Biblio

Smart cities are a wide range of projects made to facilitate the problems of everyday life and ensure security. Our interest focuses only on the Intelligent Transport System (ITS) that takes care of the transportation issues using the Vehicular Ad-Hoc Network (VANET) paradigm as its base. VANETs are a promising technology for autonomous driving that provides many benefits to the user conveniences to improve road safety and driving comfort. VANET is a promising technology for autonomous driving that provides many benefits to the user's conveniences by improving road safety and driving comfort. The problem with such rapid development is the continuously increasing digital threats. Among all these threats, we will target the Sybil attack since it has been proved to be one of the most dangerous attacks in VANETs. It allows the attacker to generate multiple forged identities to disseminate numerous false messages, disrupt safety-related services, or misuse the systems. In addition, Machine Learning (ML) is showing a significant influence on classification problems, thus we propose a behavior-based classification algorithm that is tested on the provided VeReMi dataset coupled with various machine learning techniques for comparison. The simulation results prove the ability of our proposed mechanism to detect the Sybil attack in VANETs.

Although 6LoWPAN has brought about a revolutionary leap in networking for Low-power Lossy Networks, challenges still exist, including security concerns that are yet to answer. The most common type of attack on 6LoWPANs is the network layer, especially routing attacks, since the very members of a 6LoWPAN network have to carry out packet forwarding for the whole network. According to the initial purpose of IoT, these nodes are expected to be resource-deficient electronic devices with an utterly stochastic time pattern of attachment or detachment from a network. This issue makes preserving their authenticity or identifying their malignity hard, if not impossible. Since 6LoWPAN is a successor and a hybrid of previously developed wireless technologies, it is inherently prone to cyber-attacks shared with its predecessors, especially Wireless Sensor Networks (WSNs) and WPANs. On the other hand, multiple attacks have been uniquely developed for 6LoWPANs due to the unique design of the network layer protocol of 6LoWPANs known as RPL. While there exist publications about attacks on 6LoWPANs, a comprehensive survey exclusively on RPL-specific attacks is felt missing to bold the discrimination between the RPL-specific and non-specific attacks. Hence, the urge behind this paper is to gather all known attacks unique to RPL in a single volume.

The Internet of Things (IoT) continuously grows as applications require connectivity and sensor networks are being deployed in multiple application domains. With the increased applicability demand, the need for testing and development frameworks also increases. This paper presents a novel simulation framework for testing IPv6 over Low Power Wireless Personal Networks (6LoWPAN) networks using the Mininet-WiFi simulator. The goal of the simulation framework is to allow easier automation testing of large-scale networks and to also allow easy configuration. This framework is a starting point for many development scenarios targeting traffic management, Quality of Service (QoS) or security network features. A basic smart city simulation is presented which demonstrates the working principles of the framework.

The rapid shift towards smart cities, particularly in the era of pandemics, necessitates the employment of e-learning, remote learning systems, and hybrid models. Building adaptive and personalized education becomes a requirement to mitigate the downsides of distant learning while maintaining high levels of achievement. Explainable artificial intelligence (XAI), machine learning (ML), and the internet of behaviour (IoB) are just a few of the technologies that are helping to shape the future of smart education in the age of smart cities through Customization and personalization. This study presents a paradigm for smart education based on the integration of XAI and IoB technologies. The research uses data acquired on students' behaviours to determine whether or not the current education systems respond appropriately to learners' requirements. Despite the existence of sophisticated education systems, they have not yet reached the degree of development that allows them to be tailored to learners' cognitive needs and support them in the absence of face-to-face instruction. The study collected data on 41 learner's behaviours in response to academic activities and assessed whether the running systems were able to capture such behaviours and respond appropriately or not; the study used evaluation methods that demonstrated that there is a change in students' academic progression concerning monitoring using IoT/IoB to enable a relative response to support their progression.

