An automated intrusion detection system in IoT system using Attention based Deep Bidirectional Sparse Auto Encoder model

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

The internet of things (IoT) transforms the world in many ways. It combines many types of hardware and software with a variety of communication technologies to enable the development of innovative applications. A typical IoT system consists of IoT device, IoT gateway, IoT platform, and IoT application. Developing these elements and delivering an IoT system for fulfilling business requirements encompasses many activities to be executed and is not straightforward. To expedite these activities, some major vendors provide software development kits (SDK), integrated development environments (IDE), and utility tools for developing software to be executed on IoT devices/gateways. Moreover, these vendors utilize their cloud platforms to provide fundamental services, such as data storage, analytics, stream processing, for developing IoT systems. These vendors also developed IoT specific cloud-based services, such as connectivity and device management, to support IoT system development. This chapter presents an overview of tools and platforms provided by five major vendors.

Some Internet of Things (IoT) platforms use blockchain to transport data. The value proposition of IoT is the connection to the Internet of a myriad of devices that provide and exchange data to improve people’s lives and add value to industries. The blockchain technology transfers data and value in an immutable and decentralised fashion. Security, composed of both non-intentional and intentional risk management, is a fundamental design requirement for both IoT and blockchain. We study how blockchain answers some of the IoT security requirements with a focus on intentional risk. The review of a sample of security incidents impacting public blockchains confirm that identity and access management (IAM) is a key security requirement to build resilience against intentional risk. This fact is also applicable to IoT solutions built on a blockchain. We compare the two IoT platforms based on public permissionless distributed ledgers with the highest market capitalisation: IOTA, run on an alternative to a blockchain, which is a directed acyclic graph (DAG); and IoTeX, its contender, built on a blockchain. Our objective is to discover how we can create IAM resilience against intentional risk in these IoT platforms. For that, we turn to complex network theory: a tool to describe and compare systems with many participants. We conclude that IoTeX and possibly IOTA transaction networks are scale-free. As both platforms are vulnerable to attacks, they require resilience against intentional risk. In the case of IoTeX, DIoTA provides a resilient IAM solution. Furthermore, we suggest that resilience against intentional risk requires an IAM concept that transcends a single blockchain. Only with the interplay of edge and global ledgers can we obtain data integrity in a multi-vendor and multi-purpose IoT network.

The Internet of Things (IoT) platform consists of numerous IoT devices and a small number ofservers. In order for the platform to operate efficiently and reliably, servers need to have completecontrol over many IoT devices. The advantage of using network boot in this environment is thatthe server has control over the device’s boot process. When applying network boot on an IoT platform,the server needs to be able to handle the network boot that many IoT devices request. If theserver does not have enough processing power for the network boot request, IoT device boot wouldbe delayed. In this paper, we propose a resource-efficient network boot to solve this problem. Theproposed framework has three mechanism. First, the server reserves the right to control which systemimage to transfer during the network boot process on the platform. Second, the server appliesdeduplication to the system image to minimize the amount of data required for transmission. Third,the server monitors network boot requests and performs file transfers appropriate to the situation,minimizing time spent on transfers. The proposed system allows the server to efficiently control numerousIoT devices when using network boot on the IoT platform.

The Internet of Things (IoT) paradigm enables the development of new kinds of mass market applications that benefit from environment data collected by sensors. Because of the huge number of objects encompassed by the IoT, their impermanence and their world wide accessibility, the middleware community faces new challenges. Offering application developers the possibility to declare IoT variables easily discovered and transparently updated is one of these challenges. IoT systems are supported by distributed architectures where sensors and end-user applications interact through IoT platforms deployed on the cloud or on the edges. In this paper, we describe the IoTVar middleware that manages interactions between end-user applications and IoT platforms through IoTVar proxies. We show how IoTVar enables software developers to declare and manage IoT variables with a minimum number of lines of code.

With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

Distributed Denial-of-Service (DDoS) attacks are widespread for Internet of Things (IoT) systems that aim to disrupt the availability of a system completely (high-rate DDoS) or partially (low-rate DDoS). Design and placement of intrusion detection systems (IDSs) for DDoS attacks on IoT systems are challenging due to the low power and lossy nature of networks. Existing IDSs are designed to handle either high-rate or low-rate DDoS but cannot handle both with good accuracy. Existing IDS placement techniques are mostly nontransparent, making malicious nodes aware of the presence of IDS nodes. Most of the IDS placement strategies are nonoptimal, making them energy inefficient. Accordingly, this work proposes a transparent, optimally placed, distributed IDS solution, namely, OPTIMIST, which can handle both high-rate and low-rate DDoS attacks with good accuracy. The placement problem is formulated as the weighted minimum vertex cover problem of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -uniform hypergraph and solved with an approximation algorithm. The IDS module is based on a long short-term memory (LSTM) model where a novel offline training method for LSTM is proposed using Wasserstein GAN-generated artificial flows. Extensive experimentation on simulation and testbed shows that the OPTIMIST can best achieve the balance between DDoS detection and energy overhead.

