Secure Internet Of Things Networks Research Articles

Performance Evaluation of Deep Learning Models for Classifying Cybersecurity Attacks in IoT Networks

The Internet of Things (IoT) presents great potential in various fields such as home automation, healthcare, and industry, among others, but its infrastructure, the use of open source code, and lack of software updates make it vulnerable to cyberattacks that can compromise access to data and services, thus making it an attractive target for hackers. The complexity of cyberattacks has increased, posing a greater threat to public and private organizations. This study evaluated the performance of deep learning models for classifying cybersecurity attacks in IoT networks, using the CICIoT2023 dataset. Three architectures based on DNN, LSTM, and CNN were compared, highlighting their differences in layers and activation functions. The results show that the CNN architecture outperformed the others in accuracy and computational efficiency, with an accuracy rate of 99.10% for multiclass classification and 99.40% for binary classification. The importance of data standardization and proper hyperparameter selection is emphasized. These results demonstrate that the CNN-based model emerges as a promising option for detecting cyber threats in IoT environments, supporting the relevance of deep learning in IoT network security.

Towards secure IoT networks: A comprehensive study of metaheuristic algorithms in conjunction with CNN using a self-generated dataset

The Internet of Things (IoT) has radically reformed various sectors and industries, enabling unprecedented levels of connectivity and automation. However, the surge in the number of IoT devices has also widened the attack surface, rendering IoT networks potentially susceptible to a plethora of security risks. Addressing the critical challenge of enhancing security in IoT networks is of utmost importance. Moreover, there is a considerable lack of datasets designed exclusively for IoT applications. To bridge this gap, a customized dataset that accurately mimics real-world IoT scenarios impacted by four different types of attacks—blackhole, sinkhole, flooding, and version number attacks was generated using the Contiki-OS Cooja Simulator in this study. The resulting dataset is then consequently employed to evaluate the efficacy of several metaheuristic algorithms, in conjunction with Convolutional Neural Network (CNN) for IoT networks. •The proposed study's goal is to identify optimal hyperparameters for CNNs, ensuring their peak performance in intrusion detection tasks.•This study not only intensifies our comprehension of IoT network security but also provides practical guidance for implementation of the robust security measures in real-world IoT applications.

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.

Hybrid Data Augmentation Based Machine Learning Approach for Botnet Attack Detection in IOT Networks

This paper presents a comprehensive approach to botnet detection in Internet of Things (IoT) networks through the development and evaluation of a Generative Adversarial Network (GAN) augmented machine learning model. The methodology encompasses a multi-step process, starting with data collection and pre-processing, including feature extraction, normalization, and handling missing values. To address the challenge of data imbalance, a novel application of GANs is proposed. For classification of network traffic into botnet and legitimate traffic is performed using xgboost. The performance of the proposed model is rigorously evaluated using the N-BaIoT dataset, demonstrating its effectiveness through high accuracy, precision, recall, and F1-score metrics. The results indicate significant improvements over existing models, showcasing the potential of the proposed methodology in enhancing IoT network security against botnet threats.

IoT Threat Mitigation: Leveraging Deep Learning for Intrusion Detection

The growth of smart gadgets connected via the Internet of Things (IoT) in today’s modern technology landscape has substantially improved our everyday lives. However, this convenience is juxtaposed with a concomitant surge in cyber threats capable of compromising the integrity of these interconnected systems. Conventional intrusion detection systems (IDS) prove inadequate for IoT due to the unique challenges they present. We propose and evaluate an intrusion detection system (IDS) based on a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model in this paper. The model is designed to capture both temporal and spatial patterns in network data, offering a robust solution for detecting malicious activities within IoT environments. The CNN-LSTM model displayed excellent accuracy, reaching 98% in both multi-class and binary classifications when trained on the UNSW-NB15 dataset. Furthermore, we explore the real-world applicability of the model through testing on Raspberry Pi, showcasing its effectiveness in IoT scenarios. The system is augmented with alert mechanisms, promptly notifying relevant parties upon intrusion detection. Our findings highlight the CNN-LSTM model's efficacy in strengthening IoT network security.

