Biblio

Privacy and security are the key challenges of wearable IoTs. Smart wearables are becoming popular choice of people because of their indispensable application in the field of clinical medication and medical care, wellbeing the executives, working environments, training, and logical examination. Currently, IoT is facing several challenges, such as- user unawareness, lack of efficient security protocols, vulnerable wireless communication and device management, and improper device management. The paper investigates a efficient audit of safety and protection issues involved in wearable IoT devices with the following structure, as- (i) Background of IoT systems and applications (ii) Security and privacy issues in IoT (iii) Popular wearable IoTs in demand (iv) Highlight the existing IoT security and privacy solutions, and (v) Approaches to secure the futuristic IoT based environment. Finally, this study summarized with security vulnerabilities in IoT, Countermeasures and existing security and privacy solutions, and futuristic smart wearables.

Recently, the frequent security incidents of the Internet of things make the wearable IOT health monitoring systems (WIHMS) face serious security threats. Aiming at the security requirements of WIHMS identity authentication, Q. Jiang proposed a lightweight device mutual identity authentication solution in 2019. The scheme has good security performance. However, we find that in Jiang’s scheme, in the authentication phase, the server CS needs at least 3 queries and 1 update of the database operation, which affects the overall performance of the system. For this reason, we propose a new device mutual authentication and key agreement protocol. In our protocol, the authentication server only needs to query the server database twice.

Recent work on intrusion-tolerance has shown that resilience to sophisticated network attacks requires system replicas to be deployed across at least three geographically distributed sites. While commodity data centers offer an attractive solution for hosting these sites due to low cost and management overhead, their use raises significant confidentiality concerns: system operators may not want private data or proprietary algorithms exposed to servers outside their direct control. We present a new model for Byzantine Fault Tolerant replicated systems that moves toward “intrusion tolerance as a service”. Under this model, application logic and data are only exposed to servers hosted on the system operator's premises. Additional offsite servers hosted in data centers can support the needed resilience without executing application logic or accessing unencrypted state. We have implemented this approach in the open-source Spire system, and our evaluation shows that the performance overhead of providing confidentiality can be less than 4% in terms of latency.

The interdisciplinary field of immersive learning research is scattered. Combining efforts for better exploration of this field from the different disciplines requires researchers to communicate and coordinate effectively. We call upon the community of immersive learning researchers for planting the Knowledge Tree of Immersive Learning Research, a proposal for a systematization effort for this field, combining both scholarly and practical knowledge, cultivating a robust and ever-growing knowledge base and methodological toolbox for immersive learning. This endeavor aims at promoting evidence-informed practice and guiding future research in the field. This paper contributes with the rationale for three objectives: 1) Developing common scientific terminology amidst the community of researchers; 2) Cultivating a common understanding of methodology, and 3) Advancing common use of theoretical approaches, frameworks, and models.

Blockchain is well known for its storage consistency, decentralization and tamper-proof, but the privacy disclosure and difficulty in auditing discourage the innovative application of blockchain technology. As compared to public blockchain and private blockchain, consortium blockchain is widely used across different industries and use cases due to its privacy-preserving ability, auditability and high transaction rate. However, the present co-audit of privacy-preserving data on consortium blockchain is inefficient. Private data is usually encrypted by a session key before being published on a consortium blockchain for privacy preservation. The session key is shared with transaction parties and auditors for their access. For decentralizing auditorial power, multiple auditors on the consortium blockchain jointly undertake the responsibility of auditing. The distribution of the session key to an auditor requires individually encrypting the session key with the public key of the auditor. The transaction initiator needs to be online when each auditor asks for the session key, and one encryption of the session key for each auditor consumes resources. This work proposes GAChain and applies group key agreement technology to efficiently co-audit privacy-preserving data on consortium blockchain. Multiple auditors on the consortium blockchain form a group and utilize the blockchain to generate a shared group encryption key and their respective group decryption keys. The session key is encrypted only once by the group encryption key and stored on the consortium blockchain together with the encrypted private data. Auditors then obtain the encrypted session key from the chain and decrypt it with their respective group decryption key for co-auditing. The group key generation is involved only when the group forms or group membership changes, which happens very infrequently on the consortium blockchain. We implement the prototype of GAChain based on Hyperledger Fabric framework. Our experimental studies demonstrate that GAChain improves the co-audit efficiency of transactions containing private data on Fabric, and its incurred overhead is moderate.

