networks. The conventional security techniques are considered inadequate, leading to the suggestion of AI-powered intrusion detection systems and novel strategies such as blockchain integration. This research aims to promote the development of smart cities by enhancing sustainability, security, and efficiency in the industrial and agricultural sectors through the use of IoT, blockchain, AI, and new communication technologies like 5G. In this paper, we provide a comprehensive review and analysis of secure and energy-efficient cross-layer IoT frameworks based on survey of more than 100 published research articles. We highlight the significance of developing IoT security for robust and sustainable connected systems. We discuss multi-layered security approaches and ways to enhance the energy efficiency of resource-constrained devices in IoT networks. Finally, we identify open research issues and future research directions in the emerging field of cross-layer design for secure and energy-efficient IoT networks. In order to improve cybersecurity and efficiency in smart cities, the research also focuses on developing a secure, energy-efficient IoT framework integrating blockchain, artificial intelligence, and quantum-safe cryptography.

Построение онтологии для систематизации характеристик сети Интернета вещей

A formalization of the Internet of Things network model designed for monitoring technological premises with tele-communications equipment at the Federal Research Center "Krasnoyarsk Scientific Center SB RAS" is presented. The network includes measuring devices, a telecommunications environment, data collection servers, and application soft-ware. For information interaction, a “publisher-subscriber” scheme and a lightweight protocol with a low load on com-munication channels are used. An ontology has been created that describes the network architecture and the properties of devices that collect, transmit, store, and process data. The ontology contains classes representing the concepts of the subject area, relationships, data properties, ranges of their changes, and critical values that limit the attributes of ontol-ogy elements. Ontology objects have their own digital representation in databases, including measurement results ob-tained by Internet of Things network sensors, precedents of anomalous data, and their statistical and frequency char-acteristics. This formalization made it possible to identify implicit dependencies between objects, connect them with the characteristics of processes observed by Internet of Things network devices, and solve practical tasks. The problem of selecting characteristics that influence changes in information interaction patterns is considered. A survey of experts was carried out, and a Kano model was built to prioritize the characteristics that influence decision-making on the or-ganization of an information interaction scheme in the Internet of Things network.

AI-powered malware detection with Differential Privacy for zero trust security in Internet of Things networks

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from ϵ=0.1 to ϵ=1.0. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model.

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental transformation in the way contemporary society functions. The security of emerging technologies such as IoT has become a significant challenge due to the added complexities, misconfigurations, and conflicts between modern and legacy systems. This challenge has a notable impact on the reliability and accessibility of existing infrastructure. Edge computing (EC) is a collaborative computing system that brings data processing and analysis closer to the edge of the network, where the data is generated, rather than in a centralized cloud environment. The utilization of the IoT has become more prevalent in both everyday life and the manufacturing sector, with a particular emphasis on critical infrastructure. The IoT is presently being utilized across diverse domains, including but not limited to industrial, agricultural, healthcare, and logistical sectors. The security of IoT networks has implications for the safety of individuals, the security of the nation, and economic development. Notwithstanding, conventional intrusion detection techniques that rely on centralized cloud-based systems that have been suggested in previous studies for IoT network security are insufficient to meet the requirements for data confidentiality, network capacity, and prompt responsiveness. In addition, the integration of IoT applications into smart devices has been shown to augment their functionalities. However, it is important to note that this integration also brings about potential security vulnerabilities. Furthermore, a significant number of contemporary IoT devices exhibit restricted security capabilities, rendering them vulnerable to intricate attacks and impeding the extensive integration of IoT technologies. Also, a lot of IoT network devices have been put in place that don't have hardware security measures. This means that traditional intrusion detection systems (IDS) aren't enough to protect the IoT network ecosystem. To address these issues, this research suggests the IoT-Defender framework, which combines a Modified Genetic Algorithm (MGA) model with a deep Long Short-Term Memory (LSTM) network to find cyberattacks in IoT networks. This research represents a pioneering attempt to employ the MGA for feature selection and the GA for fine-tuning the LSTM parameters within an EC framework. The parameters of the LSTM model were fine-tuned through the manipulation of the number of hidden layers, utilizing the GA fitness function. The customization of the MGA aimed to enhance its performance in selecting relevant features, optimizing the use of limited resources on IoT devices and edge nodes. The fine-tuning process involved optimizing hyperparameters, architecture, and training strategies to maximize the LSTM network's effectiveness in learning and detecting patterns in IoT network traffic. The synergy between the MGA and LSTM aimed at creating a comprehensive and efficient IDS. The feature selection by the MGA contributes to improving the LSTM’s performance by providing it with more relevant and discriminating features. In order to solve the issue of class imbalance, we utilize the focal loss function, which provides greater weights to minority classes, hence improving the model’s capacity to learn from those particular classes. The performance of the IoT-Defender model was assessed on the BoT-IoT, UNSW-NB15, and N-BaIoT datasets utilizing a Raspberry Pi IoT device. The results of our study show that the IoT-Defender model works better than other methods. This is shown by its accuracy score of 99.41%, detection rate of 99.78%, precision score of 98.50%, false alarm rate of 2.56%, mean intersection over union (mIoU) of 0.68, and training time of 81.3 seconds on BoT-IoT. The proposed IoT model is designed to be lightweight and can be installed on edge servers to detect cyber-attacks in real-time, specifically in the context of IoT security.

