Securing IoT devices using Ensemble Machine Learning in Smart Home Management System

This paper explores various security and privacy issues inherent in IoT devices, ranging from vulnerabilities in device firmware and software to data breaches and unauthorized access. We delve into the challenges of securing IoT devices due to their resource constraints, diverse communication protocols, and often lax security practices during development. Additionally, we discuss privacy implications stemming from the collection and sharing of sensitive personal data by IoT devices, as well as the potential for surveillance and data misuse. Furthermore, we examine the implications of IoT devices in critical infrastructure and industrial systems, where security breaches can have severe consequences. Finally, we propose potential solutions and best practices to address these challenges, including robust encryption methods, regular security updates, and improved authentication mechanisms, to ensure the security and privacy of IoT devices in an increasingly connected world. The exponential growth of IoT devices across various sectors such as smart homes, healthcare, transportation, and industrial automation underscores the importance of ensuring their security and privacy. As these devices become more integrated into daily life and critical infrastructure, any vulnerability can have widespread and severe consequences. The evaluation of alternative performances through Complex Proportionality Assessment (COPRAS) requires an understanding of key criteria, exploration of options, and comparison of relevant facts. Meeting the decision-makers' desire for comparing grades involves choosing among multiple options based on predetermined competing requirements. COPRAS offer a method for such assessments in real-world scenarios, where criteria are nuanced and values cannot be quantified numerically. From the result smart thermostat got the first rank whereas wearable fitness tracker is having the lowest rank.

Background: The increasing number of Internet of Things (IoT) devices has raised worries about their susceptibility to different cyber threats. Although there have been improvements in software-level security, hardware measures continue to be essential for strong protection. Objective: The purpose of this study is to thoroughly analyze different architectural methods for ensuring hardware security in IoT devices, assessing how effective and practical they are. Methods: The study conducted a comparative investigation of different hardware security mechanisms, such as Trusted Platform Modules (TPMs), Physical Unclonable Functions (PUFs), and hardware security modules (HSMs). Information was gathered from several Internet of Things (IoT) implementations in industrial, healthcare, and consumer industries. Results: The results suggest that PUFs have notable benefits in terms of distinctiveness and protection against physical tampering, while TPMs offer extensive security capabilities but at a greater expense and level of complexity. High-security modules (HSMs) were determined to be the most effective choice for environments with high stakes that demand strict security measures. Conclusion: The selection of a suitable security method for IoT applications depends on the particular requirements and hazards associated with each application, as there is no one-size-fits-all hardware security mechanism. This paper establishes a fundamental structure for choosing and executing hardware security measures in IoT designs, improving the durability of devices against advancing cyber threats.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

This dissertation proposes a solution to the growing concern about the security of Internet of Things (IoT) devices in home or business environments. Entitled MAIOS (Multi Accountability IoT Oriented Switch), the system offers an SDN (Software Defined Networking) switch architecture, which provides effective segmentation for IoT devices, increasing their protection against cyber threats. The research begins by contextualizing the exponential increase in IoT devices and the security challenges associated with them. MAIOS addresses this issue through a centralized control layer, which allows efficient management of IoT devices and the implementation of dynamic security policies, which can be applied from an SDN control plane application layer. The system was designed considering functional, non-functional requirements and deployment scenarios, either in home or business environments. To increase usability, an intuitive interface was developed for users, facilitating the configuration and monitoring of IoT devices. The solution is based on open source technologies such as pfSense proxy/firewall and Home Assistant to provide functionalities such as firewall, IoT control and real-time traffic monitoring. The effectiveness of MAIOS is evaluated through security and performance tests in simulated environments, which demonstrated its ability to identify and mitigate cyber threats, providing robust security for IoT devices. This dissertation offers a contribution to the field of IoT security, presenting a practical, affordable and effective solution to protect devices in residential and business networks. MAIOS represents an important advance in ensuring digital security in an increasingly connected world.