UPI (Unified Payments Interface) is a framework in India wherein customers can send payments to merchants from their smartphones. The framework consists of UPI servers that are connected to the banks at the sender and receiver ends. To send and receive payments, customers and merchants would have to first register themselves with UPI servers by executing a registration protocol using payment apps such as BHIM, PayTm, Google Pay, and PhonePe. Weaknesses were recently reported on these protocols that allow attackers to make money transfers on behalf of innocent customers and even empty their bank accounts. But the reported weaknesses were found after informal and manual analysis. However, as history has shown, formal analysis of cryptographic protocols often reveals flaws that could not be discovered with manual inspection. In this paper, we model UPI protocols in the pattern of traditional cryptographic protocols such that they can be rigorously studied and analyzed using formal methods. The modeling simplifies many of the complexities in the protocols, making it suitable to analyze and verify UPI protocols with popular analysis and verification tools such as the Constraint Solver, ProVerif and Tamarin. Our modeling could also be used as a general framework to analyze and verify many other financial payment protocols than just UPI protocols, giving it a broader applicability.

The industrial revolution 4.0 has become a challenge for various sectors in mastering information technology, one of which is the arts and culture sector. Cultural arts that are quite widely spread and developed in Indonesia are traditional masks. Traditional masks are one of the oldest and most beautiful cultures in Indonesia. However, with the development of the era to the digital world in the era of the industrial revolution 4.0, this beloved culture is fading due to the entry of foreign cultures and technological developments. Many young people who succeed the nation do not understand this cultural art, namely traditional masks. So those cultural arts such as traditional masks can still keep up with the development of digital technology in industry 4.0, we conduct research to use technology to preserve this traditional mask culture. The research uses the ADDIE method starting with Analyze, Design, Develop, Implement, and Evaluate. We took some examples of traditional masks such as Malangan masks, Cirebon masks, and Panji masks from several regions in Indonesia. This research implements marker-based Augmented reality technology and makes a traditional mask book that can be a means of augmented reality.

Existing digital identity management systems fail to deliver the desirable properties of control by the users of their own identity data, credibility of disclosed identity data, and network-level anonymity. The recently proposed Self-Sovereign Identity (SSI) approach promises to give users these properties. However, we argue that without addressing privacy at the network level, SSI systems cannot deliver on this promise. In this paper we present the design and analysis of our solution TCID, created in collaboration with the Dutch government. TCID is a system consisting of a set of components that together satisfy seven functional requirements to guarantee the desirable system properties. We show that the latency incurred by network-level anonymization in TCID is significantly larger than that of identity data disclosure protocols but is still low enough for practical situations. We conclude that current research on SSI is too narrowly focused on these data disclosure protocols.

The fog platform is very suitable for time and location sensitive applications. Compared with cloud computing, fog computing faces new security and privacy challenges. This paper integrates blockchain nodes with fog nodes, and uses multi-party secure computing (MPC) in smart contracts to realize privacy-protected fog computing. MPC technology realizes encrypted input and output, so that participants can only get the output value of their own function. It is impossible to know the input and output of other people, and privacy calculation is realized. At the same time, the blockchain can perform network-wide verification and consensus on the results calculated by the MPC under the chain. Ensure the reliability of the calculation results. Due to the integration of blockchain and fog nodes, access control and encryption are guaranteed, integrity and isolation are provided, and privacy-sensitive data is controlled. As more complex topological structures emerge, the entire chain of fog nodes must be trusted. This ensures the network security of distributed data storage and network topology, users and fog service providers. Finally, trusted fog computing with privacy protection is realized.

