Biblio

The proliferation of linked devices in decisive infrastructure fields including health care and the electric grid is transforming public perceptions of critical infrastructure. As the world grows more mobile and connected, as well as as the Internet of Things (IoT) expands, the growing interconnectivity of new critical sectors is being fuelled. Interruptions in any of these areas can have ramifications across numerous sectors and potentially the world. Crucial industries are critical to contemporary civilization. In today's hyper-connected world, critical infrastructure is more vulnerable than ever to cyber assaults, whether they are state-sponsored, carried out by criminal organizations, or carried out by individuals. In a world where more and more gadgets are interconnected, hackers have more and more entry points via which they may damage critical infrastructure. Significant modifications to an organization's main technological systems have created a new threat surface. The study's goal is to raise awareness about the challenges of protecting digital infrastructure in the future while it is still in development. Fog architecture is designed based on functionality once the infrastructure that creates large data has been established. There's also an in-depth look of fog-enabled IoT network security requirements. The next section examines the security issues connected with fog computing, as well as the privacy and trust issues raised by fog-enabled Internet of Things (IoT). Block chain is also examined to see how it may help address IoT security problems, as well as the complimentary interrelationships between block-chain and fog computing. Additionally, Formalizes big data security goal and scope, develops taxonomy for identifying risks to fog-based Internet of Things systems, compares current development contributions to security service standards, and proposes interesting study areas for future studies, all within this framework

The Internet of Things is an emerging technology for recent marketplace. In IoT, the heterogeneous devices are connected through the medium of the Internet for seamless communication. The devices used in IoT are resource-constrained in terms of memory, power and processing. Due to that, IoT system is unable to implement hi-end security for malicious cyber-attacks. The recent era is all about connecting IoT devices in various domains like medical, agriculture, transport, power, manufacturing, supply chain, education, etc. and thus need to be prevented from attacks and analyzed after attacks for legal action. The legal analysis of IoT data, devices and communication is called IoT forensics which is highly indispensable for various types of attacks on IoT system. This paper will review types of IoT attacks and its preventive measures in cyber security. It will also help in ascertaining IoT forensics and its challenges in detail. This paper will conclude with the high requirement of cyber security in IoT domains with implementation of standard rules for IoT forensics.

Smart building security systems typically consist of sensors and controllers that monitor power operating systems, alarms, camera monitoring, access controls, and many other important information and security systems. These systems are managed and controlled through online platforms. A successful attack on one of these platforms may result in the failure of one or more critical intelligent systems in the building. In this paper, the security requirements in the application layer of any IoT system were discussed, in particular the role of IoT platforms in dealing with the security problems that smart buildings are exposed to and the extent of their strength to reduce the attacks they are exposed to, where an experimental platform was designed to test the presence of security vulnerabilities and This was done by using the Zed Attack Proxy (ZAP) tool, according to the OWASP standards and security level assessment, and the importance of this paper comes as a contribution to providing information about the most famous IoT platforms and stimulating work to explore security concerns in IoT-based platforms.

Even as Internet of Things (IoT) network security grows, concerns about the security of IoT devices have arisen. Although a few companies produce IP-connected gadgets for such ranging from small office, their security policies and implementations are often weak. They also require firmware updates or revisions to boost security and reduce vulnerabilities in equipment. A brownfield advance is necessary to verify systems where these helpless devices are present: putting in place basic security mechanisms within the system to render the system powerless possibly. Gadgets should cohabit without threatening their security in the same device. IoT network security has evolved into a platform that can segregate a large number of IoT devices, allowing law enforcement to compel the communication of defenseless devices in order to reduce the damage done by its unlawful transaction. IoT network security appears to be doable in well-known gadget types and can be deployed with minimum transparency.

This article describes an analysis of the key technologies currently applied to improve the quality, efficiency, safety and sustainability of Smart Grid systems and identifies the tools to optimize them and possible gaps in this area, considering the different energy sources, distributed generation, microgrids and energy consumption and production capacity. The research was conducted with a qualitative methodological approach, where the literature review was carried out with studies published from 2019 to 2022, in five (5) databases following the selection of studies recommended by the PRISMA guide. Of the five hundred and four (504) publications identified, ten (10) studies provided insight into the technological trends that are impacting this scenario, namely: Internet of Things, Big Data, Edge Computing, Artificial Intelligence and Blockchain. It is concluded that to obtain the best performance within Smart Grids, it is necessary to have the maximum synergy between these technologies, since this union will enable the application of advanced smart digital technology solutions to energy generation and distribution operations, thus allowing to conquer a new level of optimization.

