Intrusion Detection Systems for Smart Home IoT Devices: Experimental Comparison Study

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

The fourth industrial revolution put new processes on the rails of automation in industry, healthcare, home and other areas of human life through the mass integration of the concept of the Internet of Things into these areas. At the same time, these processes are not bypassed by home automation systems or smart homes. A smart home is defined as a system of interconnected sensors, actuators, and other devices that are networked together with computer systems and controlled by appropriate software. This connection allows to collect, share and analyze data, which helps to increase comfort, automation and control over the parameters of the house. However, along with the obvious advantages and conveniences of rooting home automation systems, this concept leaves a number of potential security bottlenecks for attackers. Data collected by smart devices is always point of interest to hackers and hijackers of confidential information.Third-party access to data collected by smart devices can lead to a variety of emergencies, the degree of danger of which will depend solely on the wish of the owner of the intercepted data. The paper proposes an intelligent system for detecting anomalies and identifying smart home devices based on the collective communication of smart homes. The concept of the system is based on the benefits of combining smart homes into a social network in terms of improving the security of both a single smart home and the entire social network of combined smart homes. Detection of anomalies and identification of devices in each of the smart homes is based on monitoring network traffic and forming profiles of smart devices that are present in the network. Profiles consist of a set of features that describe the behavior of smart devices on the network, including the period of activity of the device and the period of its sleep. Based on this, a whitelist of allowed profiles of devices operation in the cluster is formed. To verify the presence of a profile in the whitelist the Random Forest algorithm was used. A key feature of the system is the communication of smart home clusters with each other to exchange information about the available smart device profiles in the whitelists of each cluster.

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.

How are people using current smart home technologies, and how do they conceptualize future ones that are more interconnected and more capable than those available today? We deployed an online survey study to 150 participants to investigate use of and opinions about smart speakers, home robots, virtual assistants, and other smart home devices. We also gauged how impressions of connected smart home devices are shaped by the way the devices interact with one another. Through a mixed-methods qualitative and quantitative approach, we found that people mostly use single devices for single functions, and have simple and brief interactions with virtual assistants. However, they imagine their future devices to have more control over the physical environment (i.e., interact with each other) and envision them interacting with people in more socially complex ways. These findings motivate design considerations and research directions for connected smart home technologies.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.

Fog Computing is an approach involving smart devices. These devices carry out data processing to provide collaborative services to reach a common goal, usually, in the cloud. In the fog computing paradigm, uncertainty and vagueness are inherent to the data processing due to the limitations of computational and communication capabilities of the smart devices. Fuzzy logic and protoforms represent a powerful tool to model and compute imprecise data presented within the fog-computing paradigm. In this paper, we present a fuzzy cloud-fog approach based on fuzzy temporal windows and fuzzy aggregation. The innovations of this paper are: i) to model the uncertainty involved in fog nodes linguistically, ii) to compute and distribute relevant linguistic information (protoforms), and iii) to publish the computed protoforms in the cloud to generate complex protoforms, which reach the common goal. This new fuzzy cloud-fog approach is applied to the problem of activity recognition in smart homes. In this context, the smart devices in a smart home are represented by fog nodes, which cooperate for activity recognition using a fuzzy cloud-fog computing approach to provide solutions in ambient-assisted living. Finally, to demonstrate the effectiveness of the proposal in handling situations/environments where multiple and heterogeneous devices are involved (such as UWB beacons, smart objects and smart wearable devices), a case study of the proposed fuzzy cloud-fog approach is implemented in the smart lab of the University of Jaén. So, the results obtained with the proposed approach in the case study are compared to the results obtained with a non-fuzzy approach with the aim of showing the advantages of the fuzzy methodology.