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.

The security of IOT from the perspective of the observability of complex networks

The Internet of Things (IOT) is based on the computer Internet, using RFID, wireless data communication and other technologies to construct a network covering everything in the world. It contains numerous entities such as sensors, processors, transmitters and actuators, meanwhile the interactions of which are complicated. These characteristics of IOT are consistent with those of the complex network. Motivated by this, this paper comprehends the security issue of IOT from the sight of the observability of complex network and regards the ability of reconstruction as a security threat to IOT network. We try to identify the minimum vertices whose data could reconstruct the whole data of network, in other words, we need to implement additional protective measures on these vertices to enhance the security of IOT network. By analyzing the topology of IOT network, an identification strategy is adopted and the corresponding algorithm is proposed to identify the minimum protection vertices.

GEMLIDS-MIOT: A Green Effective Machine Learning Intrusion Detection System based on Federated Learning for Medical IoT network security hardening

The increasing use of Internet of Things (IoT) gadgets in a daily rate has heightened security apprehension, particularly within the healthcare sector. In order to prevent the unauthorized disclosure of sensitive data, it is imperative for Internet of Things (IoT) systems to promptly and effectively respond to harmful activities. Nevertheless, the act of transferring data to distant cloud servers for the purpose of analysis gives rise to both temporal delays and apprehensions regarding privacy. To ensure the security of medical Internet of Things (MIoT) networks, a power-efficient Intrusion Detection System (IDS) is employed for three primary objectives that it will result in three stages of execution: (i) The objective is to categorize different types of attacks, such as Man-in-the-Middle (MitM) and Distributed Denial of Service (DDoS), by utilizing well-established machine learning (ML) techniques. This classification stage will serve to enhance the Intrusion Detection System (IDS) and the reporting system. (ii) Anomaly detection (unknown attack identification), or detection of unknown attacks, will be employed to identify previously unknown attacks. This identification stage involves retraining the ML model to enable future recognition and classification of these unknown attacks when the anomaly attack detector identifies that an unknown attack is recognized. Then, a retraining of the first stage classification model is executed due to the anomaly detection. (iii) To ensure that a remote cloud server remains current with the latest classification model changes, Federated Learning (FL) will be utilized. FL allows for collaborative model training while preserving data privacy and security. The experimental findings indicate that the Enhanced Random Forest (also called ensemble random forest) algorithm achieves a remarkable accuracy rate of 99.98% in classifying attacks. Thus, it will be our first stage classifier. Continuing, the One-Class Support Vector Machine (SVM) algorithm demonstrates a high level of accuracy, reaching 99.7% in detecting anomalies so that it will be our second stage identifier. Finally, the third-stage approach, which has as a target the overall system model updater, will be our introduced Federated Learning approach that works with the Enhanced Random Forests and identifies the ERF differences from the old model in an optimal way. The efficacy of our technique is confirmed through the implementation of experiments involving an Internet of Things (IoT) system and a Raspberry Pi MIoT gateway and with simulations that simulate the FL updating process. These experiments successfully identify known and unknown attacks with a high reliability level while limiting resource utilization and energy consumption. Future studies of this work will focus on enhancing the scalability and efficiency of our Intrusion Detection System in MIoT networks.

Anomaly Detection of IoT Cyberattacks in Smart Cities Using Federated Learning and Split Learning

The swift proliferation of the Internet of Things (IoT) devices in smart city infrastructures has created an urgent demand for robust cybersecurity measures. These devices are susceptible to various cyberattacks that can jeopardize the security and functionality of urban systems. This research presents an innovative approach to identifying anomalies caused by IoT cyberattacks in smart cities. The proposed method harnesses federated and split learning and addresses the dual challenge of enhancing IoT network security while preserving data privacy. This study conducts extensive experiments using authentic datasets from smart cities. To compare the performance of classical machine learning algorithms and deep learning models for detecting anomalies, model effectiveness is assessed using precision, recall, F-1 score, accuracy, and training/deployment time. The findings demonstrate that federated learning and split learning have the potential to balance data privacy concerns with competitive performance, providing robust solutions for detecting IoT cyberattacks. This study contributes to the ongoing discussion about securing IoT deployments in urban settings. It lays the groundwork for scalable and privacy-conscious cybersecurity strategies. The results underscore the vital role of these techniques in fortifying smart cities and promoting the development of adaptable and resilient cybersecurity measures in the IoT era.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