WebAssembly is an Increasingly popular lightweight binary instruction format, which can be efficiently embedded and sandboxed. Languages like C, C++, Rust, Go, and many others can be compiled into WebAssembly. This paper describes Twine, a WebAssembly trusted runtime designed to execute unmodified, language-independent applications. We leverage Intel SGX to build the runtime environment without dealing with language-specific, complex APIs. While SGX hardware provides secure execution within the processor, Twine provides a secure, sandboxed software runtime nested within an SGX enclave, featuring a WebAssembly system interface (WASI) for compatibility with unmodified WebAssembly applications. We evaluate Twine with a large set of general-purpose benchmarks and real-world applications. In particular, we used Twine to implement a secure, trusted version of SQLite, a well-known full-fledged embeddable database. We believe that such a trusted database would be a reasonable component to build many larger application services. Our evaluation shows that SQLite can be fully executed inside an SGX enclave via WebAssembly and existing system interface, with similar average performance overheads. We estimate that the performance penalties measured are largely compensated by the additional security guarantees and its full compatibility with standard WebAssembly. An indepth analysis of our results indicates that performance can be greatly improved by modifying some of the underlying libraries. We describe and implement one such modification in the paper, showing up to 4.1 × speedup. Twine is open-source, available at GitHub along with instructions to reproduce our experiments.

Cyber security is an important concern in critical infrastructures such as banking and financial organizations, where a number of malicious insiders are involved. These insiders may be existing employees / users present within the organization and causing harm by performing any malicious activity and are commonly known as insider threats. Existing insider threat detection (ITD) methods are based on statistical analysis, machine and deep learning approaches. They monitor and detect malicious user activity based on pre-built rules which fails to detect unforeseen threats. Also, some of these methods require explicit feature engineering which results in high false positives. Apart from this, some methods choose relatively insufficient features and are computationally expensive which affects the classifier's accuracy. Hence, in this paper, a user behaviour based ITD method is presented to overcome the above limitations. It is a conceptually simple and flexible approach based on augmented decision making and anomaly detection. It consists of bi-directional long short term memory (bi-LSTM) for efficient feature extraction. For the purpose of classifying users as "normal" or "malicious", a binary class support vector machine (SVM) is used. CMU-CERT v4.2 dataset is used for testing the proposed method. The performance is evaluated using the following parameters: Accuracy, Precision, Recall, F- Score and AUC-ROC. Test results show that the proposed method outperforms the existing methods.

In a beyond-5G (B5G) scenario, we consider a virtual private mobile network (VPMN), i.e., a set of user equipments (UEs) directly communicating in a device-to-device (D2D) fashion, and connected to the cellular network by multiple gateways. The purpose of the VPMN is to hide the position of the VPMN UEs to the mobile network operator (MNO). We investigate the design and performance of packet routing inside the VPMN. First, we note that the routing that maximizes the rate between the VPMN and the cellular network leads to an unbalanced use of the gateways by each UE. In turn, this reveals information on the location of the VPMN UEs. Therefore, we derive a routing algorithm that maximizes the VPMN rate, while imposing for each UE the same data rate at each gateway, thus hiding the location of the UE. We compare the performance of the resulting solution, assessing the location privacy achieved by the VPMN, and considering both the case of single hop and multihop in the transmissions from the UEs to the gateways.

Mobile cloud computing has suggested as a viable technology as a result of the fast growth of mobile applications and the emergence of the cloud computing idea. Mobile cloud computing incorporates cloud computing into the mobile environment and addresses challenges in mobile cloud computing applications like (processing capacity, battery storage capacity, privacy, and security). We discuss the enabling technologies and obstacles that we will face when we transition from mobile computing to mobile cloud computing to develop next-generation mobile cloud applications. This paper provides an overview of the processes and open concerns for mobility in mobile cloud computing for augmented reality service provisioning. This paper outlines the concept, system architecture, and taxonomy of virtualization technology, as well as research concerns related to virtualization security, and suggests future study fields. Furthermore, we highlight open challenges to provide light on the future of mobile cloud computing and future development.