The Internet of Things (IoT) has enabled smart home devices to provide users with the convenience of monitoring and controlling their homes directly from their smartphones. However, connecting these devices to the internet and to each other introduces significant security and privacy challenges, including the need to ensure the confidentiality, integrity, and authenticity of the data they collect and exchange. Due to inherent vulnerabilities, smart home devices are susceptible to various forms of cyberattacks, which can compromise the safety and trustworthiness of the smart home environment. Consequently, assessing and understanding these security risks is essential to determine the resilience of such devices. For smart home technologies to achieve widespread adoption, they must gain user confidence through robust security and privacy safeguards. As with many technology-driven sectors, maintaining security remain a critical and ongoing challenge. As homes become increasingly digitized and the adoption of smart devices continues to grow, the likelihood of targeted attacks has also increased. Therefore, this research proposes a methodology comprising four phases: information gathering to identify the device’s FCC ID, establishing the laboratory environment, conducting the exploitation phase, and analyzing the results of the vulnerability assessment. The main objective is to identify potential weaknesses in selected smart home devices. Experimental results revealed that the tested device is vulnerable to replay attacks, in which retransmission of captured packets prompts the device to execute unintended commands.

The Internet of Things (IoT) is a constantly growing environment for connecting hardware, software, physical things, and communicating devices altogether or individually over a network with or without human interventions. It collects, computes, and exchanges data together. This quality of IoT makes the platform flexible for the variety of applications that work on this technology to get incredible benefits, including healthcare domains. In the olden days, the medical system does not have any digital file system, clinical data monitoring, and cyber-physical systems. Cyber Physical System (CPS) is the nucleus of IoT. The smart parameters of IoT create an interesting thing on the Internet of Healthcare System (IoHT) and keep the best place in revenue. Apart from smart homes, cities, and smart devices, IoT established a vital position in IoT. The security and the privacy of Healthcare data should be managed properly. Thus, making a new concept into it called, “Blockchain.” This not only marks its step in cryptocurrencies, but it also tights the security and privacy of all types of data, and it supports heterogeneous applications, especially IoT-based applications. This article describes the Blockchain-IoT-based healthcare applications, security of the Internet of Medical Things (IoMT), and the future direction in the healthcare system based on Blockchain.

Internet-of-Things technology is being increasingly important in our daily lives. As IoT technology evolved, IoT devices face a data protection hazard, particularly smart home IoT gateway devices, which became evident. The demand for a low-cost, secure smart home gateway device or router among smart home users. The problem is that as the internet of things (IoT) becomes more ubiquitous, there is a growing need to simplify wireless network control mechanisms. Because data collecting and the process includes processes such as monitoring, judging, and controlling are all involved in IoT, the control mechanism is challenging to simplify. Many internets of things technology offer memory and communication capabilities, and are easily vulnerable to hacking, due to the mobile software available at the tip of one's fingers to operate the linked gadgets to the web. In the Internet of Things, secure data transfer is always a concern. To increase safety in IoT and wireless networks, the current study introduces a unique RSA-based method, as well as the AES algorithm and the lightweight protocol message queue telemetry transport (MQTT).

Abstract—The rapid expansion of interconnected smart devices has enabled widespread automation and connectivity across smart homes, healthcare, manufacturing, and industrial control systems. However, this large-scale connectivity has significantly increased vulnerabilities, leading to a sharp rise in IoT botnet attacks. Compromised devices are remotely controlled and used to perform cyber-crimes such as Distributed Denial of Ser- vice (DDoS), unauthorized data access, and aggressive network flooding. Traditional signature-based detection methods fail to identify evolving botnet families due to rapid mutation of attack patterns. This paper introduces a machine learning-based intelligent detection framework capable of identifying IoT botnet behavior through traffic flow analysis. The proposed system preprocesses collected network traffic, extracts relevant features, classifies flows as benign (0) or malicious (1), and triggers an automated alert notification when malicious activity is detected. Experimental evaluation demonstrates highly reliable perfor- mance and strong prediction accuracy, proving the suitability of machine learning methods for real-time IoT botnet detection and prevention. Additional discussions include feature selection techniques, model optimization, and deployment considerations for scalable and robust IoT security solutions. Index Terms—IoT Security, Botnet Detection, Machine Learn- ing, Network Traffic Analysis, Intrusion Detection System, Real- Time Alerting, Flow-Based Detection.