As an emerging technology, IoT is rapidly revolutionizing the global communication network with billions of new devices deployed and connected with each other. Many of these devices collect and transfer a large amount of sensitive or mission critical data, making security a top priority. Compared to traditional Internet, IoT networks often operate in open and harsh environment, and may experience frequent delays, traffic loss and attacks; Meanwhile, IoT devices are often severally constrained in computational power, storage space, network bandwidth, and power supply, which prevent them from deploying traditional security schemes. Authentication is an important security mechanism that can be used to identify devices or users. Due to resource constrains of IoT networks, it is highly desirable for the authentication scheme to be lightweight while also being highly effective. In this paper, we developed and evaluated a hash-chain-based multi-node mutual authentication algorithm. Nodes on a network all share a common secret key and broadcast to other nodes in range. Each node may also add to the hash chain and rebroadcast, which will be used to authenticate all nodes in the network. This algorithm has a linear running time and complexity of O(n), a significant improvement from the O(nˆ2) running time and complexity of the traditional pairwise multi-node mutual authentication.

Internet of Things devices collect and share data (IoT). Internet connections and emerging technologies like IoT offer privacy and security challenges, and this trend is anticipated to develop quickly. Internet of Things intrusions are everywhere. Businesses are investing more to detect these threats. Institutes choose accurate testing and verification procedures. In recent years, IoT utilisation has increasingly risen in healthcare. Where IoT applications gained popular among technologists. IoT devices' energy limits and scalability raise privacy and security problems. Experts struggle to make IoT devices more safe and private. This paper provides a machine-learning-based IDS for IoT network threats (ML-IDS). This study aims to implement ML-supervised IDS for IoT. We're going with a centralised, lightweight IDS. Here, we compare seven popular categorization techniques on three data sets. The decision tree algorithm shows the best intrusion detection results.

With the development of Internet of Things (IoT) technology, the transaction behavior of IoT devices has gradually increased, which also brings the problem of transaction data security and transaction processing efficiency. As one of the research hotspots in the field of data security, blockchain technology has been widely applied in the maintenance of transaction records and the construction of financial payment systems. However, the proportion of microtransactions in the Internet of Things poses challenges to the coupling of blockchain and IoT devices. This paper proposes a three-party scalable architecture based on “IoT device-edge server-blockchain”. In view of the characteristics of micropayment, the verification mechanism of the execution results of the off-chain transaction is designed, and the bridge node is designed in the off-chain architecture, which ensures the finality of the blockchain to the transaction. According to system evaluation, this scalable architecture improves the processing efficiency of micropayments on blockchain, while ensuring its decentration equal to that of blockchain. Compared with other blockchain-based IoT device payment schemes, our architecture is more excellent in activity.

The design of efficient and secure cryptographic algorithms is a fundamental problem of cryptography. Due to the tight cost and constrained resources devices such as Radio-Frequency IDentification (RFID), wireless sensors, smart cards, health-care devices, lightweight cryptography has received a great deal of attention. Recent research mainly focused on designing optimized cryptographic algorithms which trade offs between security performance, time consuming, energy consumption and cost. In this paper, we present two chaotic stream ciphers based on chaos and we report the results of a comparative performance evaluation study. Compared to other crypto-systems of the literature, we demonstrate that our designed stream ciphers are suitable for practical secure applications of the Internet of Things (IoT) in a constrained resource environment.

Model compression is one of the most preferred techniques for efficiently deploying deep neural networks (DNNs) on resource- constrained Internet of Things (IoT) platforms. However, the simply compressed model is often vulnerable to adversarial attacks, leading to a conflict between robustness and efficiency, especially for IoT devices exposed to complex real-world scenarios. We, for the first time, address this problem by developing a novel framework dubbed Magical-Decomposition to simultaneously enhance both robustness and efficiency for hardware. By leveraging a hardware-friendly model compression method called singular value decomposition, the defending algorithm can be supported by most of the existing DNN hardware accelerators. To step further, by using a recently developed DNN interpretation tool, the underlying scheme of how the adversarial accuracy can be increased in the compressed model is highlighted clearly. Ablation studies and extensive experiments under various attacks/models/datasets consistently validate the effectiveness and scalability of the proposed framework.