The Internet of Things (IoT) technology is being utilized in endless applications nowadays and the security of these applications is of great importance. Image based IoT applications serve a wide variety of fields such as medical application and smart cities. Steganography is a great threat to these applications where adversaries can use the images in these applications to hide malicious messages. Therefore, this paper presents an image steganalysis technique that employs Convolutional Neural Networks (CNN) to detect the infamous JPEG steganography technique: JPEG universal wavelet relative distortion (J-UNIWARD). Several experiments were conducted to determine the breaking point of J-UNIWARD, whether the hiding technique relies on correlation of the images, and the effect of utilizing Discrete Cosine Transform (DCT) on the performance of the CNN. The results of the CNN display that the breaking point of J-UNIWARD is 1.5 (bpnzAC), the correlation of the database affects the detection accuracy, and DCT increases the detection accuracy by 13%.

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition.

Smart city technologies promise reduced congestion by optimizing transportation movements. Increased connectivity, however, may increase the attack surface of a municipality's critical functions. Increased supply chain attacks (up nearly 80 % in 2019) and municipal ransomware attacks (up 60 % in 2019) motivate the need for holistic approaches to risk assessment. Therefore, we present a methodology to quantify the degree to which supply-chain movements may be observed or disrupted via compromised smart-city devices. Our data-processing pipeline uses publicly available datasets to model intermodal commodity flows within and surrounding a municipality. Using a hierarchy tree to adaptively sample spatial networks within geographic regions of interest, we bridge the gap between grid- and network-based risk assessment frameworks. Results based on fieldwork for the Jack Voltaic exercises sponsored by the Army Cyber Institute demonstrate our approach on intermodal movements through Charleston, SC and San Diego, CA.

Transportation of people and goods is important and crucial in the context of smart cities. The trend in regard of people's mobility is moving from privately owned vehicles towards shared mobility. This trend is even stronger in urban areas, where space for parking is limited, and the mobility is supported by the public transport system, which lowers the need for private vehicles. Several challenges and barriers of currently available solutions retard a massive growth of this mobility option, such as the trust problem, data monopolism, or intermediary costs. Decentralizing mobility management is a promising approach to solve the current problems of the mobility market, allowing to move towards a more usable internet of mobility and smart transportation. Leveraging blockchain technology allows to cut intermediary costs, by utilizing smart contracts. Important in this ecosystem is the proof of identity of participants in the blockchain network. To proof the possession of the claimed identity, the private key corresponding to the wallet address is utilized, and therefore essential to protect. In this paper, a blockchain-based shared mobility platform is proposed and a proof-of-concept is shown. First, current problems and state-of-the-art systems are analyzed. Then, a decentralized concept is built based on ERC-721 tokens, implemented in a smart contract, and augmented with a Hardware Security Module (HSM) to protect the confidential key material. Finally, the system is evaluated and compared against state-of-the-art solutions.

The Internet is evolving everywhere and expanding its entity globally. The IoT(Internet of things) is a new and interesting concept introduced in this world of internet. Generally it is interconnected computing device which can be embedded in our daily routine objects through which we can send and receive data. It is beyond connecting computers and laptops only although it can connect billion of devices. It can be described as reliable method of communication that also make use of other technologies like wireless sensor, QR code etc. IoT (Internet of Things) is making everything smart with use of technology like smart homes, smart cities, smart watches. In this chapter, we will study the security algorithms in IoT (Internet of Things) which can be achieved with encryption process. In the world of IoT, data is more vulnerable to threats. So as to protect data integrity, data confidentiality, we have Light weight Encryption Algorithms like symmetric key cryptography and public key cryptography for secure IoT (Internet of Things) named as Secure IoT. Because it is not convenient to use full encryption algorithms that require large memory size, large program code and larger execution time. Light weight algorithms meet all resource constraints of small memory size, less execution time and efficiency. The algorithms can be measured in terms of key size, no of blocks and algorithm structure, chip size and energy consumption. Light Weight Techniques provides security to smart object networks and also provides efficiency. In Symmetric Key Cryptography, two parties can have identical keys but has some practical difficulty. Public Key Cryptography uses both private and public key which are related to each other. Public key is known to everyone while private key is kept secret. Public Key cryptography method is based on mathematical problems. So, to implement this method, one should have a great expertise.

