Biblio

One of the biggest problems in the current society is people's safety. Safety measures and mechanisms are especially important in the case of vulnerable social groups, such as migrants, homeless, and victims of domestic and/or sexual violence. In order to cope with this problem, we witness an increasing number of personal alarm systems in the market, most of them based on panic buttons. Nevertheless, none of them has got widespread acceptance mainly because of limited Human-Computer Interaction. In the context of this work, we developed an innovative mobile application that recognizes an attack through speech and gesture recognition. This paper describes such a system and presents its features, some of them based on the emerging concept of Human-in-the-Loop Cyber-physical Systems and new concepts of Human-Computer Interaction.

Personalized IoT adapt their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapt to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract user's private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. Being a new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or system behavior are not adequate to detect SpyCon. We discuss possible detection and mitigation mechanisms that can hinder the effect of SpyCon.

The rapid progress experienced in the Internet of Things (IoT) space is one that has introduced new and unique challenges for cybersecurity and IoT-Forensics. One of these problems is how digital forensics and incident response (DFIR) are handled in IoT. Since enormous users use IoT platforms to accomplish their day to day task, massive amounts of data streams are transferred with limited hardware resources; conducting DFIR needs a new approach to mitigate digital evidence and incident response challenges owing to the facts that there are no unified standard or classified principles for IoT forensics. Today's IoT DFIR relies on self-defined best practices and experiences. Given these challenges, IoT-related incidents need a more structured approach in identifying problems of DFIR. In this paper, we examined the major DFIR challenges in IoT by exploring the different phases involved in a DFIR when responding to IoT-related incidents. This study aims to provide researchers and practitioners a road-map that will help improve the standards of IoT security and DFIR.

With the widespread adoption of Internet of Things (IoT) applications around the world, security related problems become a challenge since the number of cybercrimes that must be identified and investigated increased dramatically. The volume of data generated and handled is immense due to the increased number of IoT applications around the world. As a result, when a cybercrime happens, the volume of digital data needs to be dealt with is massive. Consequently, more effort and time are needed to handle the security issues. As a result, in digital forensics, the analysis phase is an important and challenging phase. This paper proposes a generic proactive model for the cybercrime analysis process in the Internet of Things. The model is focused on the classification of evidences in advance based on its significance and relation to past crimes, as well as the severity of the evidence in terms of the probability occurrence of a cybercrime. This model is supposed to save time and effort during the automated forensic investigation process.

In recent years, with the rapid update of wireless communication technologies such as 5G and the Internet of Things, as well as the explosive growth of wireless intelligent devices, people's demand for radio spectrum resources is increasing, which leads spectrum scarcity is becoming more serious. To address the scarcity of spectrum, the Internet of Things based on cognitive radio (CR-IoT) has become an effective technique to enable IoT devices to reuse the spectrum that has been fully utilized. The frequency band information is transmitted through wireless communication in the CR-IoT network, so the node is easily to be eavesdropped or tampered with by attackers in the process of transmitting data, which leads to information leakage and wrong perception results. To deal with the security problem of channel data transmission, this paper proposes a chaotic compressed spectrum sensing algorithm. In this algorithm, the chaotic parameter package is utilized to generate the measurement matrix, which makes good use of the sensitivity of the initial value of chaotic system to improve the transmission security. And the introduction of the semi-tensor theory significantly reduces the dimension of the matrix that the secondary user needs to store. In addition, the semi-tensor compressed sensing is used in the fusion center for parallel reconstruction process, which effectively reduces the sensing time delay. The simulation results show that the chaotic compressed spectrum sensing algorithm can achieve faster, high-quality, and low-energy channel energy transmission.

Both the Internet of Things (IoT) and Information Centric Networking (ICN) have gathered a lot of attention from both research and industry in recent years. While ICN has proved to be beneficial in many situations, it is not widely deployed outside research projects, also not addressing needs of IoT application programming interfaces (APIs). On the other hand, today's IoT solutions are built on top of the host-centric communication model associated with the usage of the Internet Protocol (IP). This paper contributes a discussion on the need of an integration of a specific form of IoT APIs, namely WebSocket based streaming APIs, into an ICN. Furthermore, different access models are discussed and requirements are derived from real world APIs. Finally, the design of an ICN-style extension is presented using one of the examined APIs.