Recently, application scenario of crowdsourcing IoT has covered to e-healthcare service, smart home, smart city, internet of vehicles due to the proliferation of smart devices such as smart mobile devices, smart wearable device, smart medical devices and smart furniture, etc. Patient's data collected by the smart devices send to the various remote medical servers. A group of medical professionals remote access patient data stored at the medical server database. Smart home users want to remote real-time access information of smart devices at home. All these operations need via wireless remote communication, which is suffering from various kinds of threat and attacks. Hence, there are a large number of multi-factor remote authentication and key agreement schemes designed for the application of crowdsourcing IoT. However, in most existing related multi-factor schemes, all factors for identity authentication only act as a parameter for encrypting the local secret key. In this paper, we propose a new secure remote multi-factor authentication scheme that includes three factors: 1) user identity; 2) password; and 3) user biometrics, which are authenticated by the remote server, act as a part of the secret key and participate in the key agreement process. We choose the chaotic map since it has a smaller key size and lower computational overhead, and then achieve remote multi-factor authentication and key agreement by artfully employ it to zero-knowledge technology and the fuzzy extractor technology. Our scheme is more secure and robust since the user revealing nothing sensitive information, and the adversary cannot impersonate any user even if he gets the server's master key. We have done security proof for our proposed scheme using the Random-Or-Real(ROR) model, Burrows-Abadi-Needham (BAN) logic, and ProVerif 2.00 to show that the presented scheme is secure. Also, we give an additional security analysis for other various attacks. Finally, according to the test and simulation result, the proposed scheme is very suitable for the power-constrained smart devices, and in the next generation 5G communication environment, its applicability and usability will be greatly enhanced.

The rise of our smart life, along with the popularity of smart devices, has made the Internet of Things (IoT) a prevalent research topic in recent years. Even though it increases the intelligence level of society to a very great extent, it still has many flaws, especially when it comes to the security domain. The birth of blockchain has brought a new evolution for IoT. The decentralized and traceable feature of blockchain is entirely in sync with the need for the decentralized IoT devices in the future. Currently, there are many applications of blockchain that include but not limited to, smart homes, smart cities, banking, insurance, and financial services. However, there are still many challenges regarding the use of blockchain in IoT, as many things require to be proved and realized. Therefore, the paper is going to research the challenges of blockchain in IoT to find out about its causes and the cures. The changes in the identity of IoT during its lifetime is also covered and how to supervise the governance of data and privacy in a smart device. Data protection law is also taken into consideration when it comes to legal and compliance issues. To solve these difficulties, several perspectives are worth considering, like how to optimize the data authentication, ways to secure communication, methods to improve the smart contract, etc. The paper also aims to help the people from the industry to have better clarity and understanding of the scope of using blockchain in IoT.

Internet of Things (IoT) devices and applications are growing for smart home use cases. Although various security risks have been identified in smart home devices, studies reveal that the majority of the smart home devices released in the market have multiple security vulnerabilities and, in many cases, no security mechanisms are implemented. These vulnerabilities of smart home networks are mostly attributed to user unawareness of security. Many users are not aware and do not have the technical skills or knowledge of what is happening on their devices in their smart home environment. When an intruder connects to their device or takes advantage of it, users are unaware of such a connection. This paper introduces a configuration tool that helps users to create device interaction rules that are added to an existing Network Intrusion Detection System (NIDS) in the smart home network. These rules generate alarms that alert the user if unwanted interaction takes place between their devices or from an external network, and in this way increases user awareness of the security of their smart home network.

ABSTRACTContextSmart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message.ObjectiveAware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy.MethodWe propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure.ResultsWe evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second.ConclusionsOur proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.

The Internet of Things (IoT) presents many possibilities, including security and privacy issues. The Digital Forensics has long been studied in academia and industry, but forensics for smart home device has never been attempted. Smart home forensics deals with tools and techniques for recovering data and evidence from mobile devices. This paper describes a data acquisition, classification and analysis process of smart home devices using the IoT. It also includes analysis based on attack scenarios of collected data and smart home device forensic models suitable for such scenarios.