Biblio

JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency. Our paper explores the deployment of JPEG-XS on lossy packet networks. To preserve low latency, Forward Error Correction (FEC) is envisioned as the protection mechanism of interest. Although the JPEG-XS codestream is not scalable in essence, we observe that the loss of a codestream fraction impacts the decoded image quality differently, depending on whether this codestream fraction corresponds to codestream headers, to coefficient significance information, or to low/high frequency data. Hence, we propose a rate-distortion optimal unequal error protection scheme that adapts the redundancy level of Reed-Solomon codes according to the rate of channel losses and the type of information protected by the code. Our experiments demonstrate that, at 5% loss rates, it reduces the Mean Squared Error by up to 92% and 65%, compared to a transmission without and with optimal but equal protection, respectively.

Nowadays, cloud computing has become one of the most important and easily available storage options. This paper represents providing a platform where the data redundancy and the data security is maintained. Materials and Methods: This study contains two groups, the elliptic curve cryptography is developed in group 1 with 480 samples and advanced encryption is developed in group 2 with 960 samples. The accuracy of each of the methods is compared for different sample sizes with G power value as 0.8. Result: Advanced elliptic curve cryptography algorithm provides 1.2 times better performance compared to conventional elliptic curve cryptography algorithm for various datasets. The results were obtained with a significance value of 0.447 (p\textgreater0.05). Conclusion: From the obtained results the advanced elliptic curve cryptography algorithm seems to be better than the conventional algorithm.

This paper examines how the extent of the network has expanded from the traditional computer Internet to the field of edge computing based on mobile communication technology with the in-depth development of the mobile Internet and the Internet of Things. In particular, the introduction of 5G has enabled massive edge computing nodes to build a high-performance, energy-efficient and low-latency mobile edge computing architecture. Traditional network security technologies and methods are not fully applicable in this environment. The focus of this paper is on security protection for edge networks. Using virtualized networks builds a dynamic heterogeneous redundancy security model (i.e., DHR). It first designs and evaluates the DHR security model, then constructs the required virtualized heterogeneous entity set, and finally constructs a DHR-based active defense scheme. Compared with existing network security solutions, the security protection technology of the edge network studied this time has a better protective effect against the unknown security threats facing the edge network.

Today, social communication through the Internet has become more popular and has become a crucial part of our daily life. Naturally, sending and receiving various data through the Internet has also grown a lot. Keeping important data secure in transit has become a challenge for individuals and even organizations. Therefore, the trinity of confidentiality, integrity, and availability will be essential, and encryption will definitely be one of the best solutions to this problem. Of course, for image data, it will not be possible to use conventional encryption methods for various reasons, such as the redundancy of image data, the strong correlation of adj acent pixels, and the large volume of image data. Therefore, special methods were developed for image encryption. Among the prevalent methods for image encryption is the use of DNA sequences as well as chaos signals. In this paper, a cycling 3D chaotic map and DNA sequences are used to present a new method for color image encryption. Several experimental analyses were performed on the proposed method, and the results proved that the presented method is secure and efficient.

A critical property of distributed data processing systems is the high level of reliability of such systems. A practical solution to this problem is to place copies of archives of magnetic media in the nodes of the system. These archives are used to restore data destroyed during the processing of requests to this data. The paper shows the impact of the use of archives on the reliability indicators of distributed systems.

Markov models of reliability of fault-tolerant computer systems are proposed, taking into account two stages of recovery of redundant memory devices. At the first stage, the physical recovery of memory devices is implemented, and at the second, the informational one consists in entering the data necessary to perform the required functions. Memory redundancy is carried out to increase the stability of the system to the loss of unique data generated during the operation of the system. Data replication is implemented in all functional memory devices. Information recovery is carried out using replicas of data stored in working memory devices. The model takes into account the criticality of the system to the timeliness of calculations in real time and to the impossibility of restoring information after multiple memory failures, leading to the loss of all stored replicas of unique data. The system readiness coefficient and the probability of its transition to a non-recoverable state are determined. The readiness of the system for the timely execution of requests is evaluated, taking into account the influence of the shares of the distribution of the performance of the computer allocated for the maintenance of requests and for the entry of information into memory after its physical recovery.