In order to improve the security and encryption efficiency of multi image cross hybrid encryption, a multi image cross hybrid encryption method based on combined chaotic system is proposed. On the basis of chaos theory, the characteristics of Logistic chaotic system and Lorenz chaotic system are analyzed, and Logistic chaotic system and Lorenz chaotic system are combined to form a combined chaotic system. In order to improve the security of multi image encryption, the plaintext image is preprocessed before encryption. The preprocessing process is embedding random number sequence in the plaintext image. Based on the random number embedded image, the combined chaotic system is applied to the multi image cross chaotic encryption method. Experimental results show that the proposed method has high encryption security and high encryption efficiency.

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.

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fréchet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. The proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection.

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.

With the rapid development of the information technology era, the era of big data has also arrived. While computer networks are promoting the prosperity and development of society, their applications have become more extensive and in-depth. Smart city video surveillance systems have entered an era of networked surveillance and business integration. The problems are also endless. This article discusses computer network security in the era of big data, hoping to help strengthen the security of computer networks in our country. This paper studies the computer network security prevention strategies of smart cities in the era of big data.

A grouping-proof protocol aims to generate an evidence that two or more RFID (Radio Frequency Identification) tags in a group are coexistent, which has been widely deployed in practical scenarios, such as healthcare, supply-chain management, and so on. However, existing grouping-proof protocols have many issues in security and efficiency, either incompatible with EPCglobal Class-1 Generation-2 (C1G2) standard, or vulnerable to different attacks. In this paper, we propose a lightweight grouping-proof protocol which only utilizes bitwise operations (AND, XOR) and 128-bit pseudorandom number generator (PRNG). 2-round interactions between the reader and the tags allow them to cooperate on fast authentication in parallel mode where the reader broadcasts its round messages rather than hang on for the prior tag and then fabricate apposite output for the next tag consecutively. Our design enables the reader to aggregate the first round proofs (to bind the membership of tags in the same group) generated by the tags to an authenticator of constant size (independent of the number of tags) that can then be used by the tags to generate the second round proofs (and that will be validated by the verifier). Formal security (i.e., PPT adversary cannot counterfeit valid grouping-proof that can be accepted by any verifier) of the proposed protocol relies on the hardness of the learning parity with noise (LPN) problem, which can resist against quantum computing attacks. Other appealing features (e.g., robustness, anonymity, etc.) are also inspected. Performance evaluation shows its applicability to C1G2 RFID.

Automatic dependent surveillance-broadcast (ADS- B) records provide an important basis and evidence for future route planning and accountability. However, due to the lack of effective support for the integrity and confidentiality of ADS-B, the air traffic control (ATC) system based on ADS-B faces serious security threats. Once the data is tampered with, it will cause immeasurable losses to society. The ADS-B data is arranged in chronological order, and the order-preserving encryption method allows users to directly search for ciphertexts by time. However, encryption alone does not guarantee the integrity of the data. The attacker can still destroy the integrity of the data by modifying the ciphertext. This paper proposes a secure ADS- B protection scheme that supports queries. We construct a dynamic order-preserving encryption (DOPE) scheme to achieve data confidentiality and sequential search of target data in the ciphertext. In addition, the scheme achieves fast integrity checking by calculating the unique verification label of the entire ciphertext, and supports blockless verification, which means that all data does not need to be transmitted during the audit phase. In the meanwhile, the auditor can verify the integrity of multiple ADS-B documents at once, which improves the computational efficiency of the audit. We analyze the integrity and security of the scheme and proved that DOPE is indistinguishable under an ordered chosen-plaintext attack (IND-OCPA). Furthermore, we conclude through performance analysis that the communication overhead is constant and computation overhead is logarithmic level. The proposed scheme is applicable to all data arranged in order, such as hospital records arranged by date and so on. At the same time, ADS-B can be used for urban vehicle monitoring and is a basic means to realize smart transportation.