Multi-Objective Evolutionary Algorithm to Optimize IoT Based Scheduling Problem Using (NSGA-II Algorithm)

Due to the continual advancements in the Internet of Things (IoT), which generate enormous volumes of data, the cloud computing infrastructure recently has received the most significance. to meet the demands made by the network of IoT devices. It is anticipated that the planned Fog computing system would constitute the next development in cloud computing. The optimal distribution of computing capacity to reduce processing times and operating costs is one of the tasks that fog computing confronts. In the IoT, fog computing is a decentralized computing approach that moves data storage and processing closer to the network's edge. This research article discusses a unique technique for lowering operating expenses and improving work scheduling in a cloud-fog environment. Non-dominated sorting genetic algorithm II (NSGA-II) is a proposal that is presented in this paper. Its purpose is to allocate service requests with the multi-objective of minimising finishing time and running cost. Determining the Pareto front that is associated with a group of perfect solutions, which are sometimes referred to as non-dominated solutions or Pareto sets, is the fundamental objective of the Pareto NSGA-II. There is a contradiction between the environmental and economic performances, which is shown by the Pareto set of sub-optimal solutions that are the consequence of the bi-objective issue.

Hybrid computing framework security in dynamic offloading for IoT-enabled smart home system.

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10-20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5-15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10-12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8-11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance.

Application of IoT network for marine wildlife surveillance

Abstract Every day of the week, wireless communication is almost all around us. The Internet of Things (IoT) is a standard protocol used to describe the rapidly advancing technology in which almost every electronic device is or may be connected to the Internet. These electronic gadgets constantly provide data signals to the gateways, which satellites such as those in Low Earth Orbit may transmit. Because of these networks’ limited resources and the IoT, these transactions must be completed with the least amount of latency and data loss possible. We also analyze the performance implications of implementing RF-based powering for such a network. The techniques presented in this paper may benefit the scientific community and industry in general when it comes to the dynamic distributed parameter allocation methodology for IoT network devices. We will also discuss how research on animals and the natural environment has been impacted by IoT breakthroughs, in particular, animal sensors’ limits and incapacity to broadcast from everywhere. Our analysis illustrates the most effective data transmission technique and establishes the bounds of these restrictions. Furthermore, the physics of the RF channel plays a critical role in the uncertainty of the channel as well as the amount of energy harvested. By employing simulation based on the physics of the RF channel, the article shows the performance of the system considering both the uncertainty of data arrival as well as the variability of the channel. The findings of the simulation show that the devices consume less energy overall as the signal-to-noise ratio rises. Furthermore, a timing factor of 10–15% is shown to be effective in maintaining a constant mean rate and increasing the energy efficiency of the system.