The emergence of CPSs leads to modernization of critical infrastructures and improving flexibility and efficiency from one point of view. However, from another point of view, this modernization has subjected them to cyber threats. This paper provides a modeling approach for evaluating the security of CPSs. The main idea behind the presented model is to study the attacker and the system behaviors in the penetration and attack phases with exploiting some defensive countermeasures such as redundant components and attack detection strategies. By using the proposed approach, we can investigate how redundancy factor of sensors, controllers and actuators and intrusion detection systems can improve the system security and delay the system security failure.

We study a game-theoretic model of the interactions between a Cyber-Physical System’s (CPS) operator (the defender) against an attacker who launches stepping-stone attacks to reach critical assets within the CPS. We consider that, in addition to optimally allocating its security budget to protect the assets, the defender may choose to modify the CPS through network design interventions. In particular, we propose and motivate four ways in which the defender can introduce additional nodes in the CPS: these nodes may be intended as additional safeguards, be added for functional or structural redundancies, or introduce additional functionalities in the system. We analyze the security implications of each of these design interventions, and evaluate their impacts on the security of an automotive network as our case study. We motivate the choice of the attack graph for this case study and elaborate how the parameters in the resulting security game are selected using the CVSS metrics and the ISO-26262 ASIL ratings as guidance. We then use numerical experiments to verify and evaluate how our proposed network interventions may be used to guide improvements in automotive security.

Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.

Biometrics as a means of personal authentication has demonstrated strong viability in the past decade. However, directly deriving a unique cryptographic key from biometric data is a non-trivial task due to the fact that biometric data is usually noisy and presents large intra-class variations. Moreover, biometric data is permanently associated with the user, which leads to security and privacy issues. Cancellable biometrics and bio-cryptosystem are two main branches to address those issues, yet both approaches fall short in terms of accuracy performance, security, and privacy. In this paper, we propose a Bio-Crypto system on fingerprint Cancellable template (Bio-CanCrypto), which bridges cancellable biometrics and bio-cryptosystem to achieve a middle-ground for alleviating the limitations of both. Specifically, a cancellable transformation is applied on a fixed-length fingerprint feature vector to generate cancellable templates. Next, an LDPC coding mechanism is introduced into a reusable fuzzy extractor scheme and used to extract the stable cryptographic key from the generated cancellable templates. The proposed system can achieve both cancellability and reusability in one scheme. Experiments are conducted on a public fingerprint dataset, i.e., FVC2002. The results demonstrate that the proposed LDPC coded reusable fuzzy extractor is effective and promising.

Ternary content addressable memories (TCAMs) widely utilized in network systems to enforce the labeling of packets. For example, they are used for packet forwarding, security, and software-defined networks (SDNs). TCAMs are typically deployed as standalone instruments or as an embedded intellectual property component on application-specific integrated circuits. However, field-programmable gate arrays (FPGAs) do not have TCAM bases. However, FPGAs’ versatility allows them to appeal for SDN deployment, and most FPGA vendors have SDN production kits. Those need to help TCAM features and then simulate TCAMs using the FPGA logic blocks. Several methods to reproduction TCAMs on FPGAs have been introduced in recent years. Some of them use a huge multiple storage blocks within modern FPGAs to incorporate TCAMs. A trouble while remembrances are that soft errors that corrupt stored bits can affect them. Memories may be covered by a parity test to identify errors or by an error correction code, although this involves extra bits in a word frame. This brief considers memory security used to simulate TCAMs. It is shown in particular that by leveraging the assumption its part of potential memory information is true, most single-bit errors can be resolved when memoirs are emulated with a parity bit.