The prevalence of Internet of Things (IoT) allows heterogeneous and lightweight smart devices to collaboratively provide services with or without human intervention. With an ever-increasing presence of IoT-based smart applications and their ubiquitous visibility from the Internet, user data generated by highly connected smart IoT devices also incur more concerns on security and privacy. While a lot of efforts are reported to develop lightweight information assurance approaches that are affordable to resource-constrained IoT devices, there is not sufficient attention paid from the aspect of security solutions against hardware-oriented attacks, i.e. side channel attacks. In this paper, a COTS (commercial off-the-shelf) based Randomized Switched-Mode Voltage Regulation System (RSMVRS) is proposed to prevent power analysis based side channel attacks (P-SCA) on bare metal IoT edge device. The RSMVRS is implemented to direct power to IoT edge devices. The power is supplied to the target device by randomly activating power stages with random time delays. Therefore, the cryptography algorithm executing on the IoT device will not correlate to a predictable power profile, if an adversary performs a SCA by measuring the power traces. The RSMVRS leverages COTS components and experimental study has verified the correctness and effectiveness of the proposed solution.

The fields of transportation and fleet management have been evolving at a rapid pace and most of these changes are due to numerous incremental developments in the area. However, a comprehensive study that critically compares and contrasts all the existing techniques and methodologies in the area is still missing. This paper presents a comparative analysis of the vulnerabilities and security threats for IoT and their mitigation strategies in the context of transportation and fleet management. Moreover, we attempt to classify the existing strategies based on their underlying principles.

As the number of Internet of things devices increases, there is a growing importance of securely managing and storing the secret and private keys in these devices. Public-key cryptosystems or symmetric encryption algorithms both use special keys that need to be kept secret from other peers in the network. Additionally, ensuring the integrity of the installed application firmware of these devices is another security problem. In this study, private key storage methods are explained in general. Also, ESP32-S2 device is used for experimental case study for its robust built-in trusted platform module. Secure boot and flash encryption functionalities of ESP32-S2 device, which offers a solution to these security problems, are explained and tested in detail.

Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features based on the structure of trigger circuits for machine-learning-based hardware Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on the random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 63.6% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.7 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.

Ransomware is one of the most serious threats which constitute a significant challenge in the cybersecurity field. The cybercriminals use this attack to encrypts the victim's files or infect the victim's devices to demand ransom in exchange to restore access to these files and devices. The escalating threat of Ransomware to thousands of individuals and companies requires an urgent need for creating a system capable of proactively detecting and preventing ransomware. In this research, a new approach is proposed to detect and classify ransomware based on three machine learning algorithms (Random Forest, Support Vector Machines , and Näive Bayes). The features set was extracted directly from raw byte using static analysis technique of samples to improve the detection speed. To offer the best detection accuracy, CF-NCF (Class Frequency - Non-Class Frequency) has been utilized for generate features vectors. The proposed approach can differentiate between ransomware and goodware files with a detection accuracy of up to 98.33 percent.

With rapid advancement of Smart Grid as well as Internet of Things (IoT), current power distribution communication network faces the challenges of satisfying the emerging data transmission requirements of ubiquitous secure coverage for distributed power services. This paper focuses on secure ubiquitous wireless communication solution for power distribution Internet of Things (PDİoT) in Smart Grid. Detailed secure ubiquitous wireless communication networking topology is presented, and integrated encryption and communication device is developed. The proposed solution supports several State Secret cryptographic algorithm including SM1/SM2/SM3/SM4 as well as forward and reverse isolation functions, thus achieving secure wireless communication for PDİoT services.