The integration of advanced information, communication and data analytic technologies has transformed the traditional grid into an intelligent bidirectional system that can automatically adapt its services for utilities or consumers' needs. However, this change raises new privacy-related challenges. Privacy leakage has become a severe issue in the grid paradigm as adversaries run malicious analytics to identify the system's internal insight or use it to interrupt grids' operation by identifying real-time demand-based supply patterns. As a result, current grid authorities require an integrated mechanism to improve the system's sensitive data's privacy preservation. To this end, we present a multilayered smart grid architecture by characterizing the privacy issues that occur during data sharing, aggregation, and publishing by individual grid end nodes. Based on it, we quantify the nodes preferred privacy requirements. We further introduce personalized differential privacy (PDP) scheme based on trust distance in our proposed framework to provide the system with the added benefit of a user-specific privacy guarantee to eliminate differential privacy's limitation that allows the same level of privacy for all data providers. Lastly, we conduct extensive experimental analysis on a real-world grid dataset to illustrate that our proposed method is efficient enough to provide privacy preservation on sensitive smart grid data.

The Covid-19 Pandemic has caused huge losses worldwide and is still affecting people all around the world. Even after rigorous, incessant and dedicated efforts from people all around the world, it keeps mutating and spreading at an alarming rate. In times such as these, it is extremely important to take proper precautionary measures to stay safe and help to contain the spread of the virus. In this paper, we propose an innovative design of one such commonly used public disinfection method, an Automatic Walkthrough Sanitization Tunnel. It is a walkthrough sanitization tunnel which uses sensors to detect the target and automatically disinfects it followed by irradiation using UV-C rays for extra protection. There is a proposition to add an IoT based Temperature sensor and data relay module used to detect the temperature of any person entering the tunnel and in case of any anomaly, contact nearby covid wards to facilitate rapid treatment.

Internet of Things (IoT) is flourishing in several application areas, such as smart cities, smart factories, smart homes, smart healthcare, etc. With the adoption of IoT in critical scenarios, it is crucial to investigate its security aspects. All the layers of IoT are vulnerable to severely disruptive attacks. However, the attacks in IoT Network layer have a high impact on communication between the connected objects. Routing in most of the IoT networks is carried out by IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). RPL-based IoT offers limited protection against routing attacks. A trust-based approach for routing security is suitable to be integrated with IoT systems due to the resource-constrained nature of devices. This research proposes a trust-based secure routing protocol to provide security against packet dropping attacks in RPL-based IoT networks. IoT networks are dynamic and consist of both static and mobile nodes. Hence the chosen trust metrics in the proposed method also include the mobility-based metrics for trust evaluation. The proposed solution is integrated into RPL as a modified objective function, and the results are compared with the default RPL objective function, MRHOF. The analysis and evaluation of the proposed protocol indicate its efficacy and adaptability in a mobile IoT environment.

Due to the critical importance of Industrial Control Systems (ICS) to the operations of cities and countries, research into the security of critical infrastructure has become increasingly relevant and necessary. As a component of both the research and application sides of smart city development, accurate and precise modeling, simulation, and verification are key parts of a robust design and development tools that provide critical assistance in the prevention, detection, and recovery from abnormal behavior in the sensors, controllers, and actuators which make up a modern ICS system. However, while these tools have potential, there is currently a need for helper-tools to assist with their setup and configuration, if they are to be utilized widely. Existing state-of-the-art tools are often technically complex and difficult to customize for any given IoT/ICS processes. This is a serious barrier to entry for most technicians, engineers, researchers, and smart city planners, while slowing down the critical aspects of safety and security verification. To remedy this issue, we take a case study of existing simulation toolkits within the field of water management and expand on existing tools and algorithms with simplistic automated retrieval functionality using a much more in-depth and usable customization interface to accelerate simulation scenario design and implementation, allowing for customization of the cyber-physical network infrastructure and cyber attack scenarios. We additionally provide a novel in-tool-assessment of network’s resilience according to graph theory path diversity. Further, we lay out a roadmap for future development and application of the proposed tool, including expansions on resiliency and potential vulnerability model checking, and discuss applications of our work to other fields relevant to the design and operation of smart cities.