The development and use of Internet of Things (IoT) devices have grown significantly in recent years. Advanced IoT device characteristics are mainly to blame for the wide range of applications that may now be achieved with IoT devices. Corporations have begun to embrace the IoT concept. Identifying true and suitable devices, security faults that might be used for bad reasons, and administration of such devices are only a few of the issues that IoT, a new concept in technological progress, provides. In some ways, IoT device traffic differs from regular device traffic. Devices with particular features can be classified into categories, irrespective of their function or performance. Ever-changing and complex environments, like a smart home, demand this classification scheme. A total of 41 IoT devices were employed in this investigation. To build a multiclass classification model, IoT devices contributed 13 network traffic parameters. To further preprocess the raw data received, preprocessing techniques like Normalization and Dataset Scaling were utilized. Feature engineering techniques can extract features from the text data. A total of 117,423 feature vectors are contained in the dataset after stratification, which are used to further improve the classification model. In this study, a variety of performance indicators were employed to show the performance of the logiboosted algorithms. Logi-XGB scored 80.2% accuracy following application of the logit-boosted algorithms to the dataset for classification, whereas Logi-GBC achieved 77.8% accuracy. Meanwhile, Logi-ABC attained 80.7% accuracy. Logi-CBC, on the other hand, received the highest Accuracy score of 85.6%. The accuracy of Logi-LGBM and Logi-HGBC was the same at 81.37% each. Our suggested Logi-CBC showed the highest accuracy on the dataset when compared to existing Logit-Boosted Algorithms used in earlier studies.

Internet of things is one of the latest technology in today’s era, IoT devices are increasing day by day and become a part of our life. Sometimes we even didn’t know that we are using and surrounded by such technolgy. Smart devices like smart phone, tablets and any wearables are the example of IoT technology. These all are IoT devices and there are many more. As we all are in 2020 and facing the pandemic we all are become more connected with IoT devices to be safe. In this way the number of IoT devices per person will increase so much rapidly and several new innovations either of hardware or software take place. But the Internet of Things (IoT) devices are becoming more popular, the security measures are not upto the mark, and several issues will come in knowledge at different time on different devices. So we have to think about the security and privacy of user and should make it correct. We should learn from the past issues and try to fix them accordingly. To overcome the issues of IoT devices we can have to ways, one is to use ECC which will enchance the security in IoT device and another possible solution can be Blockchain. In this paper we will discuss about ECC and blockchain technology both and try to find a comparitive study.

Internet of Things (IoT) devices are largely embedded devices which lack a sophisticated user interface, e.g., touch screen, keyboard, etc. As a consequence, traditional Pre-Shared Key (PSK) based authentication for mobile devices becomes difficult to apply. For example, according to our study on home automation devices which leverage smartphone for PSK input, the current process does not protect against active impersonating attack and also leaks the Wi-Fi password to eavesdroppers, i.e., currently these IoT devices can be exploited to enter into critical infrastructures, e.g., home networks. Motivated by this real-world security vulnerability, in this paper we propose a novel proximity-based mechanism for IoT device authentication, called Move2Auth, for the purpose of enhancing IoT device security. In Move2Auth, we require user to hold smartphone and perform one of two hand-gestures (moving towards and away, and rotating) in front of IoT device. By combining (1) large RSS-variation and (2) matching between RSS-trace and smartphone sensor-trace, Move2Auth can reliably detect proximity and authenticate IoT device accordingly. Based on our implementation on Samsung Galaxy smartphone and commodity Wi-Fi adapter, we prove Move2Auth can protect against powerful active attack, i.e., the false-positive rate is consistently lower than 0.5%.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

In the modern security-conscious world, Deep Packet Inspection (DPI) proxies are increasingly often used on industrial and enterprise networks to perform TLS unwrapping on all outbound connections. However, enabling TLS unwrapping requires local devices to have the DPI proxy Certificate Authority certificates installed. While for conventional computing devices this is addressed via enterprise management, it's a difficult problem for Internet of Things (IoT) devices which are generally not under enterprise management, and may not even be capable of it due to their resource-constrained nature. Thus, for typical IoT devices, being installed on a network with DPI requires either manual device configuration or custom DPI proxy configuration, both of which solutions have significant shortcomings. This poses a serious challenge to the deployment of IoT devices on DPI-enabled intranets. The authors propose a solution to this problem: a method of installing on IoT devices the CA certificates for DPI proxy CAs, as well as other security configuration (security bootstrapping). The proposed solution respects the DPI policies, while allowing the commissioning of IoT and IIoT devices without the need for additional manual configuration either at device scope or at network scope. This is accomplished by performing the bootstrap operation over unsecured connection, and downloading certificates using TLS validation at application level. The resulting solution is light-weight and secure, yet does not require validation of the DPI proxy's CA certificates in order to perform the security bootstrapping, thus avoiding the chicken-and-egg problem inherent in using TLS on DPI-enabled intranets.