Classification of cyber threats for internet of things

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

Deep reinforcement learning based efficient access scheduling algorithm with an adaptive number of devices for federated learning IoT systems

With the widespread application of Internet of Things (IoT), its energy consumption is becoming a major concern. Federated Learning (FL) allows multiple low-power devices to collaboratively learn a shared model, which reduces the energy consumption of IoT systems. However, FL involves highly varied data structures and require large amounts of communication. The communication efficiency greatly affects the system performance. In this paper, we optimize communication efficiency in the device access layer. Considering the instability of IoT device network connections, we propose a Deep Reinforcement Learning (DRL) based Efficient Access Scheduling Algorithm (DRL-EASA). It can adapt to changes in IoT device numbers and densities and thus applicable to instantaneous dynamic FL IoT systems. In scale-changed scenarios, DRL-EASA constructs geospatial-oriented state spaces and utilizes learning algorithm to train access scheduling strategies, effectively decoupling device numbers from the algorithm’s network structure. Firstly, the geographic region is divided into grids and the User State Information (USI) is mapped into fixed-dimensional Geographic State Information Vectors (GSIV). Secondly, a Convolutional Neural Network (CNN) is used as an agent to extract interference feature information from GSIV, and the Proximal Policy Optimization (PPO) algorithm is utilized to train the agent. Finally, a random algorithm is employed to assist DRL in generating scheduling decisions, enhancing the algorithm’s generalization performance in high device density scenarios. Numerical results show that our approach optimizes access scheduling strategies for both uplink and downlink and demonstrates effective adaptability to dynamically changing device numbers and exhibits strong generalization performance across various device densities.

Differentially Private Crowdsourcing With the Public and Private Blockchain

As a result of the rapid development of IoT systems, an increasing number of academics are focusing on finding new applications for IoT systems. For Internet of Things (IoT) systems, crowdsourcing is a prevalent practise. Due to the large number of deployed devices in IoT networks, more research is still required on the privacy and trust issues that arise when utilising crowdsourcing. As a result of the characteristics of social computing, the crowdsourcing network poses issues in terms of confidentiality and reliability. To consolidate and create this industry, we have built a differentially private crowdsourcing system that integrates public and private blockchains to address the privacy and trust issues of conventional crowdsourcing systems. Our proposed solution enables varying levels of privacy protection to protect the user’s identity and location. Moreover, the installation of blockchain networks might potentially ensure the data’s integrity. In the conclusion of this article, the possibility of deploying a crowdsourcing system with blockchain in IoE networks is examined.

An SDN perspective IoT-Fog security: A survey

The utilization of the Internet of Things (IoT) has burst in recent years. Fog computing is a notion that solves cloud computing’s limitations by offering low latency to IoT network user applications. However, the significant number of networked IoT devices, the large scale of the IoT, security concerns, users’ critical data, and heterogeneity in this extensive network significantly complicate the implementation. The IoT-Fog architecture consists of fog devices (servers) at the fog layer, which decreases network utilization and response time due to their closeness to IoT devices. However, as the number of IoT and fog devices under the IoT-Fog architecture grows, new security concerns and requirements emerge. Because incorporating fog computing into IoT networks introduces some vulnerabilities to IoT-Fog networks, the nodes in the fog layer are the target of security threats. Software-Defined Networking (SDN) is a novel paradigm that decouples the data plane from control plane, resulting in better programmability and manageability. Attack defense mechanisms can be implemented in the IoT-Fog network without SDN. But SDN paradigm provides the IoT-Fog with some characteristics that facilitate counterattacks. This survey briefly explains some works that utilized the SDN features in the IoT-Fog network for security threats in the IoT-Oriented fog layer. To this end, we examine IoT-Fog, SDN, and SDN-based IoT-Fog networks. We describe security threats in IoT-Fog networks and briefly explain the vulnerabilities and attacks in the fog layer. Then, we describe the fog layer’s most common IoT-Fog security defense mechanisms. Following that, we present the SDN features, explore how SDN can help defensive mechanisms in IoT-Fog networks, and categorize the works based on the SDN features they use. We explain their features and present a comparison between them. Finally, we discuss the disadvantages of SDN in IoT-Fog networks.

Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.

