Biblio

In this paper, we investigate how to automatically persist versioned data structures in distributed settings (e.g. cloud + edge) using append-only storage. By doing so, we facilitate resiliency by enabling program state to survive program activations and termination, and program-level data structures and their version information to be accessed programmatically by multiple clients (for replay, provenance tracking, debugging, and coordination avoidance, and more). These features are useful in distributed, failure-prone contexts such as those for heterogeneous and pervasive Internet of Things (IoT) deployments. We prototype our approach within an open-source, distributed operating system for IoT. Our results show that it is possible to achieve algorithmic complexities similar to those of in-memory versioning but in a distributed setting.

One of the major issues which are hindering widespread and seamless adoption of Internet of Thing (IoT) is security. The IoT devices are vulnerable and susceptible to attacks which became evident from a series of recent large-scale distributed denial-of-service (DDoS) attacks, leading to substantial business and financial losses. Furthermore, in order to find vulnerabilities in IoT, there is a lack of comprehensive security analysis framework. In this paper, we present a modular, adaptable and tunable framework, called PIT, to probe IoT systems at different layers of design and implementation. PIT consists of several security analysis engines, viz., penetration testing, fuzzing, static analysis, and dynamic analysis and an exploitation engine to discover multiple IoT vulnerabilities, respectively. We also develop a novel grey-box fuzzer, called Applica, as a part of the fuzzing engine to overcome the limitations of the present day fuzzers. The proposed framework has been evaluated on a real-world IoT testbed comprising of the state-of-the-art devices. We discovered several network and system-level vulnerabilities such as Buffer Overflow, Denial-of-Service, SQL Injection, etc., and successfully exploited them to demonstrate the presence of security loopholes in the IoT devices.

A kitchen underpinned by the Internet of Things (IoT) requires the management of complex procedural processes. This is due to the fact that when supporting an end-user in the preparation of even only one dish, various devices may need to coordinate with each other. Additionally, it is challenging— yet desirable—to enable an end-user to program their kitchen devices according to their preferred behaviour and to allow them to visualise and track their cooking workflows. In this paper, we compared two semantic representations, namely, Behaviour Trees and the DX-MAN model. We analysed these representations based on their suitability for a range of end-users (i.e., novice to experienced). The methodology required the analysis of smart kitchen user requirements, from which we inferred that the main architectural requirements for IoT cooking workflows are variability and compositionality. Guided by the user requirements, we examined various scenarios and analysed workflow complexity and feasibility for each representation. On the one hand, we found that execution complexity tends to be higher on Behaviour Trees. However, due to their fallback node, they provide more transparency on how to recover from unprecedented circumstances. On the other hand, parameter complexity tends to be somewhat higher for the DX-MAN model. Nevertheless, the DX-MAN model can be favourable due to its compositionality aspect and the ease of visualisation it can offer.

As a supplement to the cloud computing model, the edge computing model can use edge servers and edge devices to coordinate information processing on the edge of the network to help Internet of Thing (IoT) data storage, transmission, and computing tasks. In view of the complex and changeable situation of edge computing IoT scenarios, this paper proposes a multi-dimensional trust evaluation factor selection scheme. Improve the traditional trusted modeling method based on direct/indirect trust, introduce multi-dimensional trusted decision attributes and rely on the collaboration of edge servers and edge device nodes to infer and quantify the trusted relationship between nodes, and combine the information entropy theory to smoothly weight the calculation results of multi-dimensional decision attributes. Improving the current situation where the traditional trusted assessment scheme's dynamic adaptability to the environment and the lack of reliability of trusted assessment are relatively lacking. Simulation experiments show that the edge computing IoT multi-dimensional trust evaluation model proposed in this paper has better performance than the trusted model in related literature.