A smart decentralized identifiable distributed ledger technology‐based blockchain (DIDLT‐BC) model for cloud‐IoT security

AbstractThe most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

Detection of Botnet in the loT Network

The ubiquity of Internet of Things (IoT) devices has prompted security concerns, particularly in the face of evolving botnet attacks. This paper investigates the impact of botnet attacks on IoT devices and proposes a network-based detection and prevention system employing signature and anomaly-based mechanisms. Notably, our methodology extends beyond traditional detection, focusing on proactively impeding bot creation. Leveraging a Linux-based distributed system, Security Information and Event Management (SIEM) tools, and custom rules, our approach encompasses distinct phases Preprocessing, Network Security Monitoring, Rule-based IDS System, and Analysis. Experimental results with diverse PCAP files demonstrate the efficacy of custom rules, significantly enhancing alert counts for various security aspects, including network trojan detection and privacy violations. The significant finding is the substantial increase in alert counts after the integration of custom rules, exemplified in the 1.1 GB PCAP file scenario. Network trojan detection surged from 585 to 988, emphasizing the heightened efficacy of rule-based measures. Privacy breaches and bad traffic alerts also experienced significant increments, showcasing the system’s improved sensitivity and responsiveness. This finding reinforces the pivotal role of custom rules in fortifying IoT network security comprehensively.

RAKSHAM: Responsive approach to Knock‐off scavenging hackers and attack mitigation

AbstractWith the increasing interconnectivity of devices and systems in the Internet of Things (IoT) landscape, the importance of cybersecurity has grown significantly. This work presents a comprehensive solution that combines deceptive techniques and AI‐based detection to strengthen the security of IoT networks against sophisticated cyber threats. Deceptive techniques, such as the utilization of honeypots, deceptive IoT devices, and deceptive network traffic, are deliberately employed with the aim of misleading potential attackers. However, the effectiveness of these systems is often challenged by highly skilled attackers and the complexities associated with allocating resources. To address these challenges, “RAKSHAM” is a unique approach to improving the security of IoT networks. It combines deceptive techniques with AI‐based detection mechanisms, minimizing false positives and operational outages in IoT networks. This approach utilizes Eigenvector Centrality (EVC) to identify nodes with a higher risk level. It also evaluates the performance of an AI‐based attack detection model. The AI model, which utilizes a Convolutional Neural Network (CNN), demonstrates exceptional efficacy in the detection of attacks with 99% accuracy and a minimal attack success probability rate of 0.10. RAKSHAM offers a pragmatic and efficient solution for securing IoT networks by accurately detecting attacks and minimizing false positives and operational outages.

Safeguarding the IoT: Taxonomy, security solutions, and future research opportunities

AbstractWith the advancement of innovative technologies, the Internet of Things (IoT) has become quite popular in various applications such as smart homes, smart cities, agriculture, healthcare, and so forth. However, the heterogeneity of IoT protocols can pose security challenges to wireless networks. Thus, we have presented a systematic review of IoT technologies such as ZigBee, Radio Frequency Identification (RFID), Bluetooth, WiFi, Ethernet, AR, embedded subscriber identification modules (E‐SIM), and smart sensors, along with their communication protocols, which are part of the IoT‐layered architecture. We have bifurcated the IoT‐layered architecture into the perception, network, and application layers. Furthermore, we have presented IoT security attacks associated with the perception, network, and application layers of the IoT‐layered architecture. Moreover, we have discussed several IoT security solutions, open issues, and research challenges dedicated to the security of IoT networks. IoT security solutions can provide secure and efficient data communication over wireless networks.