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.

Distributed computation and AI processing at the edge has been identified as an efficient solution to deliver real-time IoT services and applications compared to cloud-based paradigms. These solutions are expected to support the delay-sensitive IoT applications, autonomic decision making, and smart service creation at the edge in comparison to traditional IoT solutions. However, existing solutions have limitations concerning distributed and simultaneous resource management for AI computation and data processing at the edge; concurrent and real-time application execution; and platform-independent deployment. Hence, first, we propose a novel three-layer architecture that facilitates the above service requirements. Then we have developed a novel platform and relevant modules with integrated AI processing and edge computer paradigms considering issues related to scalability, heterogeneity, security, and interoperability of IoT services. Further, each component is designed to handle the control signals, data flows, microservice orchestration, and resource composition to match with the IoT application requirements. Finally, the effectiveness of the proposed platform is tested and have been verified.

Information leaks are a top concern to industry and government leaders. The Internet of Things (IoT) is a rapidly growing technology capable of sensing real-world events. IoT devices lack a common security standard and typically use lightweight security solutions, exposing the sensitive real-world data they gather. Covert channels are a practical method of exfiltrating data from these devices.This research presents a novel IoT covert timing channel (CTC) that encodes data within preexisting network information, namely ports or addresses. This method eliminates the need for inter-packet delays (IPD) to encode data. Seven different encoding methods are implemented between two IoT protocols, TCP/IP and ZigBee. The TCP/IP covert channel is created by mimicking a Ring smart doorbell and implemented using Amazon Web Services (AWS) servers to generate traffic. The ZigBee channel is built by copying a Philips Hue lighting system and executed on an isolated local area network (LAN). Variants of the CTC focus either on Stealth or Bandwidth. Stealth methods mimic legitimate traffic captures to make them difficult to detect while the Bandwidth methods forgo this approach for maximum throughput. Detection results are presented using shape-based and regularity-based detection tests.The Stealth results have a throughput of 4.61 bits per second (bps) for TCP/IP and 3.90 bps for ZigBee. They also evade shape and regularity-based detection tests. The Bandwidth methods average 81.7 Kbps for TCP/IP and 9.76 bps for ZigBee but are evident in detection tests. The results show that CTC using address or port encoding can have superior throughput or detectability compared to IPD-based CTCs.

The computing of smart devices at the perception layer of the power Internet of Things is often insufficient, and complex computing can be outsourced to server resources such as the cloud computing, but the allocation process is not safe and controllable. Under special constraints of the power Internet of Things such as multi-users and heterogeneous terminals, we propose a CP-ABE-based non-interactive verifiable computation model of perceptual layer data. This model is based on CP-ABE, NPOT, FHE and other relevant safety and verifiable theories, and designs a new multi-user non-interactive secure verifiable computing scheme to ensure that only users with the decryption key can participate in the execution of NPOT Scheme. In terms of the calculation process design of the model, we gave a detailed description of the system model, security model, plan. Based on the definition given, the correctness and safety of the non-interactive safety verifiable model design in the power Internet of Things environment are proved, and the interaction cost of the model is analyzed. Finally, it proves that the CP-ABE-based non-interactive verifiable computation model for the perceptual layer proposed in this paper has greatly improved security, applicability, and verifiability, and is able to meet the security outsourcing of computing in the power Internet of Things environment.

Metaverse technologies depend on various advanced human-computer interaction (HCI) devices to be supported by extended reality (XR) technology. Many new HCI devices are supported by wireless Internet of Things (IoT) networks, where a reliable routing scheme is essential for seamless data trans-mission. Routing Protocol for Low power and Lossy networks (RPL) is a key routing technology used in IPv6-based low power and lossy networks (LLNs). However, in the networks that are configured, such as small wireless devices applying the IEEE 802.15.4 standards, due to the lack of a system that manages the identity (ID) at the center, the maliciously compromised nodes can make fabricated IDs and pretend to be a legitimate node. This behavior is called Sybil attack, which is very difficult to respond to since attackers use multiple fabricated IDs which are legally disguised. In this paper, Sybil attack countermeasures on RPL-based networks published in recent studies are compared and limitations are analyzed through simulation performance analysis.

