MAIDE: Augmented Reality (AR)-facilitated Mobile System for Onboarding of Internet of Things (IoT) Devices at Ease

Proliferation of the connected Internet of things (IoT) devices and applications like augmented reality have resulted in a paradigm shift in computation requirement and power management of these devices. Furthermore, processing enormous amounts of data generated by ubiquitous IoT devices and meeting real-time deadline requirements of novel IoT applications exacerbate the challenges in IoT design. To address these challenges, in this paper, we propose a computation offloading architecture to process the huge amount of data generated by IoT devices while simultaneously meeting the real-time deadlines of IoT applications. In our proposed architecture, a resource-constrained IoT device requests a relatively resourceful computing device (e.g., a personal computer) in the same local network for computation offloading. Additionally, in our proposed computation offloading architecture, both client and server devices tune their tunable parameters, such as operating frequency and number of active cores, to meet the application's real-time deadline requirements. We compare our proposed computation offloading architecture with contemporary computation offloading models that use cloud computing. Experimental results verify that our proposed architecture provides a performance improvement of 21.4% on average as compared to cloud-based computation offloading schemes.

Connecting food supply chains

Introduction: The widespread adoption of Internet of Things (IoT) devices has transformed multiple industries, enhancing operational efficiency and convenience. However, the rapid expansion of IoT ecosystems also brings forth significant security challenges. Traditional security frameworks often fail to adequately protect these systems due to their large scale, diversity, and limited resources. In response, cloud-based security solutions have emerged as a promising alternative, offering centralized management, advanced authentication techniques, and real-time threat monitoring. Problem Statement: IoT environments are vulnerable to various security risks, including unauthorized access, data breaches, and device manipulation. Existing security mechanisms often fall short when it comes to defending against sophisticated cyber-attacks targeting IoT devices and networks. The resource-constrained nature of many IoT devices further limits the implementation of robust local security measures. As a result, there is an urgent need for effective, cloud-based security solutions designed specifically for the unique demands of IoT systems. Objective: This research aims to explore the effectiveness of cloud-based security solutions in mitigating the security challenges faced by IoT environments and devices. The study focuses on evaluating the performance of cloud-based authentication mechanisms, intrusion detection systems, and encryption techniques in strengthening the security and privacy of IoT ecosystems. Methodology: A comprehensive approach is employed, combining a literature review, case studies, and empirical research to assess the current landscape of IoT security in smart environments. Data collection includes unstructured interviews with industry experts and stakeholders, offering insights into current practices and emerging security trends. The research framework incorporates threat modeling, risk assessments, and the development of proactive security strategies. Results: Initial findings indicate that cloud-based security solutions offer several benefits for protecting IoT environments and devices. Centralized management enhances integration and scalability, while advanced authentication methods, such as multi-factor and biometric authentication, improve access control. Real-time threat detection and response capabilities further bolster security by enabling timely interventions to prevent breaches and attacks. Conclusion: Cloud-based security solutions present a highly effective approach to addressing the unique security concerns of IoT environments and devices. By leveraging the scalability, flexibility, and computational power of cloud platforms, organizations can enhance the resilience of their IoT deployments against evolving cyber threats. However, further research is needed to optimize cloud-based security tools to better serve diverse IoT applications and use cases.

Remote exploitation attacks use software vulnerabilities to penetrate through a network of Internet of Things (IoT) devices. This work addresses defending against remote exploitation attacks on vulnerable IoT devices. As an attack mitigation strategy, we assume it is not possible to fix all the vulnerabilities and propose to diversify the open-source software used to manage IoT devices. Our approach is to deploy dynamic cloud-based virtual machine proxies for physical IoT devices. Our architecture leverages virtual machine proxies with diverse software configurations to mitigate vulnerable and static software configurations on physical devices. We develop an algorithm for selecting new configurations based on network anomaly detection signals to learn vulnerable software configurations on IoT devices, automatically shifting towards more secure configurations. Cloud-based proxy machines mediate requests between application clients and vulnerable IoT devices, facilitating a dynamic diversification system. We report on simulation experiments to evaluate the dynamic system. Two models of powerful adversaries are introduced and simulated against the diversified defense strategy. Our experiments show that a dynamically diversified IoT architecture can be invulnerable to large classes of attacks that would succeed against a static architecture.

Smart home consists of various Internet of things (IoT) devices. These IoT devices are designed to help and simplify people’s lives. The technical progress of the IoT field is aimed at simplifying human life, thereby creating new cyber threats. Different scientific papers are mentioned that number of IoT devices is growing constantly by 15% per year. As a result, around 1.6 billion IoT devices will be used globally over the internet. It means that IoT devices will be accessed over internet by consumers. Nowadays, Internet is accessible easily by everyone, so they can afford freely the ecosystem of IoT devices at home. Within this development of IoT ecosystem, consumers can face serious problems of transmission and storage of information by IoT devices. These problems might be data theft from IoT devices, using such IoT devices for Denial of Service (DoS) attacks, user tracking and so on. Local cyber threats provide an opportunity for an attacker to gain access to a home network and take advantages of it. Global cyber threats are dangerous because IoT devices can be controlled remotely from anywhere in the world without the knowledge of the user. One of the risks is that the user’s home network of IoT devices could be controlled by botnets to carry out cyber-attacks. The article describes and analyzes current threats to IoT smart home devices and provides examples of data collected and processed by smart devices. Collecting information about users through IoT devices is a novelty of this work.