Internet of things (IoT) healthcare devices, like other IoT devices, typically use proprietary protocol communications. Usually, these proprietary protocols are not audited and may present security flaws. Further, new proprietary protocols are desgined in the field of IoT devices, like data-over-sound communications. Data-over-sound is a new method of communication based on audio with increasing popularity due to its low hardware requirements. Only a speaker and a microphone are needed instead of the specific antennas required by Bluetooth or Wi-Fi protocols. In this paper, we analyze, audit and reverse engineer a modern IoT healthcare device used for performing electrocardiograms (ECG). The audited device is currently used in multiple hospitals and allows remote health monitoring of a patient with heart disease. For this auditing, we follow a black-box reverse-engineering approach and used STRIDE threat analysis methodology to assess all possible attacks. Following this methodology, we successfully reverse the proprietary data-over-sound protocol used by the IoT healthcare device and subsequently identified several vulnerabilities associated with the device. These vulnerabilities were analyzed through several experiments to classify and test them. We were able to successfully manipulate ECG results and fake heart illnesses. Furthermore, all attacks identified do not need any patient interaction, being this a transparent process which is difficult to detect. Finally, we suggest several short-term solutions, centred in the device isolation, as well as long-term solutions, centred in involved encryption capabilities.

Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints and is set to become part of a multi-billion industry. The resource constraints in this novel network infrastructure tier constricts the deployment of existing Network Intrusion Detection System with Deep Learning models (DLM). We address this issue by developing a novel light, fast and accurate `Edge-Detect' model, which detects Distributed Denial of Service attack on edge nodes using DLM techniques. Our model can work within resource restrictions i.e. low power, memory and processing capabilities, to produce accurate results at a meaningful pace. It is built by creating layers of Long Short-Term Memory or Gated Recurrent Unit based cells, which are known for their excellent representation of sequential data. We designed a practical data science pipeline with Recurring Neural Network to learn from the network packet behavior in order to identify whether it is normal or attack-oriented. The model evaluation is from deployment on actual edge node represented by Raspberry Pi using current cybersecurity dataset (UNSW2015). Our results demonstrate that in comparison to conventional DLM techniques, our model maintains a high testing accuracy of 99% even with lower resource utilization in terms of cpu and memory. In addition, it is nearly 3 times smaller in size than the state-of-art model and yet requires a much lower testing time.

With the explosive growth of IoT applications, billions of things are now connected via edge devices and a colossal volume of data is sent over the internet. Providing security to the user data becomes crucial. The rise in zero-day attacks are a challenge in IoT scenarios. With the large scale of IoT application detection and mitigation of such attacks by the network administrators is cumbersome. The edge device Raspberry pi is remotely logged using Secure Shell (SSH) protocol in 90% of the IoT applications. The case study of SSH brute force attack on the edge device Raspberry pi is demonstrated with experimentation in the IoT networking scenario using Intrusion Detection System (IDS). The IP crawlers available on the internet are used by the attacker to obtain the IP address of the edge device. The proposed system continuously monitors traffic, analysis the log of attack patterns, detects and mitigates SSH brute attack. An attack hijacks and wastes the system resources depriving the authorized users of the resources. With the proposed IDS, we observe 25% CPU conservation, 40% power conservation and 10% memory conservation in resource utilization, as the IDS, mitigates the attack and releases the resources blocked by the attacker.

The emergence of the 5G network has boosted the advancements in the field of the internet of things (IoT) and edge/cloud computing. We present a novel architecture to detect fire in indoor and outdoor environments, dubbed as EAC-FD, an abbreviation of edge and cloud-based fire detection. Compared with existing frameworks, ours is lightweight, secure, cost-effective, and reliable. It utilizes a hybrid edge and cloud computing framework with Intel neural compute stick 2 (NCS2) accelerator is for inference in real-time with Raspberry Pi 3B as an edge device. Our fire detection model runs on the edge device while also capable of cloud computing for more robust analysis making it a secure system. We compare different versions of SSD-MobileNet architectures with ours suitable for low-end devices. The fire detection model shows a good balance between computational cost frames per second (FPS) and accuracy.

Internet of Things (IoT) and its applications are becoming commonplace with more devices, but always at risk of network security. It is therefore crucial for an IoT network design to identify attackers accurately, quickly and promptly. Many solutions have been proposed, mainly concerning secure IoT architectures and classification algorithms, but none of them have paid enough attention to reducing the complexity. Our proposal in this paper is an edge-cloud architecture that fulfills the detection task right at the edge layer, near the source of the attacks for quick response, versatility, as well as reducing the cloud's workload. We also propose a multi-attack detection mechanism called LCHA (Low-Complexity detection solution with High Accuracy) , which has low complexity for deployment at the edge zone while still maintaining high accuracy. The performance of our proposed mechanism is compared with that of other machine learning and deep learning methods using the most updated BoT-IoT data set. The results show that LCHA outperforms other algorithms such as NN, CNN, RNN, KNN, SVM, KNN, RF and Decision Tree in terms of accuracy and NN in terms of complexity.