A Design of Improved Trust-based Clustering Approach to Enhance the Energy Computation in Secured Internet of Things (IoT)

The Internet of Things (IoT) is the trending area that occupies maximum of the applications of intelligent communication. The nodes in the IoT system are located in the monitoring environment to attain stable and reliable communication. In between the nodes and the sink, similar data is sensed and transmitted at maximum of the period and that leads to high energy consumption. On the other hand, it increases the security issues of the network. So the main objective is reducing power utilization and increasing the IoT network security. In this paper, we suggest an improved trust-based clustering approach to enhance energy computation and security (ITCES-IoT) in IoT networks which can able to reduce energy consumption and increase the security among the nodes in the network. We first introduced an effective system model that can able to reduce the transmitting and receiving power during communication. Secondly, the initial cluster setup phase, LEACH-based cluster head (CH) selection phase, and re-clustering principles are discussed. Then, the trust calculation is established for both the cluster members (CM) and the CH through direct and indirect trust calculation processes followed by the best solution of path selection is performed between the CM and the sink node. We constructed Bad mouthing attacks, Ballot stuffing attacks, and detection mechanisms with a Fast Entropy algorithm. Finally, intra-cluster and inter-cluster communication are elaborated in the communication phase. Through this process, the nodes and CHs can able to perform effective communication with minimum power utilization, and the nodes with the transmitted information are secured from the external environment. At the end stage, we validate our proposed ITCES-IoT approach in certain scenarios in network simulator NS2 and their performance analysis includes the parameters of packet delivery ratio, network throughput, and packet loss and energy consumption. Then the final outcomes are compared with the base methods such as ECET-IoT, DRTP-IoT, and RCDA-IoT. From the results it is understood that the proposed ITCES-IoT outperforms the baseline methods in terms of delivery ratio and throughput and as well it consumes less power at the time of data transmission.

Enhancing IoT Network Security: Unveiling the Power of Self-Supervised Learning against DDoS Attacks.

The Internet of Things (IoT), projected to exceed 30 billion active device connections globally by 2025, presents an expansive attack surface. The frequent collection and dissemination of confidential data on these devices exposes them to significant security risks, including user information theft and denial-of-service attacks. This paper introduces a smart, network-based Intrusion Detection System (IDS) designed to protect IoT networks from distributed denial-of-service attacks. Our methodology involves generating synthetic images from flow-level traffic data of the Bot-IoT and the LATAM-DDoS-IoT datasets and conducting experiments within both supervised and self-supervised learning paradigms. Self-supervised learning is identified in the state of the art as a promising solution to replace the need for massive amounts of manually labeled data, as well as providing robust generalization. Our results showcase that self-supervised learning surpassed supervised learning in terms of classification performance for certain tests. Specifically, it exceeded the F1 score of supervised learning for attack detection by 4.83% and by 14.61% in accuracy for the multiclass task of protocol classification. Drawing from extensive ablation studies presented in our research, we recommend an optimal training framework for upcoming contrastive learning experiments that emphasize visual representations in the cybersecurity realm. This training approach has enabled us to highlight the broader applicability of self-supervised learning, which, in some instances, outperformed supervised learning transferability by over 5% in precision and nearly 1% in F1 score.

An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach

The rapid proliferation of Internet of Things (IoT) devices has raised critical concerns regarding the security of corresponding IoT networks. The Routing Protocol for Low-Power and Lossy Networks (RPL), a foundational element in IoT communication, is susceptible to diverse routing attacks due to IoT nodes’ constrained resources and open nature. This underscores the necessity for an Intrusion Detection System (IDS) to safeguard RPL-based IoT networks. Existing anomaly-based IDS suffer from high false alarm rates (FAR). In response to these challenges, this paper presents the Ensemble Learning-based Intrusion Detection System (ELG-IDS), which employs stacking and extreme parameter optimization to detect three RPL internal attacks: version number, decreased rank, and DIS flooding attacks. ELG-IDS employs enhanced feature extraction and genetic algorithm (GA)-based feature selection. Experimental results on a dedicated dataset demonstrate ELG-IDS's remarkable accuracy: 99.18%, 99.38%, 99.66%, and 97.90% for version number, rank attack, DIS flooding, and an average accuracy of 97.90% in multi-classification mode, respectively. This study advances IoT network security through ELG-IDS, enhancing protection against evolving security challenges.