Recently, the informatics infrastructure of INRiM Time and Frequency Laboratory has been completely renewed with particular attention to network security and software architecture aspects, with the aims to improve the reliability, robustness and automation of the overall set-up. This upgraded infrastructure has allowed, since January 2020, a fully automated generation and monitoring of the Italian time scale UTC(IT), based on dedicated software developed in-house [1]. We focus in this work on the network and software aspects of our set-up, which enable a robust and reliable automatic time scale generation with continuous monitoring and minimal human intervention.

The semaphore mechanism is an important part of the embedded operating system. Therefore, it is very necessary to ensure its safety. Traditional software testing methods are difficult to ensure 100% coverage of the program. Therefore, it is necessary to adopt a formal verfication method which proves the correctness of the program theoretically. This paper proposes a proof framework based on the theorem proof tool Coq: modeling the semaphore mechanism, extracting important properties from the requirement documents, and finally verifying that the semaphore mechanism can meet these properties, which means the correctness of the semaphore mechanism is proved and also illustrates the feasibility of the verification framework proposed in this paper, which lays a foundation for the verification of other modules of operating systems.

In the last decade the use of artificial neural networks (ANNs) in many fields like image processing or speech recognition has become a common practice because of their effectiveness to solve complex tasks. However, in such a rush, very little attention has been paid to security aspects. In this work we explore the possibility to embed a watermark into the ANN parameters. We exploit model redundancy and adaptation capacity to lock a subset of its parameters to carry the watermark sequence. The watermark can be extracted in a simple way to claim copyright on models but can be very easily attacked with model fine-tuning. To tackle this culprit we devise a novel watermark aware training strategy. We aim at delving into the loss landscape to find an optimal configuration of the parameters such that we are robust to fine-tuning attacks towards the watermarked parameters. Our experimental results on classical ANN models trained on well-known MNIST and CIFAR-10 datasets show that the proposed approach makes the embedded watermark robust to fine-tuning and compression attacks.

In this paper, we propose a zero-distortion blind reversible robust scheme for watermarking relational databases called PKMark. Data owner can declare the copyright of the databases or pursue the infringement by extracting the water-mark information embedded in the database. PKMark is mainly based on the primary key attribute of the tuple. So it does not depend on the type of the attribute, and can provide high-precision numerical attributes. PKMark uses RSA encryption on the watermark before embedding the watermark to ensure the security of the watermark information. Then we use RSA to sign the watermark cipher text so that the owner can verify the ownership of the watermark without disclosing the watermark. The watermark embedding and extraction are based on the hash value of the primary key, so the scheme has blindness and reversibility. In other words, the user can obtain the watermark information or restore the original database without comparing it to the original database. Our scheme also has almost excellent robustness against addition attacks, deletion attacks and alteration attacks. In addition, PKMark is resistant to additive attacks, allowing different users to embed multiple watermarks without interfering with each other, and it can indicate the sequence of watermark embedding so as to indicate the original copyright owner of the database. This watermarking scheme also allows data owners to detect whether the data has been tampered with.

Cloud computing has changed the paradigm of using computing resources. It has shifted from traditional storage and computing to Internet based computing leveraging economy of scale, cost saving, elimination of data redundancy, scalability, availability and regulatory compliance. With these, cloud also brings plenty of security issues. As security is not a one-time solution, there have been efforts to investigate and provide countermeasures. In the wake of emerging quantum computers, the aim of post-quantum cryptography is to develop cryptography schemes that are secure against both classical computers and quantum computers. Since cloud is widely used across the globe for outsourcing data, it is essential to strive at providing betterment of security schemes from time to time. This paper reviews recent development, methods of cloud data security in post-quantum perspectives. It provides useful insights pertaining to the security schemes used to safeguard data dynamics associated with cloud computing. The findings of this paper gives directions for further research in pursuit of more secure cloud data storage and retrieval.

