Internet of Things Security and Privacy

The Internet of Things is becoming more and more popular with time. The extremely low cost of sensors is putting the growth of the Internet of Things on steroids. Many industries such as healthcare, construction, agriculture, and transportation are increasingly leveraging this technology. However, security and privacy are two big concerns when it comes to the future of the Internet of Things. Since most of these “things” that are connected to the Internet are simple devices with limited hardware capabilities, it is nearly impossible to harden them via traditional resource-heavy defenses. In this chapter, we discuss the importance of securing the Internet of Things networks, layout the challenges of the Internet of Things security, and briefly discuss potential solutions in the literature

Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.Comment: 6 pages, 9 figure

In Focus: An Opinion on the Report on Securing and Growing the Digital Economy

On 1 December 2016, the US Commission on Enhancing National Cybersecurity (the Commission)—charged with developing recommendations to ensure the digital economy’s growth and security—released the “Report on Securing and Growing the Digital Economy” (the Report).(1) The nonpartisan Commission was formed to develop the Report in response to challenges posed by cyberthreats. The Report focuses on areas such as the protection of critical infrastructure, the Internet of Things (IoT), cybersecurity R&D, public awareness and education to strengthen cybersecurity, governance issues, development of a cyber-ready workforce, identity management and authentication, cyberinsurance, international and global issues, and the role of small and medium-sized businesses (SMBs)

Internet of Things security with machine learning techniques:a systematic literature review

Abstract. The Internet of Things (IoT) technologies are beneficial for both private and businesses. The growth of the technology and its rapid introduction to target fast-growing markets faces security challenges. Machine learning techniques have been recently used in research studies as a solution in securing IoT devices. These machine learning techniques have been implemented successfully in other fields. The objective of this thesis is to identify and analyze existing scientific literature published recently regarding the use of machine learning techniques in securing IoT devices. In this thesis, a systematic literature review was conducted to explore the previous research on the use of machine learning in IoT security. The review was conducted by following a procedure developed in the review protocol. The data for the study was collected from three databases i.e. IEEE Xplore, Scopus and Web of Science. From a total of 855 identified papers, 20 relevant primary studies were selected to answer the research question. The study identified 7 machine learning techniques used in IoT security, additionally, several attack models were identified and classified into 5 categories. The results show that the use of machine learning techniques in IoT security is a promising solution to the challenges facing security. Supervised machine learning techniques have better performance in comparison to unsupervised and reinforced learning. The findings also identified that data types and the learning method affects the performance of machine learning techniques. Furthermore, the results show that machine learning approach is mostly used in securing the network

A State-of-the-Art Survey for IoT Security and Energy Management based on Hashing Algorithms

The Internet of Things (IoT) has developed as a disruptive technology with wide-ranging applications across several sectors, enabling the connecting of devices and the acquisition of substantial volumes of data. Nevertheless, the rapid expansion of networked gadgets has generated substantial apprehensions pertaining to security and energy administration. This survey paper offers a detailed examination of the present state of research and advancements in the field of Internet of Things (IoT) security and energy management. The work places special emphasis on the use of hashing algorithms in this context. The security of the Internet of Things (IoT) is a crucial element in safeguarding the confidentiality, integrity, and availability of data inside IoT environments. Hashing algorithms have gained prominence as a fundamental tool for enhancing IoT security. This survey reviews the state of the art in cryptographic hashing techniques and their application in securing IoT devices, data, and communication. Furthermore, the efficient management of energy resources is essential to prolong the operational lifespan of IoT devices and reduce their environmental impact. Hashing algorithms are also instrumental in optimizing energy consumption through data compression, encryption, and authentication. This survey explores the latest advancements in energy-efficient IoT systems and how hashing algorithms contribute to energy management strategies. Through a comprehensive analysis of recent research findings and technological advancements, this survey identifies key challenges and open research questions in the fields of IoT security and energy management based on hashing algorithms. It provides valuable insights for researchers, practitioners, and policymakers to further advance the state of the art in these critical IoT domains

The Feasibility of Wearables in an Enterprise Environment and Their Impact on IT Security