Implementation of blockchain technology using extended CNN for lung cancer prediction

Early identification of lung cancer is essential since the disease progresses quickly. Early-stage lung cancer diagnosis will be the first usage of the Internet of Things (IoT). With a worldwide network of IoT devices and a high degree of trust in the model's accuracy, on-the-fly training for IoT devices is very essential. As many as a million lives are saved each year because to early detection of illness, which seals the airways and prevents infection. Image processing and machine learning techniques provided the first evidence of malignant growth. Symptoms of lung cancer generally don't show up until the disease has advanced very far. At this stage, getting medical help becomes quite difficult. A whistling sound, hoarseness, weight gain in the face and/or an increase in the size of the upper chest may appear first, followed by the curling or rising of your fingers or the experience of pain when swallowing. Sputum with a red or rust-colored hue is a sign of malignancy, as is shortness of breath and chronic chest pain. In addition to identifying and arranging lung knobs, a lung computed tomography image may also be utilised to estimate their risk level. Preparation does not have as much of an impact on ECNN's accuracy and temporal complexity as it did on previous frameworks. They are made up of abnormal cells that form a tumour. An uncontrolled development and destruction of the lungs. Various kinds of lung cancer begin to develop as a result of this process, which continues until a tumour forms. Lung cells are damaged when they come into contact with airborne contaminants. + The new approach offered is ECNN+.

Blockchain Technology: A Panacea for IoT Security Challenge

The Internet of Things (IoT) platforms, despite the wide range of application is not without loop holes of which cyberattackers can take advantage. In order to improve the platform's security while also increasing other features, it has been proposed that blockchain technology be implemented in any IoT system. However, while blockchain technology has many advantages, it is important to consider other options because they all have their own drawbacks that may not be ideal for every use case situation. IoT network devices have limited computer power, storage space, and bandwidth. As a result, these systems are easily prone to assault than other network connected devices, such PCs, cell phones and tablets. With focus on IoT security challenges and the countermeasures offered by the blockchain technology, consensus algorithm, data encryption and smart contracts were discovered to be the common and effective algorithm employed by the blockchain technology in securing Iot systems over time.

Device models and technologies of data transmission and processing in the internet of things networks

The subject of research is the analysis and definition of the information model of devices, the choice of technologies and protocols for data collection and analysis, routing protocols for the Internet of Things (IoT) networks. The purpose of the article is to create an information model of IoT devices, to define technologies and protocols for data transmission and processing and routing in Internet of Things systems. Building an information model of an IoT device includes identifying data sources and their formats, creating a model and data structure, and analyzing them using tools such as: Eclipse Vorto ThingBoard, Ubodots IoT, Node-Red-UI, freeboard.io . An example of a description of a model of microclimate control devices using the Eclipse Vorto toolkit is given. Two main data processing technologies from IoT devices are considered: IBM Watson IoT, Cisco IWF with examples of their application. Radio frequency, infrared, optical and galvanic technologies for interaction between data collection and data transmission devices in IoT networks with examples of their use are discussed. The areas of application of three main IoT network protocols are considered: MQTT (large corporate networks), CoAP (limited networks on the Internet), Bluetooth Low Energy (local networks without the Internet with a small amount of data).Wi-Fi WebSockets, ZigBee, LoRA, Simple RF, XMPP, RFID, NFC can be used as additional protocols for networks. The features of using routing protocols in IoT networks are discussed: RPL (low power consumption), cognitive RPL (for intelligent networks), CARP (for underwater communication networks), E-CARP (for data reuse).

Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT

The Internet of Things (IoT) has subsequently been applied to a variety of sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the Internet of Things has enormous promise in a wide range of applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing the energy consumption of devices in IoT networks, which will result in a longer network lifetime. The far more suitable Cluster Head (CH) throughout the IoT system is determined in this study to optimize energy consumption. Whale Optimization Algorithm (WOA) with Evolutionary Algorithm (EA) is indeed a mixed meta-heuristic algorithm used during the suggested study. Various quantifiable metrics, including the variety of adult nodes, workload, temperatures, remaining energy, and a target value, were utilized IoT network groups. The suggested method then is contrasted to several cutting-edge optimization techniques, including the Artificial Bee Colony method, Neural Network, Adapted Gravity Simulated annealing. The findings show that the suggested hybrid method outperforms conventional methods.