The Internet of Things (IoT) paradigm has been proposed in the last few years with the goal of addressing technical problems in fields such as home and industrial automation, smart lighting systems and traffic monitoring. However, due to the very nature of the IoT devices (generally low-powered and often lacking strong security functionalities), typical deployments pose a great risk in terms of security and privacy. In this respect, the utilization of both a Trusted Execution Environment (TEE) and a Trusted Platform Module (TPM) can serve as a countermeasure against typical attacks. Furthermore, these functional blocks can serve as safe key storage services and provide a robust secure boot implementation and a firmware update mechanism, thus ensuring run-time authentication and integrity. The Common Criteria for Information Technology Security Evaluation allows to determine the degree of attainment of precise security properties in a product. The main objective of this work is to identify, propose and compose bricks of protection profile (PP), as defined by Common Criteria, that are applicable to secure IoT architectures. Moreover, it aims at giving some guiding rules and facilitate future certifications of components and/or their composition. Finally, it also provides a structure for a future methodology of assessment for IoT devices.

The paper describes a methodology for assessing non-functional requirements, such as trust characteristics for applications running on computationally constrained devices in the Internet of Things. The methodology is demonstrated through an example of a microcontroller-based temperature monitoring system. The concepts of trust and trustworthiness for software and devices of the Internet of Things are complex characteristics for describing the correct and secure operation of such systems and include aspects of operational and information security, reliability, resilience and privacy. Machine learning models, which are increasingly often used for such tasks in recent years, are resource-consuming software implementations. The paper proposes to use a logic circuit model to implement the above algorithms as an additional module for computationally constrained devices for checking the trustworthiness of applications running on them. Such a module could be implemented as a hardware, for example, as an FPGA in order to achieve more effectiveness.

Internet of Things (IoT) connects various nodes such as sensor devices. For users from foreign networks, their direct access to the data of sensor devices is restricted because of security threats. Therefore, a ticket-based authentication scheme was proposed, which can mutually authenticate a mobile device and a sensor device. This scheme with new features fills a gap in IoT authentication, but the scheme has not been verified formally. Hence, it is important to study the security and reliability of the scheme from the perspective of formal methods.In this paper, we model this scheme using Communicating Sequential Processes (CSP). Considering the possibility of key leakage caused by security threats in IoT networks, we also build models where one of the keys used in the scheme is leaked. With the model checker Process Analysis Toolkit (PAT), we verify four properties (deadlock freedom, data availability, data security, and data authenticity) and find that the scheme cannot satisfy the last two properties with key leakage. Thus, we propose two improved models. The verification results show that the first improved model can guarantee data security, and the second one can ensure both data security and data authenticity.

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.

The development of edge computing and machine learning technologies have led to the growth of Industrial IoT systems. Autonomous decision making and smart manufacturing are flourishing in the current age of Industry 4.0. By providing more compute power to edge devices and connecting them to the internet, the so-called Cyber Physical Systems are prone to security threats like never before. Security in the current industry is based on cryptographic techniques that use pseudorandom number keys. Keys generated by a pseudo-random number generator pose a security threat as they can be predicted by a malicious third party. In this work, we propose a secure Industrial IoT Architecture that makes use of true random numbers generated by a quantum random number generator (QRNG). CITRIOT's FireConnect IoT node is used to show the proof of concept in a quantum-safe network where the random keys are generated by a cloud based quantum device. We provide an implementation of QRNG on both real quantum computer and quantum simulator. Then, we compare the results with pseudorandom numbers generated by a classical computer.

Higher education is increasingly called upon to enhance cyber education, including hands-on "experiential" training. The good news is that additional tools and techniques are becoming more available, both in-house and through third parties, to provide cyber training environments and simulations at various features and price points. However, the training thus far has only focused on "traditional" Cybersecurity that lightly touches on wireless in undergraduate and master's degree programs, and certifications. The purpose of this research is to identify and recognize nascent cyber training emphasizing a broader spectrum of wireless security and encourage curricular development that includes critical experiential training. Experiential wireless security training is important to keep pace with the growth in wireless communication mediums and associated Internet of Things (IoT) and Cyber Physical System (CPS) applications. Cyber faculty at a university offering undergraduate and master's Cybersecurity degrees authored this paper; both degrees are offered to resident as well as online students.

This paper considers a multivariate quickest detection problem with false data injection (FDI) attacks in internet of things (IoT) systems. We derive a sequential generalized likelihood ratio test (GLRT) for zero-mean Gaussian FDI attacks. Exploiting the fact that covariance matrices are positive, we propose strategies to detect positive semi-definite matrix additions rather than arbitrary changes in the covariance matrix. The distribution of the GLRT is only known asymptotically whereas quickest detectors deal with short sequences, thereby leading to loss of performance. Therefore, we use a finite-sample correction to reduce the false alarm rate. Further, we provide a numerical approach to estimate the threshold sequences, which are analytically intractable to compute. We also compare the average detection delay of the proposed detector for constant and varying threshold sequences. Simulations showed that the proposed detector outperforms the standard sequential GLRT detector.