Polar codes have been shown to provide an effective mechanism for achieving physical-layer security over various wiretap channels. A majority of these schemes require channel state information (CSI) at the encoder for both intended receivers and eavesdroppers. In this paper, we consider a polar coding scheme for secrecy over a Gaussian wiretap channel when no CSI is available. We show that the availability of a shared keystream between friendly parties allows polar codes to be used for both secure and reliable communications, even when the eavesdropper knows a large fraction of the keystream. The scheme relies on a predetermined strategy for partitioning the bits to be encoded into a set of frozen bits and a set of information bits. The frozen bits are filled with bits from the keystream, and we evaluate the security gap when the cyclic redundancy check-aided successive cancellation list decoder is used at both receivers in the wiretap channel model.

With current Traditional / Legacy networks, the reliance on manual intervention to solve a variety of issues be it primary operational functionalities like addressing Link-failure or other consequent complexities arising out of existing solutions for challenges like Link-flapping or facing attacks like DDoS attacks is substantial. This physical and manual approach towards network configurations to make significant changes result in very slow updates and increased probability of errors and are not sufficient to address and support the rapidly shifting workload of the networks due to the fact that networking decisions are left to the hands of physical networking devices. With the advent of Software Defined Networking (SDN) which abstracts the network functionality planes, separating it from physical hardware – and decoupling the data plane from the control plane, it is able to provide a degree of automation for the network resources and management of the services provided by the network. This paper explores some of the aspects of automation provided by SDN capabilities in a Mesh Network (provides Network Security with redundancy of communication links) which contribute towards making the network inherently intelligent and take decisions without manual intervention and thus take a step towards Intelligent Automated Networks.

Existing compression coding technologies are investigated using a statistical approach. The fundamental strategies used in the process of statistical coding of video information data are analyzed. Factors that have a significant impact on the reliability and efficiency of video delivery in the process of statistical coding are analyzed. A model for estimating the impact of errors in the process of reconstruction of uneven code structures on the quality of recoverable video images is being developed.The influence of errors that occur in data transmission channels on the reliability of the reconstructed video image is investigated.

Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect them to be fast, transparent, cheap, reliable and global. Recent technologies such as distributed ledgers create opportunities for near-real-time, cheaper and more transparent payments. However, in order to achieve a global payment system, payments should be possible not only within one ledger, but also across different ledgers and geographies.In this paper we propose Secure Payments with Overlay Networks (SPON), a service that enables global payments across multiple ledgers by combining the transaction exchange provided by the Interledger protocol with an intrusion-tolerant overlay of relay nodes to achieve (1) improved payment latency, (2) fault-tolerance to benign failures such as node failures and network partitions, and (3) resilience to BGP hijacking attacks. We discuss the design goals and present an implementation based on the Interledger protocol and Spines overlay network. We analyze the resilience of SPON and demonstrate through experimental evaluation that it is able to improve payment latency, recover from path outages, withstand network partition attacks, and disseminate payments fairly across multiple ledgers. We also show how SPON can be deployed to make the communication between different ledgers resilient to BGP hijacking attacks.

After the emergence of the cloud architecture, many companies migrate their data from conventional storage i.e., on bare metal to the cloud storage. Since then huge amount of data was stored on cloud servers, which later resulted in redundancy of huge amount of data. Hence in this cloud world, many data de-duplication techniques has been widely used. Not only the redundancy but also made data more secure and privacy of the existing data were also increased. Some techniques got limitations and some have their own advantages based on the requirements. Some of the attributes like data privacy, tag regularity and interruption to brute-force attacks. To make data deduplication technique more efficient based on the requirements. This paper will discuss schemes that brace user-defined access control, by allowing the service provider to get information of the information owners. Thus our scheme eliminates redundancy of the data without breaching the privacy and security of clients that depends on service providers. Our lastest deduplication scheme after performing various algorithms resulted in conclusion and producing more efficient data confidentiality and tag consistency. This paper has discussion on various techniques and their drawbacks for the effectiveness of the deduplication.