Distributed ledger technologies (DLTs) based on Directed Acyclic Graphs (DAGs) have been gaining much attention due to their performance advantage over the traditional blockchain. IOTA is an example of DAG-based DLT that has shown its significance in the Internet of Things (IoT) environment. Despite that, IOTA is vulnerable to double-spend attacks, which threaten the immutability of the ledger. In this paper, we propose an efficient yet simple method for detecting a parasite chain, which is one form of attempting a double-spend attack in the IOTA network. In our method, a score function measuring the importance of each transaction in the IOTA network is employed. Any abrupt change in the importance of a transaction is reflected in the 1st and 2nd order derivatives of this score function, and therefore used in the calculation of an anomaly score. Due to how the score function is formulated, this anomaly score can be used in the detection of a particular type of parasite chain, characterized by sudden changes in the in-degree of a transaction in the IOTA graph. The experimental results demonstrate that the proposed method is accurate and linearly scalable in the number of edges in the network.

The Manufacturer Usage Description (MUD) standard aims to reduce the attack surface for IoT devices by locking down their behavior to a formally-specified set of network flows (access control entries). Formal network behaviors can also be systematically and rigorously verified in any operating environment. Enforcing MUD flows and monitoring their activity in real-time can be relatively effective in securing IoT devices; however, its scope is limited to endpoints (domain names and IP addresses) and transport-layer protocols and services. Therefore, misconfigured or compromised IoTs may conform to their MUD-specified behavior but exchange unintended (or even malicious) contents across those flows. This paper develops PicP-MUD with the aim to profile the information content of packet payloads (whether unencrypted, encoded, or encrypted) in each MUD flow of an IoT device. That way, certain tasks like cyber-risk analysis, change detection, or selective deep packet inspection can be performed in a more systematic manner. Our contributions are twofold: (1) We analyze over 123K network flows of 6 transparent (e.g., HTTP), 11 encrypted (e.g., TLS), and 7 encoded (e.g., RTP) protocols, collected in our lab and obtained from public datasets, to identify 17 statistical features of their application payload, helping us distinguish different content types; and (2) We develop and evaluate PicP-MUD using a machine learning model, and show how we achieve an average accuracy of 99% in predicting the content type of a flow.

The spread of the Internet of Things (IoT) and the use of smart control systems in many mission-critical or safety-critical applications domains, like automotive or aeronautical, make devices attractive targets for attackers. Nowadays, several of these are mixed-criticality systems, i.e., they run both high-criticality tasks (e.g., a car control system) and low-criticality ones (e.g., infotainment). High-criticality routines often employ Real-Time Operating Systems (RTOS) to enforce hard real-time requirements, while the tasks with lower constraints can be delegated to more generic-purpose operating systems (GPOS).Much of the control code for these devices is written in memory-unsafe languages such as C and C++. This makes them susceptible to powerful binary attacks, such as the famous Return-Oriented Programming (ROP). Control-Flow Integrity (CFI) is the most investigated security technique to protect against such threats. At now, CFI solutions for real-time embedded systems are not as mature as the ones for general-purpose systems, and even more, there is a lack of in-depth studies on how different operating systems with different security requirements and timing constraints can coexist on a single multicore platform.This paper aims at drawing attention to the subject, discussing the current scientific proposal, and in turn proposing a solution for an optimized asymmetric verification system for execution integrity. By using an embedded hypervisor, predefined cores could be dedicated to only high or low-criticality tasks, with the high-priority core being monitored by the lower-criticality core, relying on offline binary instrumentation and a light exchange of information and signals at runtime. The work also presents preliminary results about a possible implementation for multicore ARM platforms, running both RTOS and GPOS, both in terms of security and performance penalties.

This paper proposes a Mobile IoT optimization method for Next-Generation networks by evaluating a series of named-based techniques implemented in Information-Centric Networking (ICN). The idea is based on the possibility to have a more suitable naming and forwarding mechanism to be implemented in IoT. The main advantage of the method is in achieving a higher success packet rate and data rate by following the proposed technique even when the device is mobile / roaming around. The proposed technique is utilizing a root prefix naming which allows faster process and dynamic increase for content waiting time in Pending Interest Table (PIT). To test the idea, a simulation is carried out by mimicking how IoT can be implemented, especially in smart cities, where a user can also travel and not be static. Results show that the proposed technique can achieve up to a 13% interest success rate and an 18.7% data rate increase compared to the well-known implementation algorithms. The findings allow for possible further cooperation of data security factors and ensuring energy reduction through leveraging more processes at the edge node.