The proliferation of malware attacks causes a hindrance to cybersecurity thus, posing a significant threat to our devices. The variety and number of both known as well as unknown malware makes it difficult to detect it. Research suggests that the ramifications of malware are only becoming worse with time and hence malware analysis becomes crucial. This paper proposes a visual malware classification technique to convert malware executables into their visual representations and obtain grayscale images of malicious files. These grayscale images are then used to classify malicious files into their respective malware families by passing them through deep convolutional neural networks (CNN). As part of deep CNN, we use various ImageNet models and compare their performance.

The automatic detection of vulnerabilities in Web applications using taint analysis is a hot topic. However, existing taint analysis methods for sanitizers identification are too simple to find available taint transmission chains effectively. These methods generally use pre-constructed dictionaries or simple keywords to identify, which usually suffer from large false positives and false negatives. No doubt, it will have a greater impact on the final result of the taint analysis. To solve that, we summarise and classify the commonly used sanitizers in Web applications and propose an identification method based on semantic analysis. Our method can accurately and completely identify the sanitizers in the target Web applications through static analysis. Specifically, we analyse the natural semantics and program semantics of existing sanitizers, use semantic analysis to find more in Web applications. Besides, we implemented the method prototype in PHP and achieved a vulnerability detection tool called VulChecker. Then, we experimented with some popular open-source CMS frameworks. The results show that Vulchecker can accurately identify more sanitizers. In terms of vulnerability detection, VulChecker also has a lower false positive rate and a higher detection rate than existing methods. Finally, we used VulChecker to analyse the latest PHP applications. We identified several new suspicious taint data propagation chains. Before the paper was completed, we have identified four unreported vulnerabilities. In general, these results show that our approach is highly effective in improving vulnerability detection based on taint analysis.

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.

Cyber-physical systems (CPS) data privacy protection during sharing, aggregating, and publishing is a challenging problem. Several privacy protection mechanisms have been developed in the literature to protect sensitive data from adversarial analysis and eliminate the risk of re-identifying the original properties of shared data. However, most of the existing solutions have drawbacks, such as (i) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (ii) ignoring data providers privacy preference, (iii) using uniform privacy protection which may create inadequate privacy for some provider while over-protecting others, and (iv) lack of a comprehensive privacy quantification model assuring data privacy-preservation. To address these issues, we propose a personalized privacy preference framework by characterizing and quantifying the CPS vulnerabilities as well as ensuring privacy. First, we introduce a Standard Vulnerability Profiling Library (SVPL) by arranging the nodes of an energy-CPS from maximum to minimum vulnerable based on their privacy loss. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that our privacy characterization and quantification model can attain better privacy preservation by eliminating the trade-off between privacy, utility, and risk of losing information.

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.

The advancement of Information and Communication Technologies has brought numerous facilities and benefits to society. In this environment, surrounded by technologies, data, and personal information, have become an essential and coveted tool for many sectors. In this scenario, where a large amount of data has been collected, stored, and shared, privacy concerns arise, especially when dealing with sensitive data such as health data. The information owner generally has no control over his information, which can bring serious consequences such as increases in health insurance prices or put the individual in an uncomfortable situation with disclosing his physical or mental health. While privacy regulations, like the General Data Protection Regulation (GDPR), make it clear that the information owner must have full control and management over their data, disparities have been observed in most systems and platforms. Therefore, they are often not able to give consent or have control and management over their data. For the users to exercise their right to privacy and have sufficient control over their data, they must know everything that happens to them, where their data is, and where they have been. It is necessary that the entire life cycle, from generation to deletion of data, is managed by its owner. To this end, this article presents an Information Chain of Custody Model based on Blockchain technology, which allows from the traceability of information to the offer of tools that will enable the effective management of data, offering total control to its owner. The result showed that the prototype was very useful in the traceability of the information. With that it became clear the technical feasibility of this research.