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay.

Through the Internet of Things (IoT), everything is connected remotely through a network, allowing smart environments to be created. The IoT will play a central role in every aspect of our daily lives, from toys to healthcare. The security of IoT devices is the most crucial concern. In this chapter, we will discuss hardware vulnerabilities in IoT devices that pose major threats to data transfer. Researchers focus on finding the major vulnerabilities and limitations of proposed countermeasures for IoT devices’ hardware security issues. Modern electronic hardware used in IoT systems consists mostly of interconnected integrated circuits, so hardware security directly affects integrated circuit security. Malicious modifications to integrated circuits are carried out in untrusted fabrication units by Hardware Trojans. Since these Hardware Trojans are stealthy, most of the detection schemes require a gold chip for measuring the side channels. In the existing Trojan detection schemes, false-positive and false-negative detections are possible because of process variation. As a result, this chapter provides an overview of the hardware security challenges with Hardware Trojans associated with IoT devices used in smart environments.

The Internet of Things (IoT) is a new paradigm that enables the convergence of smart objects and the internet. This convergence has led to the creation of an intelligent network that connects all things to the internet for the purpose of exchanging information. The direct connection of IoT devices to the internet makes them susceptible to several security threats. Researchers have developed techniques aimed at enhancing security of IoT devices at both network and application layers. In this paper, we present a real-time and lightweight algorithm, based on information theoretic approach, that enables rogue access point detection for embedded IoT devices. This is to ensure that WiFi-enabled IoT devices can intelligently distinguish between legitimate and rogue access points. We evaluated the performance of the algorithm with respect to the detection rate and also CPU utilization efficiency.

Background: Metabolic syndrome is a cluster of metabolic disorders including elevated blood pressure, high plasma glucose, excess body fat around the waist, and abnormal cholesterol or triglyceride levels. These conditions cause serious complications such as heart disease, stroke and type 2 diabetes. In Japan, specific health checkups and specific health guidance which focused on metabolic syndrome has been performed since 2008. Those who fall under certain criteria need to receive a medical treatment guidance from doctor, public health nurse or dietitian. Those who received health guidance receives a reassessment of improvement of their life-style 3-6 months later. However, the efficacy of this approach has not been elucidated. In addition, many persons who have metabolic syndrome do not receive this instruction. Recently, the image analysis technology using the artificial intelligence (AI) progresses rapidly. The smart device application “Asken” has an AI-powered photo analysis system which analyzes the photo of the entire meal, and delivers individualized messages and dietary feedbacks. In this study, we utilized the Internet of Things (IoT) device which includes Asken app, body composition analyzer and sphygmomanometer that can connect wirelessly. Objective: Our aim is to assess the efficacy of specific health guidance adding on IoT device. This is a multicenter, unblinded, non-randomized controlled study. Results: At the end of January 2020, we recruited 219 participants including 105 participants with IoT devices. We used 48 participants (32 with IoT and 16 without IoT) who had finished a reassessment 3 to 6 months after initial guidance. Results: Age, body weight (BW), body mass index (BMI), blood pressure (BP), fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), total cholesterol (T-Chol), high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol (LDL), non-HDL cholesterol (n-HDL), and triglyceride (TG), did not differ between IoT-use and control group. 6 months after initial guidance, the quantity of decrease of BW in IoT-use group was significantly larger than control (-2.5 ± 4.1 kg vs. 0.6±4.4, p = 0.03). In addition, the quantities of decrease of both T-Chol and n-HDL in IoT-use group were also significantly larger than control (T-Chol, -5.9 ± 32.0 vs. 14.3 ± 31.6, p = 0.02; n-HDL, -7.6 ± 29.0 vs. 9.4 ± 27.5, p = 0.01). Conclusion: Using IoT device might be useful for body weight loss and the improvement of mild hypercholesterolemia in those with metabolic syndrome.