Internet of Things (IoT) devices are used in many facets of modern life, from smart homes to smart cities, including Internet-enabled healthcare systems and industrial control systems. The prevalence and ubiquity of IoT devices makes them extremely attractive targets for malicious actors, in particular for taking control of vulnerable devices and demand ransom from their owners. The aim of this paper is twofold: to investigate the viability of a ransomware-type attack being carried out on IoT devices; and to explore what damage can be inflicted upon devices after they have been compromised. To test whether ransomware is a viable method for attacking IoT devices, we developed our own proof of concept malware for Linux-based IoT devices dubbed "PaperW8". We looked at feasible ways for infecting IoT devices, as well as potential methods for gaining control and applying persistent changes to the target device. We successfully created a proof of concept ransomware, which we tested against six vulnerable IoT devices of various brands and functions, some of which are known to have been targeted in the past but are still widely in use today. Developing this proof of concept tool allowed us to identify the main requirements for a successful ransomware attack against IoT devices. We also determined some limitations of IoT devices that may discourage attackers from developing IoT-specific ransomware, while highlighting workarounds that more determined attackers may use to overcome these obstacles. This paper has demonstrated that IoT ransomware is a credible threat. We implemented a proof of concept tool that can compromise many IoT devices of varying types. We envisage that this work can be used to assist current and future IoT developers to improve the security of their devices, and also to help security researchers in implementing more effective ransomware countermeasures, including for IoT devices.

With the increasing number of Internet of Things (IoT) devices connected to the internet, the industry and research community have become increasingly concerned about their security impact. Adversaries or hackers often exploit public security flaws to compromise IoT devices and launch cyber attacks. However, despite this growing concern, little effort has been made to investigate the detection of IoT devices and their underlying risks. To address this gap, this article proposes to automatically establish relationships between IoT devices and their vulnerabilities in the wild. Specifically, we construct a deep neural network (DNN) to extract semantic information from IoT packets and generate fine-grained fingerprints of IoT devices. This enables us to annotate IoT devices in cyberspace, including their device type, vendor, and product information. We collect vulnerability reports from various security sources and extract IoT device information from these reports to automatically match vulnerabilities with the fingerprints of IoT devices. We implemented a prototype system and conducted extensive experiments to validate the effectiveness of our approach. The results show that our DNN model achieved a 98% precision rate and a 95% recall rate in IoT device fingerprinting. Furthermore, we collected and analyzed over 13,063 IoT-related vulnerability reports and our method automatically built 5,458 connections between IoT device fingerprints and their vulnerabilities. These findings shed light on the ongoing threat of cyber-attacks on IoT systems as both IoT devices and disclosed vulnerabilities are targets for malicious attackers.

The security risks and vulnerabilities associated with these resource-constrained Internet of Things (IoT) devices grow as their usability rises. Distributed Denial of Service is one of the main dangers to Internet of Things devices (DDoS). Continuous monitoring, early detection, and adaptive decision-making are necessary for IoT device security to be robust and effective. With software-defined networking (SDN), these issues can be solved, giving IoT devices the chance to manage DDoS threats in an efficient manner. This study suggests a unique SDN-based secure IoT framework that uses IP Payload Analysis and session IP counters to identify IoT device vulnerabilities and malicious traffic sent by IoT devices. The proposed methods can readily identify the DDoS attack in the SD-IoT network by analyzing several metrics, even with high traffic volumes, thanks to the DDoS attack detection module of the framework. By creating a lot of traffic from a compromised node, which is later identified and alerted, these tactics are tested on an SDN controller. The results and comparison analysis show that the suggested framework effectively and accurately detects DDoS attacks in their early stages, with a detection rate ranging from 98% to 100% and a low false-positive rate. Keywords; IoT networks, DDoS, DDoS attacks, DDoS Detection and IoT security

There is a large variety of Internet of things (IoT) devices and their peripherals available in consumer markets, and IoT deployers should work on customizing device drivers that are compatible with their peripherals. Implementing compatible device drivers, however, often requires a burden of work. This paper proposes a generic platform that enables plug-and-play (PnP) integration for sensors and actuators to allow the addition and removal of IoT device peripherals without re-customizing all the device drivers. To this end, we employ IoT ontologies and semantics to represent IoT device characteristics and to infer IoT device behaviors. IoT device behavior is then passed to the generic device driver to cover device-specific operation. Since the generic device driver selectively operates most of the available function calls required in IoT devices, most of the programming work that is normally required for device customization is removed, and management overhead for software installation and maintenance can be minimized. To this end, we employ IoT ontologies and semantics as well as generic programming techniques in the generic platform in order to configure and control IoT devices. In the proposed platform, IoT device characteristics, including I/O functions and configuration rules, are defined using custom-built IoT ontologies, and operational behaviors are inferred through SPARQL queries. The generic platform then passes function-call name and configuration rules corresponding to the newly added peripheral device’s specification. The experimental results show that our generic platform covers most of the popular sensors available in the market. Our solution therefore enables a true PnP experience of sensors and actuator peripherals in IoT devices.