Recently, several Internet of Things Tools have been created, contributing to growing network loads. To refrain from IoT should use the idea of cognitive radio networks because of the lack of bandwidth. This article presents much of the research discusses the distribution of channels and preparation of packets when combining cognitive radio networks with IoT technology and we are further discussing the spectrum-based Features and heterogeneity in cognitive IoT Security. Surveying the research performed in this field reveals that the work performed is still developing. A variety of inventions and experiments are part of its initial phases.

Multimedia Internet of Things (IoT) devices, especially, the smartphones are embedded with sensors including Global Positioning System (GPS), barometer, microphone, accelerometer, etc. These sensors working together, present a fairly complete picture of the citizens' daily activities, with implications for their privacy. With the internet, Citizens in Smart Cities are able to perform their daily life activities online with their connected electronic devices. But, unfortunately, computer hackers tend to write automated malicious applications to attack websites on which these citizens perform their activities. These security threats sometime put their private information at risk. In order to prevent these security threats on websites, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHAs) are generated, as a form of security mechanism to protect the citizens' private information. But with the advancement of deep learning, text-based CAPTCHAs can sometimes be vulnerable. As a result, it is essential to conduct performance evaluation on the CAPTCHAs that are generated before they are deployed on multimedia web applications. Therefore, this work proposed an optimal and light-weight Convolutional Neural Network (CNN) to solve both numerical and alpha-numerical complex text-based CAPTCHAs simultaneously. The accuracy of the proposed CNN model has been accelerated based on Cyclical Learning Rates (CLRs) policy. The proposed CLR-CNN model achieved a high accuracy to solve both numerical and alpha-numerical text-based CAPTCHAs of 99.87% and 99.66%, respectively. In real-time, we observed that the speed of the model has increased, the model is lightweight, stable, and flexible as compared to other CAPTCHA solving techniques. The result of this current work will increase awareness and will assist multimedia security Researchers to continue and develop more robust text-based CAPTCHAs with their security mechanisms capable of protecting the private information of citizens in Smart Cities.

Lightning Network (LN) addresses the scalability problem of Bitcoin by leveraging off-chain transactions. Nevertheless, it is not possible to run LN on resource-constrained IoT devices due to its storage, memory, and processing requirements. Therefore, in this paper, we propose an efficient and secure protocol that enables an IoT device to use LN's functions through a gateway LN node. The idea is to involve the IoT device in LN operations with its digital signature by replacing original 2-of-2 multisignature channels with 3-of-3 multisignature channels. Our protocol enforces the LN gateway to request the IoT device's cryptographic signature for all operations on the channel. We evaluated the proposed protocol by implementing it on a Raspberry Pi for a toll payment scenario and demonstrated its feasibility and security.

New technologies from day to day are submitted with many vulnerabilities that can make data exploitation. Nowadays, IoT is a target for Cybercrime attacks as it is one of the popular platforms in the century. This research address the IoT security problem by carried a medium-interaction honeypot. Honeypot is one of the solutions that can be done because it is a system feed for the introduction of attacks and fraudulent devices. This research has created a medium interaction honeypot using Cowrie, which is used to maintain the Internet of Things device from malware attacks or even attack patterns and collect information about the attacker's machine. From the result analysis, the honeypot can record all trials and attack activities, with CPU loads averagely below 6,3%.

Hash constructions are used in cryptographic algorithms from very long. Features of Hashes that gives the applications the confidence to use them in security methodologies is “forward secrecy” Forward secrecy comes from one-way hash functions. Examples of earlier hash designs include SHA-3, MD-5, SHA-I, and MAME. Each of these is having their proven record to produce the security for the communication between unconstrained devices. However, this is the era of Internet of Things (IoT) and the requirement of lightweight hash designs are the need of hour. IoT mainly consists of constrained devices. The devices in IoT are having many constrained related to battery power, storage and transmission range. Enabling any security feature in the constrained devices is troublesome. Constrained devices under an IoT environment can work only with less complex and lightweight algorithms. Lightweight algorithms take less power to operate and save a lot of energy of the battery operated devices. SPONGENT, QUARK, HASH-ONE, PHOTON, are some of the well-known lightweight hash designs currently providing security to the IoT devices. In this paper, the authors will present an analysis of the functioning of different lightweight hash designs as well as their suitability to the IoT environment.