This paper is intended to explore the usability and feasibility of wearables in an enterprise environment and their impact on IT Security. In this day and age, with the advent of the Internet of Things, we must explore all the new technology emerging from the minds of the new inventors. This means exploring the use of wearables in regards to their benefits, limitations, and the new challenges they pose to securing computer networks in the Federal environment. We will explore the design of the wearables, the interfaces needed to connect them, and what it will take to connect personal devices in the Federal enterprise network environment. We will provide an overview of the wearable design, concerns of ensuring the confidentiality, integrity, and availability of information and the challenges faced by those doing so. We will also review the implications and limitations of the policies governing wearable technology and the physical efforts to enforce them

An Enhanced Architecture to Resolve Public-Key Cryptographic Issues in the Internet of Things (IoT), Employing Quantum Computing Supremacy

The Internet of Things (IoT) strongly influences the world economy; this emphasizes the importance of securing all four aspects of the IoT model: sensors, networks, cloud, and applications. Considering the significant value of public-key cryptography threats on IoT system confidentiality, it is vital to secure it. One of the potential candidates to assist in securing public key cryptography in IoT is quantum computing. Although the notion of IoT and quantum computing convergence is not new, it has been referenced in various works of literature and covered by many scholars. Quantum computing eliminates most of the challenges in IoT. This research provides a comprehensive introduction to the Internet of Things and quantum computing before moving on to public-key cryptography difficulties that may be encountered across the convergence of quantum computing and IoT. An enhanced architecture is then proposed for resolving these public-key cryptography challenges using SimuloQron to implement the BB84 protocol for quantum key distribution (QKD) and one-time pad (OTP). The proposed model prevents eavesdroppers from performing destructive operations in the communication channel and cyber side by preserving its state and protecting the public key using quantum cryptography and the BB84 protocol. A modified version is introduced for this IoT situation. A traditional cryptographic mechanism called 'one-time pad' (OTP) is employed in hybrid management

Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing

[EN] Internet of Things (IoT) is a developing technology for supporting heterogeneous physical objects into smart things and improving the individuals living using wireless communication systems. Recently, many smart healthcare systems are based on the Internet of Medical Things (IoMT) to collect and analyze the data for infectious diseases, i.e., body fever, flu, COVID-19, shortness of breath, etc. with the least operation cost. However, the most important research challenges in such applications are storing the medical data on a secured cloud and make the disease diagnosis system more energy efficient. Additionally, the rapid explosion of IoMT technology has involved many cyber-criminals and continuous attempts to compromise medical devices with information loss and generating bogus certificates. Thus, the increase in modern technologies for healthcare applications based on IoMT, securing health data, and offering trusted communication against intruders is gaining much research attention. Therefore, this study aims to propose an energy-efficient IoT e-health model using artificial intelligence with homomorphic secret sharing, which aims to increase the maintainability of disease diagnosis systems and support trustworthy communication with the integration of the medical cloud. The proposed model is analyzed and proved its significance against relevant systems.Prince Sultan University, Riyadh Saudi Arabia, (SEED-CCIS-2021{85}) under Artificial Intelligence & Data Analytics Research Lab. CCIS.Rehman, A.; Saba, T.; Haseeb, K.; Marie-Sainte, SL.; Lloret, J. (2021). Energy-Efficient IoT e-Health Using Artificial Intelligence Model with Homomorphic Secret Sharing. Energies. 14(19):1-15. https://doi.org/10.3390/en14196414S115141

Securing Our Future Homes: Smart Home Security Issues and Solutions

The Internet of Things, commonly known as IoT, is a new technology transforming businesses, individuals’ daily lives and the operation of entire countries. With more and more devices becoming equipped with IoT technology, smart homes are becoming increasingly popular. The components that make up a smart home are at risk for different types of attacks; therefore, security engineers are developing solutions to current problems and are predicting future types of attacks. This paper will analyze IoT smart home components, explain current security risks, and suggest possible solutions. According to “What is a Smart Home” (n.d.), a smart home is a home that always operates in consideration of security, energy, efficiency and convenience, whether anyone is home or not