Anonymous Communication Networks (ACNs) are networks in which, beyond data confidentiality, also traffic flow confidentiality is provided. The most popular routing approach for ACNs also used in practice is Onion. Onion is based on multiple encryption wrapping combined with the proxy mechanism (relay nodes). However, it offers neither sender anonymity nor recipient anonymity in a global passive adversary model, simply because the adversary can observe (at the first relay node) the traffic coming from the sender, and (at the last relay node) the traffic delivered to the recipient. This may also cause a loss of relationship anonymity if timing attacks are performed. This paper presents Onion-Ring, a routing protocol that improves anonymity of Onion in the global adversary model, by achieving sender anonymity and recipient anonymity, and thus relationship anonymity.

Website phishing continues to persist as one of the most important security threats of the modern Internet era. A major concern has been that machine learning based approaches, which have been the cornerstones of deployed phishing detection solutions, have not been able to adapt to the evolving nature of the phishing attacks. To create updated machine learning models, the collection of a sufficient corpus of real-time phishing data has always been a challenging problem as most phishing websites are short-lived. In this work, for the first time, we address these important concerns and describe an adaptive phishing detection solution that is able to adapt to changes in phishing attacks. Our solution has two major contributions. First, our solution allows for multiple organizations to collaborate in a privacy preserving manner and generate a robust machine learning model for phishing detection. Second, our solution is designed to be flexible in order to adapt to the novel phishing features introduced by attackers. Our solution not only allows for incorporating novel features into the existing machine learning model, but also can help, to a certain extent, the “unlearning” of existing features that have become obsolete in current phishing attacks. We evaluated our approach on a large real-world data collected over a period of six months. Our results achieve a high true positive rate of 97 %, which is on par with existing state-of-the art centralized solutions. Importantly, our results demonstrate that, a machine learning model can incorporate new features while selectively “unlearning” the older obsolete features.

The total number of photovoltaic power producing facilities whose FIT-based ten-year contract expires by 2023 is expected to reach approximately 1.65 million in Japan. If the number of renewable electricity-producing/consuming facilities reached two million, an enormous number of transactions would be invoked beyond blockchain's scalability.We propose mutually cooperative two novel methods to simultaneously solve scalability, data size, and privacy problems in blockchain-based trading platforms for renewable energy environmental value. One is a management scheme of electricity production resources (EPRs) using an extended UTXO token. The other is a data aggregation scheme that aggregates a significant number of smart meter records with evidentiality using zero-knowledge proof (ZKP).

This demo paper presents the technical details and usage scenarios of μSE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess the robustness of a given test-suite. However, we leverage this technique to systematically evaluate static analysis tools and uncover and document soundness issues.μSE's analysis has found 25 previously undocumented flaws in static data leak detection tools for Android.μSE offers four mutation schemes, namely Reachability, Complex-reachability, TaintSink, and ScopeSink, which determine the locations of seeded mutants. Furthermore, the user can extend μSE by customizing the API calls targeted by the mutation analysis.μSE is also practical, as it makes use of filtering techniques based on compilation and execution criteria that reduces the number of ineffective mutations.

Edge computing supports the deployment of ubiquitous, smart services by providing computing and storage closer to terminal devices. However, ensuring the full security and privacy of computations performed at the edge is challenging due to resource limitation. This paper responds to this challenge and proposes an adaptive approach to defense randomization among the edge data centers via a stochastic game, whose solution corresponds to the optimal security deployment at the network's edge. Moreover, security risk is evaluated subjectively based on Prospect Theory to reflect realistic scenarios where the attacker and the edge system do not similarly perceive the status of the infrastructure. The results show that a non-deterministic defense policy yields better security compared to a static defense strategy.

Along with the advance of science and technology, informationization society construction is gradually perfect. The development of modern information technology has driven the growth of the entire network spatial data, and network security is a matter of national security. There are several countries included in the national security strategy, with the increase of network space connected point, traditional network security space processing way already cannot adapt to the demand. Machine learning can effectively solve the problem of network security. Around the machine learning technology applied in the field of network security research results, this paper introduces the basic concept of network security situational awareness system, the basic model, and system framework. Based on machine learning, this paper elaborates the network security situation awareness technology, including data mining technology, feature extraction technology and situation prediction technology. Recursive feature elimination, decision tree algorithm, support vector machine, and future research direction in the field of network security situational awareness are also discussed.