This paper presents a novel authentication method based on a distributed version of Kerberos for UAVs. One of the major problems of UAVs in recent years has been cyber-attacks which allow attackers to control the UAV or access its information. The growing use of UAVs has encouraged us to investigate the methods of their protection especially authentication of their users. In the past, the Kerberos system was rarely used for authentication in UAV systems. In our proposed method, based on a distributed version of Kerberos, we can authenticate multiple ground stations, users, and controllers for one or more UAVs. This method considers most of the security aspects to protect UAV systems mainly in the authentication phase and improves the security of UAVs and ground control stations and their communications considerably.

Big Data (BD) is the combination of several technologies which address the gathering, analyzing and storing of massive heterogeneous data. The tremendous spurt of the Internet of Things (IoT) and different technologies are the fundamental incentive behind this enduring development. Moreover, the analysis of this data requires high-performance servers for advanced and parallel data analytics. Thus, data owners with their limited capabilities may outsource their data to a powerful but untrusted environment, i.e., the Cloud. Furthermore, data analytic techniques performed on external cloud may arise various security intimidations regarding the confidentiality and the integrity of the aforementioned; transferred, analyzed, and stored data. To countermeasure these security issues and challenges, several techniques have been addressed. This survey paper aims to summarize and emphasize the security threats within Big Data framework, in addition, it is worth mentioning research work related to Big Data Analytics (BDA).

The Internet of Things (IoT) is rapidly evolving, allowing physical items to share information and coordinate with other nodes, increasing IoT’s value and being widely applied to various applications. Radio Frequency Identification (RFID) is usually used in IoT applications to automate item identification by establishing symmetrical communication between the tag device and the reader. Because RFID reading data is typically in plain text, a security mechanism is required to ensure that the reading results from this RFID data remain confidential. Researchers propose a lightweight encryption algorithm framework for IoT-based RFID applications to address this security issue. Furthermore, this research assesses the implementation of lightweight encryption algorithms, such as Grain v1 and Espresso, as two systems scenarios. The Grain v1 encryption is the final eSTREAM project that accepts an 80-bit key, 64-bit IV, and has a 160-bit internal state with limited application. In contrast, the Espresso algorithm has been implemented in various applications such as 5G wireless communication. Furthermore, this paper tested the performance of each encryption algorithm in the microcontroller and inspected the network performance in an IoT system.

Cyberattacks have been progressed in the fields of Internet of Things, and artificial intelligence technologies using the advanced persistent threat (APT) method recently. The damage caused by ransomware is rapidly spreading among APT attacks, and the range of the damages of individuals, corporations, public institutions, and even governments are increasing. The seriousness of the problem has increased because ransomware has been evolving into an intelligent ransomware attack that spreads over the network to infect multiple users simultaneously. This study used open source endpoint detection and response tools to build and test a framework environment that enables systematic ransomware detection at the network and system level. Experimental results demonstrate that the use of EDR tools can quickly extract ransomware attack features and respond to attacks.

Internet of Things (IoT) and those protocol CoAP and MQTT has security issues that have entirely changed the security strategy should be utilized and behaved for devices restriction. Several challenges have been observed in multiple domains of security, but Distributed Denial of Service (DDoS) have actually dangerous in IoT that have RT. Thus, the IoT paradigm and those protocols CoAP and MQTT have been investigated to seek whether network services could be efficiently delivered for resources usage, managed, and disseminated to the devices. Internet of Things is justifiably joined with the best practices augmentation to make this task enriched. However, factors behaviors related to traditional networks have not been effectively mitigated until now. In this paper, we present and deep, qualitative, and comprehensive systematic mapping to find the answers to the following research questions, such as, (i) What is the state-of-the-art in IoT security, (ii) How to solve the restriction devices challenges via infrastructure involvement, (iii) What type of technical/protocol/ paradigm needs to be studied, and (iv) Security profile should be taken care of, (v) As the proposals are being evaluated: A. If in simulated/virtualized/emulated environment or; B. On real devices, in which case which devices. After doing a comparative study with other papers dictate that our work presents a timely contribution in terms of novel knowledge toward an understanding of formulating IoT security challenges under the IoT restriction devices take care.

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.