With the rapid development of Internet technologies, emerging network environments have been discussed, such as Internet of Things. In this manuscript, we proposed a novel subset predicate encryption for the access control in Internet of Things. Compared with the existing subset predicate encryption schemes, the proposed scheme enjoy the better efficiency due to the short private key and the efficient decryption procedure.

This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).

The increasing volume of domestic and foreign trade brings new challenges to the efficiency and safety supervision of transportation. With the rapid development of Internet technology, it has opened up a new era of intelligent Internet of Things and the modern marine Internet of Vessels. Radio Frequency Identification technology strengthens the intelligent navigation and management of ships through the unique identification function of “label is object, object is label”. Intelligent Internet of Vessels can achieve the function of “limited electronic monitoring and unlimited electronic deterrence” combined with marine big data and Cyber Physical Systems, and further improve the level of modern maritime supervision and service.

The paper deals with exponentially growing technology - Internet of Things (IoT) in the field of healthcare. It is spoken about the networked healthcare and medical architecture. The attention is given to the analysis of the international regulations on medical and healthcare cybersecurity. For building a trustworthy healthcare IoT solution, a developed normative hierarchical model of the international cybersecurity standards is provided. For cybersecurity assessment of such systems the case-oriented technique, which includes Advanced Security Assurance Case (ASAC) and an example on a wireless insulin pump of its application are provided.

The Internet of Things (IoT) is a ubiquitous system connecting many different devices - the things - which can be accessed from the distance. The cyber-physical systems (CPSs) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. This paper summarizes the state of the art of existing work on anomaly detection, fault-tolerance, and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for the CPS, which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable ``Trustworthy IoT for CPS''. Finally, this framework is illustrated on a selected use case: a smart sensor infrastructure in the transport domain.

In IoT scenarios, computational and communication costs on the user side are important problems. In most expressive ABE schemes, there is a linear relationship between the access structure size and the number of heavy pairing operations that are used in the decryption process. This property limits the application of ABE. We propose an expressive CP-ABE with the constant number of pairings in the decryption process. The simulation shows that the proposed scheme is highly efficient in encryption and decryption processes. In addition, we use the outsourcing method in decryption to get better performance on the user side. The main burden of decryption computations is done by the cloud without revealing any information about the plaintext. We introduce a new revocation method. In this method, the users' communication channels aren't used during the revocation process. These features significantly reduce the computational and communication costs on the user side that makes the proposed scheme suitable for applications such as IoT. The proposed scheme is selectively CPA-secure in the standard model.

With the processes of collecting, analyzing, and transmitting the data in the Internet of Things (IoT), the Internet of Medical Things (IoMT) comprises the medical equipment and applications connected to the healthcare system and offers an entity with real time, remote measurement, and analysis of healthcare data. However, the IoMT ecosystem deals with some great challenges in terms of security, such as privacy leaking, eavesdropping, unauthorized access, delayed detection of life-threatening episodes, and so forth. All these negative effects seriously impede the implementation of the IoMT ecosystem. To overcome these obstacles, this article presents an efficient, outsourced online/offline revocable ciphertext policy attribute-based encryption scheme with the aid of cloud servers and blockchains in the IoMT ecosystem. Our proposal achieves the characteristics of fine-grained access control, fast encryption, outsourced decryption, user revocation, and ciphertext verification. It is noteworthy that based on the chameleon hash function, we construct the private key of the data user with collision resistance, semantically secure, and key-exposure free to achieve revocation. To the best of our knowledge, this is the first protocol for a revocation mechanism by means of the chameleon hash function. Through formal analysis, it is proven to be secure in a selectively replayable chosen-ciphertext attack (RCCA) game. Finally, this scheme is implemented with the Java pairing-based cryptography library, and the simulation results demonstrate that it enables high efficiency and practicality, as well as strong reliability for the IoMT ecosystem.