Generating Synthetic Dataset for ML-Based IDS Using CTGAN and Feature Selection to Protect Smart IoT Environments

Networks within the Internet of Things (IoT) have some of the most targeted devices due to their lightweight design and the sensitive data exchanged through smart city networks. One way to protect a system from an attack is to use machine learning (ML)-based intrusion detection systems (IDSs), significantly improving classification tasks. Training ML algorithms require a large network traffic dataset; however, large storage and months of recording are required to capture the attacks, which is costly for IoT environments. This study proposes an ML pipeline using the conditional tabular generative adversarial network (CTGAN) model to generate a synthetic dataset. Then, the synthetic dataset was evaluated using several types of statistical and ML metrics. Using a decision tree, the accuracy of the generated dataset reached 0.99, and its lower complexity reached 0.05 s training and 0.004 s test times. The results show that synthetic data accurately reflect real data and are less complex, making them suitable for IoT environments and smart city applications. Thus, the generated synthetic dataset can further train models to secure IoT networks and applications.

Enhancing IoT network security through deep learning-powered Intrusion Detection System

The rapid growth of the Internet of Things (IoT) has brought about a global concern for the security of interconnected devices and networks. This necessitates the use of efficient Intrusion Detection System (IDS) to mitigate cyber threats. Deep learning (DL) techniques provides a promising approach to effectively detect irregularities in network traffic, enhancing IoT network security and reducing cyber threats. In this paper, DL-based IDS is proposed using Feed Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), and Random Neural Networks (RandNN) to protect IoT networks from cyberattacks. Each DL model has its potential benefit as reported in this paper. For example, the FFNN can handle complex IoT network traffic patterns, while the LSTM is good in capturing long-term dependencies present in the network traffic. With its random connections and flexible dynamics, the RandNN model uses its data learning ability to adapt and learn from network data. These algorithms boost cybersecurity by enabling defense mechanisms against challenging cyber threats and ensuring the security of sensitive data as IoT networks expand. The proposed technique exhibits superior performance when compared with the current state-of-the-art DL-IDS using the CIC-IoT22 dataset. An accuracy of 99.93 % is achieved for the FFNN model, 99.85 % for the LSTM model, and 96.42 % for the RandNN model in detecting intrusion. Moreover, the models have the potential to enhance intrusion detection in IoT networks by generating swift responses to security problems in IoT networks.

Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things Traffic

The rapid development of Internet of Things (IoT) networks has revealed multiple security issues. On the other hand, machine learning (ML) has proven its efficiency in building intrusion detection systems (IDSs) intended to reinforce the security of IoT networks. In fact, the successful design and implementation of such techniques require the use of effective methods in terms of data and model quality. This paper encloses an empirical impact analysis for the latter in the context of a multi-class classification scenario. A series of experiments were conducted using six ML models, along with four benchmarking datasets, including UNSW-NB15, BOT-IoT, ToN-IoT, and Edge-IIoT. The proposed framework investigates the marginal benefit of employing data pre-processing and model configurations considering IoT limitations. In fact, the empirical findings indicate that the accuracy of ML-based IDS detection rapidly increases when methods that use quality data and models are deployed. Specifically, data cleaning, transformation, normalization, and dimensionality reduction, along with model parameter tuning, exhibit significant potential to minimize computational complexity and yield better performance. In addition, MLP- and clustering-based algorithms outperformed the remaining models, and the obtained accuracy reached up to 99.97%. One should note that the performance of the challenger models was assessed using similar test sets, and this was compared to the results achieved using the relevant pieces of research.