Internet of Battlefield Things (IoBT) is the application of Internet of Things (IoT) to a battlefield environment. IoBT networks operate in difficult conditions due to high mobility and unpredictable nature of battle fields and securing them is a challenge. There is increasing interest to use deception techniques to enhance the security of IoBT networks. A honeypot is a system installed on a network as a trap to attract the attention of an attacker and it does not store any valuable data. In this work, we introduce IoBT dual sensor gateways. We propose a Reinforcement Learning (RL)-assisted scheme, in which the IoBT dual sensor gateways intelligently switch between honeypot and real function based on a threshold. The optimal threshold is determined using reinforcement learning approach that adapts to nodes reputation. To focus on the impact of the mobile and uncertain behavior of IoBT networks on the proposed scheme, we consider the nodes as moving vehicles. We statistically analyze the results of our RL-based scheme obtained using ns-3 network simulation, and optimize value of the threshold.

Intrusion Detection System (IDS) is a system that could detect suspicious activity in a network. Two approaches are known for IDS, namely signature-based and anomaly-based. The anomaly-based detection method was chosen to detect suspicious and abnormal activity for the system that cannot be performed by the signature-based method. In this study, attack testing was carried out using three DoS tools, namely the LOIC, Torshammer, and Xerxes tools, with a test scenario using IDS and without IDS. From the test results that have been carried out, IDS has successfully detected the attacks that were sent, for the delivery of the most consecutive attack packages, namely Torshammer, Xerxes, and LOIC. In the detection of Torshammer attack tools on the target FTP Server, 9421 packages were obtained, for Xerxes tools as many as 10618 packages and LOIC tools as many as 6115 packages. Meanwhile, attacks on the target Web Server for Torshammer tools were 299 packages, for Xerxes tools as many as 530 packages, and for LOIC tools as many as 103 packages. The accuracy of the IDS performance results is 88.66%, the precision is 88.58% and the false positive rate is 63.17%.

In-vehicle CAN (Controller Area Network) bus network does not have any network security protection measures, which is facing a serious network security threat. However, most of the intrusion detection solutions requiring extensive computational resources cannot be implemented in in- vehicle network system because of the resource constrained ECUs. To add additional hardware or to utilize cloud computing, we need to solve the cost problem and the reliable communication requirement between vehicles and cloud platform, which is difficult to be applied in a short time. Therefore, we need to propose a short-term solution for automobile manufacturers. In this paper, we propose a signature-based light-weight intrusion detection system, which can be applied directly and promptly to vehicle's ECUs (Electronic Control Units). We detect the anomalies caused by several attack modes on CAN bus from real-world scenarios, which provide the basis for selecting signatures. Experimental results show that our method can effectively detect CAN traffic related anomalies. For the content related anomalies, the detection ratio can be improved by exploiting the relationship between the signals.

Vehicular networks have been considered as a hopeful technology to enhance road safety, which is a crossing area of Internet of Things (IoT) and Intelligent Transportation Systems (ITS). Current Internet architecture using the TCP/IP model and based on host-to-host is limited when it comes to vehicular communications which are characterized by high speed and dynamic topology. Thus, using Named Data Networking (NDN) in connected vehicles may tackle the issues faced with the TCP/IP model. In this paper, we investigate the security concerns of applying NDN in vehicular environments and discuss the remaining challenges in order to guide researchers in this field to choose their future research direction.