Investigation on identify the multiple issues in IoT devices using Convolutional Neural Network

Internet of Things (IoT) devices have drawn significant attention over the last few years due to their significant contribution to every domain of life, but the major application of these devices has been witnessed in the healthcare sector. IoT devices have changed the complexion of healthcare set-up, however, the major limitation of such devices is susceptibility to many cyberattacks due to the use of embedded operating systems, the nature of communication, insufficient software updates, and the nature of backend resources. Similarly, they transfer a huge amount of sensitive data via sensors and actuators. Therefore, the security of Internet of Health Things (IoHT) devices remains a prime concern as these devices are prone to various cyberattacks, which can lead to compromising and violating the security of IoT devices. Therefore, IoT devices need to be authenticated before they join the network or communicate within a network, and the applied method of authentication must be robust and reliable. This authentication method has to be evaluated before being implemented for the authentication of IoT devices/equipment in a healthcare environment. In this study, an evaluation framework is introduced to provide a reliable and secure authentication mechanism based on authentication features. The proposed framework evaluates and selects the most appropriate authentication scheme/method based on evaluating authentication features using a hybrid multicriteria decision-making approach. It completes this in two steps: in the first step, the analytic hierarchy process (AHP) method is applied for assigning criteria weights; and in the second step, the technique for order preference by similarity to ideal solution (TOPSIS) approach selects the best authentication solution for IoHT devices based upon identified authentication features. This is the first attempt to present a features-based authentication model for selecting the improved authentication solution employed in IoHT devices.

Leveraging Electromagnetic Side-Channel Analysis for the Investigation of IoT Devices

A framework for DNS naming services for Internet-of-Things devices

The continuous rapid growth of Internet of Things (IoT) devices has presented a new model in fourth generation and beyond cellular networks. This continuous growth and the increasing demand in the provision of high transmission rate, delay sensitive and spectrum efficient cellular networks have made the development of fifth generation (5G) networks a reality. The design of the end-to-end 5G networks anticipated the need for terrestrial radio access networks to be complemented by its satellite counterpart in order to ensure that 5G services are provided seamlessly. These 5G services include IoT communications. This has necessitated the need for 5G satellite radio access networks to provide access to IoT devices that are located in remote and rural areas. This type of IoT devices are termed Internet of Remote Things (IoRT) devices. While this remain an appealing solution, many radio resource management issues including packet scheduling for IoRT communications in 5G satellite networks remains undefined. Hence, this paper aims to propose a new dynamic packet scheduling algorithm that will be appropriate for mixed traffic type IoRT communications in 5G satellite networks. The performance evaluation of the proposed packet scheduler is conducted through simulations, using delay, spectral efficiency, throughput and fairness index as the performance indices.

The number of attacks exploiting Internet of Things (IoT) devices has been increasing with the emergence of IoT malware targeting IoT devices. The use of IoT devices in a wide variety of situations has resulted in an urgent need to improve the security of the IoT devices themselves. However, the IoT devices themselves have low hardware performance and their operating systems and applications are not frequently updated, leaving many devices vulnerable to IoT malware attacks. Mandatory Access Control (MAC) systems based on Linux Security Modules (LSM), such as SELinux and AppArmor, can mitigate the impact of these attacks, even if software vulnerabilities are discovered and exploited. However, most IoT devices do not currently employ these systems. While existing approaches have examined on-board resources as one factor affecting the applicability of MAC systems, they are insufficient to address all relevant factors. In this paper, we report the factors that may prevent the deployment of LSM-based secure OS in IoT devices and the results of our evaluation of the effectiveness of LSM-based secure OS against IoT malware attacks. First, we comprehensively investigated the impact of each factor of IoT devices on the deployment of LSM-based secure OS. To improve the comprehensiveness of the factors affecting the deployment, we investigated the kernel version, CPU architecture, and BusyBox support. Next, we conducted an attack experiment that simulated the attack method of Mirai, a typical IoT malware, to investigate whether it is possible to protect against IoT malware. We also showed how to modify the security policy, and the cost of modifying it, for secure OSs that cannot prevent attacks from IoT malware with the default security policy. Finally, we report the results of our investigation into the impact of these factors in combination.