Designing integrated model for intrusion detection (IM-ID) for internet of things (IoT) using deep learning techniques

ABSTRACTThe Internet of Things (IoT) has transformed technology interactions by connecting devices and facilitating information exchange. However, IoT's interconnectivity presents significant security challenges, including network security, device vulnerabilities, data confidentiality, and authentication. Many IoT devices lack strong security measures, making them susceptible to misuse. Additionally, privacy concerns arise due to sensitive data storage. Solutions such as secure authentication, encryption, and encrypted communication are vital. Intrusion detection systems (IDS) play a crucial role in proactively protecting networks, yet they encounter significant challenges in identifying new intrusions and minimizing false alarms. To tackle these issues, researchers have developed IDS systems that leverage machine learning (ML) and deep learning (DL) techniques. This survey article not only provides an in‐depth analysis of current IoT IDS but also summarizes the techniques, deployment strategies, validation methods, and datasets commonly used in the development of these systems. A thorough analysis of modern Network Intrusion Detection System (NIDS) publications is also included, which evaluates, examines, and contrasts NIDS approaches in the context of the IoT with regard to its architecture, detection methods, and validation strategies, dangers that have been addressed, and deployed algorithms setting it apart from earlier surveys that predominantly concentrate on traditional systems. We concentrate on IoT NIDS implemented by ML and DL in this survey given that learning algorithms have an excellent track record for success in security and privacy. The study, in our opinion, will be beneficial for academic and industrial research in identifying IoT dangers and problems, in implementing their own NIDS and in proposing novel innovative techniques in an IoT context while taking IoT limits into consideration.

Due to the development in various tools and deep learning (DL) techniques that might be helpful in evaluating Internet of Things (IoT) big data, the integration of the IoT with DL has magnetically drawn the attention of the research community. On the other hand, as the number of IoT installations at the ground level grows, so does the number of data sources that are continuously generated. This gives rise to IoT big data, which can be employed in a variety of fields when examined using DL tools and methodologies. The proposed study provides a comprehensive overview and deep dive into the field of DL techniques that can be used for IoT big data analytics. Tables are used to identify, review, and summarize various articles on IoT-DL integration. There is also a rundown of big data-enabled IoT devices. DL is used to illustrate several new methodologies for IoT big data analytics, emphasizing the importance of DL in the context. Benefits and challenges in using DL techniques for IoT big data are discussed. Lastly, some recent work published on DL is explored to illustrate the advantages and disadvantages of using DL techniques.

The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.

Hybrid intelligent intrusion detection system for internet of things

The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.

When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensuring its security is paramount. However, the vast amount of data generated by IoT devices makes it challenging to manage QoS using conventional techniques, particularly when attempting to extract valuable characteristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data samples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in improving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms.

When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensur-ing its security is paramount. However, the vast amount of data generated by IoT devices makes it challeng-ing to manage QoS using conventional techniques, particularly when attempting to extract valuable charac-teristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data sam-ples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in im-proving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms.

Technologically speaking, humanity lives in an age of evolution, prosperity, and great development, as a new generation of the Internet has emerged; it is the Internet of Things (IoT) which controls all aspects of lives, from the different devices of the home to the large industries. Despite the tremendous benefits offered by IoT, still there are some challenges regarding privacy and information security. The traditional techniques used in Malware Anomaly Detection Systems (MADS) could not give us as robust protection as we need in IoT environments. Therefore, it needed to be replaced with Deep Learning (DL) techniques to improve the MADS and provide the intelligence solutions to protect against malware, attacks, and intrusions, in order to preserve the privacy of users and increase their confidence in and dependence on IoT systems. This research presents a comprehensive study on security solutions in IoT applications, Intrusion Detection Systems (IDS), Malware Detection Systems (MDS), and the role of artificial intelligent (AI) in improving security in IoT.

The Internet of Things (IoT) has transformed many aspects of modern life, from healthcare and transportation to home automation and industrial control systems. However, the increasing number of connected devices has also led to an increase in security threats, particularly from botnets. To mitigate these threats, various machine learning (ML) and deep learning (DL) techniques have been proposed for IoT botnet attack detection. This systematic review aims to identify the most effective ML and DL techniques for detecting IoT botnets by delving into benchmark datasets, evaluation metrics, and data pre-processing techniques in detail. A comprehensive search was conducted in multiple databases for primary studies published between 2018 and 2023. Studies were included if they reported the use of ML or DL techniques for IoT botnet detection. After screening 1,567 records, 25 studies were included in the final review. The findings suggest that ML and DL techniques show promising results in detecting IoT botnet attacks, outperforming traditional signature-based methods. However, the effectiveness of the techniques varied depending on the dataset, features, and evaluation metrics used. Based on the synthesis of the findings, this review proposes a taxonomy for ML and DL techniques in IoT botnet attack detection and provides recommendations for future research in this area. This review illuminates the considerable potential of ML and DL approaches in bolstering the detection of IoT botnet attacks, thereby offering valuable insights to researchers involved in the domain of IoT security.

Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative "things" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.

In the era of Internet of Things (IoT), the connection links are established from devices easily, which is vulnerable to insecure attacks from intruders, hence intrusion detection system in IoT is the need of an hour. One of the important thing for any organization is securing the confidential information and data from outside attacks as well as unauthorized access. There are many attempts made by the researchers to develop the strong intrusion detection system having high accuracy. These systems suffer from many disadvantages like unacceptable accuracy rates including high False Positive Rate (FPR) and high False Negative Rate (FNR), more execution time and failure rate. More of these system models are developed by using traditional machine learning techniques, which have performance limitations in terms of accuracy and timeliness both. These limitations can be overcome by using the deep learning techniques. Deep learning techniques have the capability to generate highly accurate results and are fault tolerant. Here, the intrusion detection model for IoT is designed by using the Taylor-Spider Monkey optimization (Taylor-SMO) which will be developed to train the Deep belief neural network (DBN) towards achieving an accurate intrusion detection model. The deep learning accuracy gets increased with increasing number of training data samples and testing data samples. The optimization based algorithm for training DBN helps to reduce the FPR and FNR in intrusion detection. The system will be implemented by using the NSL KDD dataset. Also, this model will be trained by using the samples from this dataset, before which feature extraction will be applied and only relevant set of attributes will be selected for model development. This approach can lead to better and satisfactory results in intrusion detection.

The Internet of Things (IoT) has emerged as a revolutionary solution that enables seamless connection and access to various devices through the Internet. With each passing day, there is a significant increase in the number of IoT devices, encompassing diverse shapes, sizes, functionalities, and complexities. While IoT technology offers an extensive range of services and applications that greatly enhance people's lives across different domains, it also exposes several security vulnerabilities. These vulnerabilities can be exploited by malicious actors for activities like sinkhole attacks, eavesdropping or denial-of-service attacks, etc. To counter these threats and ensure network security integrity when breaches occur in an IoT environment, intrusion detection systems are employed. Deep learning techniques have proven to be highly effective in enhancing the capabilities of such systems by enabling them to detect IoT-specific attacks and identify novel types of intrusions. This paper presents a model for intrusion detection in the IoT based on edge computing. The model utilizes gated convolution to improve the performance of the convolution neural network (CNN) in detecting anomalies and effectively mitigating DDoS attacks. The feasibility of this approach is evaluated through binary and multi-class classification tasks, including 8-class and 13-class scenarios. Experimental validation using the CICDDoS2019 dataset demonstrates that the proposed intelligent Intrusion Detection System achieves high accuracy rates of 99.68% for binary classes, 99.90% for 8-classes, and 99.95% for 13-classes when identifying various types of DDoS attacks. This research highlights how this method can better fulfill IoT intrusion detection requirements.

Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

Rapid advancements in communication technology have supported the invention of various internet-based devices. These devices communicate with one another and provide data from the physical world. Nowadays, the internet connected devices are used in various fields to make things easier. A great number of devices has been used, depending upon requirements. At the same time, the data produced by such devices is gradually increasing. To process the collected data, machine learning and deep learning techniques are applied. The Internet of Things (IoT) produces big datasets with multiple modalities but also a range of data with different quality standards. It is an important but also a challenging task to process all of the data within a certain time-frame. In this scenario, cloud computing gives us the optimal solution since the data generated is sent to distant cloud infrastructures. In addition to the cloud technology, machine learning (ML) and deep learning (DL) techniques are integrated with cloud computing to improve the effectiveness. In ML technique, the training data is given for learning to generate a set of rules from inferences on the data. Huge amounts of data that has been stored in the cloud gives input to DL techniques. DL architecture has been derived from the Artificial Neural Network (ANN) that uses multiple layers of nonlinear processing and transformation. The deep learning approach uses unknown elements in the input data to group objects, generate features and find new data patterns to build the model.

The devices of the Internet of Things (IoT) are facing various types of attacks, and IoT applications present unique and new protection challenges. These security challenges in IoT must be addressed to avoid any potential attacks. Malicious intrusions in IoT devices are considered one of the most aspects required for IoT users in modern applications. Machine learning techniques are widely used for intelligent detection of malicious intrusions in IoT. This paper proposes an intelligent detection method of malicious intrusions in IoT systems that leverages effective classification of benign and malicious attacks. An ensemble approach combined with various machine learning algorithms and a deep learning technique, is used to detect anomalies and other malicious activities in IoT. For the consideration of the detection of malicious intrusions and anomalies in IoT devices, UNSW-NB15 dataset is used as one of the latest IoT datasets. In this research, malicious and normal intrusions in IoT devices are classified with the use of various models. %Moreover, improved results are provided and compared with CorrAuc [1] for training accuracies, cross-validation accuracies, execution time, precision, recall and F1 score.