Ultra-Low-Power IoT Communications: A Novel Address Decoding Approach for Wake-Up Receivers

Providing energy-efficient Internet of Things (IoT) connectivity has attracted significant attention in fifth-generation (5G) wireless networks and beyond. A potential solution for realizing a long-lasting network of IoT devices is to equip each IoT device with a wake-up receiver (WuR) to have always-accessible devices instead of always-on devices. WuRs typically comprise a radio frequency demodulator, sequence decoder, and digital address decoder and are provided with a unique authentication address in the network. Although the literature on efficient demodulators is mature, it lacks research on fast, low-power, and reliable address decoders. As this module continuously monitors the received ambient energy for potential paging of the device, its contribution to WuR’s power consumption is crucial. Motivated by this need, a low-power, reliable address decoder is developed in this paper. We further investigate the integration of WuR in low-power uplink/downlink communications and, using system-level energy analysis; we characterize operation regions in which WuR can contribute significantly to energy saving. The device-level energy analysis confirms the superior performance of our decoder. The results show that the proposed decoder significantly outperforms the state-of-the-art with power consumption of 60 nW, at cost of compromising negligible increase in decoding delay.

MidSiot: A Multistage Intrusion Detection System for Internet of Things

Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute-force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC-IDS-2017, and BOT-IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state-of-the-art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

Early Generation and Detection of Efficient IoT Device Fingerprints Using Machine Learning

The proliferation of Internet of Things (IoT) markets in the last decade introduces new challenges for network traffic analysis, and processing packet flows to identify IoT devices. This type of device suffers from scarcity, making them vulnerable to spoofing operations. In such circumstances, the device can be recognized by identifying its fingerprint. In this paper, a novel idea to elicit Device FingerPrint (DFP) is presented by extracting 30 features from the collected traffic packets of 19 IoT devices during setup and startup operations. Raspberry Pi 3 Model B+ is configured as an access point to collect and analyze the traffic of seven networked IoT devices using Wireshark Network Protocol Analyzer. Moreover, the rest of IoT devices traffic is taken from the publicly available network traffic dataset. Each IoT device's feature extraction process starts from getting Extensible Authentication Protocol over LAN (EAPOL) protocol, continuing with the other flowed protocols until the first session of Transmission Control Protocol (TCP) related to that device is closed. Depending on some produced variation of device traffic features, 20 fingerprints for each device are created. The probability theorem of Gaussian Naive Bayes (GNB) supervised machine learning is utilized to identify fingerprints of individual known devices and isolate the unknown ones. The performance evaluation for the proposed technique was calculated based on two measures, F1-score and identification accuracy. The average F1 score was around 0.99, while the overall identification accuracy rate was 98.35%.

SENMQTT-SET: An Intelligent Intrusion Detection in IoT-MQTT Networks Using Ensemble Multi Cascade Features

Recently, the number of Internet of Things (IoT) networks has been grown exponentially, which results in more data sharing between devices without appropriate security mechanisms. Since huge data management is involved, maintaining the time constraints between the devices in IoT networks is another significant issue. To address these issues, an intelligent intrusion detection system has been adapted to recognize or predict a cyber-attack using Elite Machine Learning algorithms (EML), and a lightweight protocol is used to manage the time-constrained issue. The experimental analysis of work is done on a testbed setup with the hardware and sensors connected using a lightweight Message Queue Telemetry Transport (MQTT) protocol. This comprises three parts: (i) collection of data with the help of a sensor for three different scenarios called SEN-MQTTSET; (ii) multi-context feature generation using an ensemble statistical multi-view cascade feature generation algorithm from the SEN-MQTTSET dataset; and (iii) evaluating the dataset using ML algorithms. The SEN-MQTTSET dataset has been created from the three scenarios, such as normal, attack on a subscriber, and attack on a broker. The multi-context feature is generated from the raw dataset using an ensemble statistical multi-view cascade feature generation algorithm. The EML is proposed to select the best model for intrusion detection among ML algorithms such as Logistic Regression, K-Nearest Neighbour, Random Forest, Naive Bias, Support Vector Machine, Gradient Boosting, and Decision Tree by the performance metrics such as accuracy, prediction time, F1-score, and others. The proposed dataset is validated and the accuracy is found to be above 99% for the considered system model. Different quality parameters have been carried out for legitimate and attack traffic features to calculate the delay between the IoT-MQTT network.