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

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

The Internet of Things (IoT) is a hot topic in the engineering and technology community, and IoT-enabled devices are becoming increasingly common due to advancements in technology. IoT platforms provide valuable and specific services for IoT applications, and the number of platforms used by IoT applications is growing rapidly commensurate with the increasing number of IoT devices. However, fierce competition between IoT platforms is making it difficult for companies to select a suitable type of IoT platform for their business needs. This paper attempts to identify the important factors of IoT platforms that can be consider before selecting an IoT platform. In this paper, Twenty-one important IoT platform factors are identified from the literature and these factors were then verified by the Delphi method and categorized. This paper aims to help companies choose an appropriate IoT platform from the huge number and variety of IoT platforms available in the market.

The Internet of Things (IoT) is defined as a global network that links together living and/or non-living entities, such as people, animals, software, physical objects or devices. These entities can interact with each other, gather, provide or transmit information to the IoT. Although the Internet of Things is a relatively new concept, various platforms are already available. Some of them are open platforms, enabling both the integration of people, systems, and objects from the physical and virtual world, and the visualization of data. For example, there are already some IoT platforms used, like Google Cloud Platform, Microsoft Azure IoT Hub, Amazon Web Services IoT Platform, IBM Watson IoT Platform, Nimbits, Open.Sen.se, ThingWorx, and ThingSpeak. But what if things could not only “work” and “speak”, but also “sing”? We propose a game in which the things connected to IoT can play in real time different sounds, according to the values of some monitored parameters. These things can be grouped in the IoT platform to create a virtual orchestra and make music. Besides this game allowing the creation of great songs, it can be widely used to explain the new ideas behind the fast emerging areas of the Internet of Things. In addition to many technical challenges, it is also worth considering the effect the IoT concept will have on people, society, and economy as a whole.

Blockchain is a key technology that enables cryptocurrencies such as Bitcoin, Litecoin, etc. In recent years, researchers have ventured into tapping the potential of blockchain‐based ecosystems beyond cryptocurrencies. This paper explores the convergence of blockchain with the Internet of things (IoT) and its improved versions like software‐defined network (SDN)–based IoT, which are upcoming technologies in the area. This paper gives a brief description of the IoT and its challenges, especially the security aspects. It also discusses how blockchain concept can further resolve these challenges on the SDN‐based IoT platform. This paper first discussed the evolution of conventional IoT to the SDN‐based IoT, which can resolve many drawbacks of a conventional IoT system. Also, it is focused on how the concept of blockchain can be converged with SDN‐based IoT system to further improve its security aspects. Although the convergence of these different technologies has resulted into many advantages, many associated challenges still remain. This paper highlighted these challenges and opportunities to discover many other research issues that need to be resolved. The details of current literature on the same issue has also been highlighted in this paper.

The Internet of Things (IoT) has received a lot of attention, especially in industrial scenarios. One of the typical applications is the intelligent mine, which actually constructs the Six-Hedge underground systems with IoT platforms. Based on a case study of the Six Systems in the underground metal mine, this paper summarizes the main challenges of industrial IoT from the aspects of heterogeneity in devices and resources, security, reliability, deployment and maintenance costs. Then, a novel resource service model for the industrial IoT applications based on Transparent Computing (TC) is presented, which supports centralized management of all resources including operating system (OS), programs and data on the server-side for the IoT devices, thus offering an effective, reliable, secure and cross-OS IoT service and reducing the costs of IoT system deployment and maintenance. The model has five layers: sensing layer, aggregation layer, network layer, service and storage layer and interface and management layer. We also present a detailed analysis on the system architecture and key technologies of the model. Finally, the efficiency of the model is shown by an experiment prototype system.

The Internet of Things (IoT) is a fast-expanding network of smart heterogeneous objects. It refers to the physical devices that are capable of communicating with other physical devices. Unlike the wireless sensor networks (WSNs), IoT is connected to worldwide Internet that exposes it to global intrusion in addition to wireless attacks inside an IoT network. It is protected by cryptographic and network security techniques, but they are vulnerable to internal and external attacks. The IoT devices are resource constrained in terms of limited storage, battery, limited transmission range and processing. To protect these tiny devices from the inside and outside cyberattacks, we need a lightweight security system that can run efficiently and effectively on these IoT devices. We also need a system, which can identify the type of intrusion and trigger an alarm in intrusion scenario to take appropriate preventive measure. Hence, an Intrusion Detection System (IDS) plays an important role to prevent such cyberattacks in IoT. These devices can be static or mobile in an IoT environment; this must be considered while designing IDS for IoT system. This book chapter presents various IDSs for an IoT system and their comparisons in terms of detection rate, false positive and accuracy in both static and mobility of devices.

Recently, the internet of things (IoT) has evolved into a breakthrough for creating intelligent settings. Any technology's reliance on the IoT model is seen as having major security and privacy issues. The various conceivable attacks carried out by intruders give rise to privacy and security considerations. Therefore, creating an intrusion detection system is crucial for identifying attacks and anomalies in the IoT system. In this work, a deep belief network (DBN) algorithm model for the intrusion detection system has been proposed. The CICIDS 2017 dataset is used for the performance analysis of the current IDS model in terms of assaults and anomaly detection. Accuracy, recall, precision, F1-score, detection rate, and other characteristics were all improved by the proposed method. IoT technology has revolutionized how healthcare is provided to patients. Medical institutions are very concerned about IoT security because of the network enabled IoT devices' integration with healthcare network infrastructure.