Vehicles play an integral part in the life of a human being by facilitating in everyday tasks. The major concern that arises with this fact is that the rate of vehicle thefts have increased exponentially and retrieving them becomes almost impossible as the responsible party completely alters the stolen vehicles, leaving them untraceable. Ultimately, tracking and monitoring of vehicles using on-vehicle sensors is a promising and an efficient solution. The Internet of Things (IoT) is expected to play a vital role in revolutionizing the Security and Safety industry through a system of sensor networks by periodically sending the data from the sensors to the cloud for storage, from where it can be accessed to view or take any necessary actions (if required). The main contributions of this paper are the implementation and results of the prototype of a vehicle tracking and monitoring system. The system comprises of an Arduino UNO board connected to the Global Positioning System (GPS) module, Neo-6M, which senses the exact location of the vehicle in the form of latitude and longitude, and the ESP8266 Wi-Fi module, which sends the data to the Application Programming Interface (API) Cloud service, ThingSpeak, for storage and analyzing. An Android based mobile application is developed that utilizes the stored data from the Cloud and presents the user with the findings. Results show that the prototype is not only simple and cost effective, but also efficient and can be readily used by everyone from all walks of life to protect their vehicles.

As modern vehicles are complex IoT devices with intelligence capable to connect to an external infrastructure and use Vehicle-to-Everything (V2X) communication, there is a need to secure the communication to avoid being a target for cyber-attacks. Also, the organs of the car (sensors, communication, and control) each could have a vulnerability, that leads to accidents or potential deaths. Manufactures of cars have a huge responsibility to secure the safety of their costumers and should not skip the important security research, instead making sure to implement important security measures, which makes your car less likely to be attacked. This paper covers the relevant attacks and threats to modern vehicles and presents a security analysis with potential countermeasures. We discuss the future of modern and autonomous vehicles and conclude that more countermeasures must be taken to create a future and safe concept.

With the gradually popular high-speed wireless networks and 5G environments, the quality and reliability of network services will be suited for mobile vehicles. In addition to communicating information between vehicles, they can also communicate information with surrounding roadside equipment, pedestrians or traffic signs, and thus improve the road safety of passers-by.Recently, various countries have continuously invested in research on autonomous driving and unmanned vehicles. The open communication environment of the Internet of Vehicles in 5G will expose all personal information in the field of wireless networks. This research is based on the consideration of information security and personal data protection. We will focus on how to protect the real-time transmission of information between mobile vehicles to prevent from imbedding or altering important transmission information by unauthorized vehicles, drivers or passers-by participating in communications. Moreover, this research proposes a multi-level security key management agreement based on a dynamic M-tree structure for Internet of Vehicles to achieve flexible and scalable key management on large-scale Internet of Vehicles.

The transportation system is becoming tremendously important in today's human activities and the number of urban vehicles grows rapidly. The vehicle theft also has become a shared concern for all vehicle owners. However, the present anti-theft system which maybe high reliable, lack of proper mechanism for preventing theft before it happens. This work proposes the internet of things based smart vehicle security staring system; efficient security provided to the vehicle owners relies on securing car ignition system by using a developed android application running on smart phone connected to the designed system installed in vehicle. With this system it is non- viable to access the vehicle's functional system in case the ignition key has been stolen or lost. It gives the drivers the ability to stay connected with their vehicle. Whenever the ignition key is stolen or lost, it is impossible to start the vehicle as the ignition system is still locked on the vehicle start and only the authorized person will be able to start the vehicle at convenient time with the combination of ignition key and smart phone application. This study proposes to design the system that uses node MCU, Bluetooth low energy (BLE), transistors, power relays and android smartphone in system testing. In addition, it is cost effective and once installed in the vehicle there is no more cost of maintenance.

Our proposal aims to help solving a trust problem between licensors and licensees that occurs during the active life of license agreements. We particularly focus on licensing of proprietary intellectual property (IP) that is embedded in Internet of Things (IoT) devices and services (e.g. patented technologies). To achieve this we propose to encode the logic of license agreements into smart licenses (SL). We define a SL as a `digital twin' of a licensing contract, i.e. one or more smart contracts that represent the full or relevant parts of a licensing agreement in machine readable and executable code. As SL are self enforcing, the royalty computation and execution of payments can be fully automated in a tamper free and trustworthy way. This of course, requires to employ a Distributed Ledger Technology (DLT). Such an Automated Licensing Payment System (ALPS) can thus automate an established business process and solve a longstanding trust issue in licensing markets. It renders traditional costly audits obsolete, lowers entry barriers for those who want to participate in licensing markets, and enables novel business models too complex with traditional approaches.