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is \$O(\textbackslashtextbackslashsqrt n )\$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.

To improve the security and reliability of the system in case of sensor failure, a fault diagnosis algorithm based on neural network is proposed to locate the fault quickly and reconstruct the control system in this paper. Firstly, the typical airborne sensors are introduced and their common failure modes are analyzed. Then, a new method of complex feature extraction using wavelet packet is put forward to extract the fault characteristics of UAV sensors. Finally, the observer method based on BP neural network is adopted to train and acquire data offline, and to detect and process single or multiple sensor faults online. Matlab simulation results show that the algorithm has good diagnostic accuracy and strong generalization ability, which also has certain practicability in engineering.

The transportation sector is undergoing rapid changes to reduce pollution and increase life quality in urban areas. One of the most effective approaches is flexible car rental and sharing to reduce traffic congestion and parking space issues. In this paper, we envision a flexible car sharing framework where vehicle owners want to make their vehicles available for flexible rental to other users. The owners delegate the management of their vehicles to intermediate services under certain policies, such as municipalities or authorized services, which manage the due infrastructure and services that can be accessed by users. We investigate the design of an accountable solution that allow vehicles owners, who want to share their vehicles securely under certain usage policies, to control that delegated services and users comply with the policies. While monitoring users behavior, our approach also takes care of users privacy, preventing tracking or profiling procedures by other parties. Existing approaches put high trust assumptions on users and third parties, do not consider users' privacy requirements, or have limitations in terms of flexibility or applicability. We propose an accountable protocol that extends standard delegated authorizations and integrate it with Security Credential Management Systems (SCMS), while considering the requirements and constraints of vehicular networks. We show that the proposed approach represents a practical approach to guarantee accountability in realistic scenarios with acceptable overhead.

As a result of the importance of privacy at present, especially with the modern applications and technologies that have spread in the last decade, many techniques and methods have appeared to preserve privacy and protect users' data from tracking, profiling, or identification. The most popular of these technologies are those which rely on peer-to-peer or third-party cooperation. But, by reviewing a significant portion of existing research articles related to privacy, we find considerable confusion amongst several concepts and ways of protection, such as the concept of cloak area, Anonymizer, cooperation, and Third Party Peers (TTP). In this research, we revisit and review these approaches, which contain an overlap between them to distinguish each one clearly with the help of graphs and to remove their ambiguity. In this way, we shall be able provide a ready-reckoner to those interested in this field to easily differentiate between them and thus work to develop them and provide new methods. In other words, this research seeks to enhance the privacy and security in smart applications and technologies in the IoT and smart city environments.

Both the sophistication and scale of cyberattacks are increasing, revealing the extent of risks at which critical infrastructure and other information and communication systems are exposed. Furthermore, the introduction of IoT devices in a number of different applications, ranging from home automation to the monitoring of critical infrastructure, has created an even more complicated cybersecurity landscape. A large amount of research has been done on detecting these attacks in real time, however mitigation is left to security experts, which is time consuming and may have economic consequences. In addition, there is no public data available for action selection that could enable the use of the latest techniques in machine learning or deep learning for this area. Currently, most systems deploy a rule-based response selection methodology for mitigating detected attacks. In this paper, we introduce a situation calculus-based approach to automated response for IoT cyberattacks. The approach offers explicit semantic-rich cognitive modeling of attacks, effects and actions and supports situation inference for timely and accurate responses. We demonstrate the effectiveness of our approach for modelling and responding to cyberattacks by implementing a use case in a real-world IoT scenario.