Aiming at the problems of imperfect dynamic verification of power grid security and stability control strategy and high test cost, a reliability test method of power grid security control system based on BP neural network and dynamic group simulation is proposed. Firstly, the fault simulation results of real-time digital simulation system (RTDS) software are taken as the data source, and the dynamic test data are obtained with the help of the existing dispatching data network, wireless virtual private network, global positioning system and other communication resources; Secondly, the important test items are selected through the minimum redundancy maximum correlation algorithm, and the test items are used to form a feature set, and then the BP neural network model is used to predict the test results. Finally, the dynamic remote test platform is tested by the dynamic whole group simulation of the security and stability control system. Compared with the traditional test methods, the proposed method reduces the test cost by more than 50%. Experimental results show that the proposed method can effectively complete the reliability test of power grid security control system based on dynamic group simulation, and reduce the test cost.

The Digital Signature Algorithm (DSA) is a representative of a family of digital signature algorithms that are known to have a number of subliminal channels for covert data transmission. The capacity of these channels stretches from several bits (narrowband channels) to about 256 or so bits (a broadband channel). There are a couple of methods described in the literature to prevent the usage of the broadband channel with the help of a warden. In the present paper, we discuss some weaknesses of the known methods and suggest a solution that is free of the weaknesses and eliminates the broadband covert channel. Our solution also requires a warden who does not participate in signature generation and is able to check any signed message for the absence of the covert communication.

Companies like Netflix increasingly use the cloud to deploy their business processes. Those processes often involve partnerships with other companies, and can be modeled as workflows where the owner of the data at risk interacts with contractors to realize a sequence of tasks on the data to be secured.In practice, access control is an essential building block to deploy these secured workflows. This component is generally managed by administrators using high-level policies meant to represent the requirements and restrictions put on the workflow. Handling access control with a high-level scheme comes with the benefit of separating the problem of specification, i.e. defining the desired behavior of the system, from the problem of implementation, i.e. enforcing this desired behavior. However, translating such high-level policies into a deployed implementation can be error-prone.Even though semi-automatic and automatic tools have been proposed to assist this translation, policy verification remains highly challenging in practice. In this paper, our aim is to define and propose structures assisting the checking and correction of potential errors introduced on the ground due to a faulty translation or corrupted deployments. In particular, we investigate structures with formal foundations able to naturally model policies. Metagraphs, a generalized graph theoretic structure, fulfill those requirements: their usage enables to compare high-level policies to their implementation. In practice, we consider Rego, a language used by companies like Netflix and Plex for their release process, as a valuable representative of most common policy languages. We propose a suite of tools transforming and checking policies as metagraphs, and use them in a global framework to show how policy verification can be achieved with such structures. Finally, we evaluate the performance of our verification method.

Autonomous cyber-physical systems (CPS) are susceptible to non-invasive physical attacks such as sensor spoofing attacks that are beyond the classical cybersecurity domain. These attacks have motivated numerous research efforts on attack detection, but little attention on what to do after detecting an attack. The importance of attack recovery is emphasized by the need to mitigate the attack’s impact on a system and restore it to continue functioning. There are only a few works addressing attack recovery, but they all rely on prior knowledge of system dynamics. To overcome this limitation, we propose Recovery-by-Learning, a data-driven attack recovery framework that restores CPS from sensor attacks. The framework leverages natural redundancy among heterogeneous sensors and historical data for attack recovery. Specially, the framework consists of two major components: state predictor and data checkpointer. First, the predictor is triggered to estimate systems states after the detection of an attack. We propose a deep learning-based prediction model that exploits the temporal correlation among heterogeneous sensors. Second, the checkpointer executes when no attack is detected. We propose a double sliding window based checkpointing protocol to remove compromised data and keep trustful data as input to the state predictor. Third, we implement and evaluate the effectiveness of our framework using a realistic data set and a ground vehicle simulator. The results show that our method restores a system to continue functioning in presence of sensor attacks.