Biblio

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.

MIMO technology has been widely used in the telecommunication systems nowadays, and the space-time coding is a key part of MIMO technology. A good coding scheme can exploit the spatial diversity to correct the error which is generated in transmission, and increase the normalized transfer rate with low decoding complexity. On the Basis of the research on different Space-Time Block Codes, this essay proposes a new STBC, Diagonal Block Orthogonal Space-Time Block Code. Then we will compare it with other STBCs in the performance of bit error rate, transfer rate, decoding complexity and peek-to-average power ratio, the final result will prove the superiority of DBOAST.

We present an alternate-channel chaotic laser secure communication system to enhance information communication security and study its technical solution via combining chaos shift keying (CSK) and chaos masking (CM). Two coupled lasers and other two single lasers are introduced as a novel alternate-channel secure communication system, where one of two coupled lasers is modulated via CSK to encode a digital signal and the other of coupled lasers is used to emit a chaotic carrier to mask an information using CM. The two single lasers are used to decode CSK and CM information, respectively. And such CSK performance results in enhancement of CM secure performance because of in-time variation of the emitter' parameter as secret keys. The obtained numerical results show that the encoding and decoding can be successfully performed. The study is beneficial to chaotic cryptography and optics secure communication.

When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption’s computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods.

Trust is an important emerging area of study in human-robot cooperation. Many studies have begun to look at the issue of robot (agent) capability as a predictor of human trust in the robot. However, the assumption that agent capability is the sole predictor of human trust could underestimate the complexity of the problem. This study aims to investigate the effects of agent-strategy and agent-capability in a visual search task. Fourteen subjects were recruited to partake in a web-based grid search task. They were each paired with a series of autonomous agents to search an on-screen grid to find a number of outlier objects as quickly as possible. Both the human and agent searched the grid concurrently and the human was able to see the movement of the agent. Each trial, a different autonomous agent with its assigned capability, used one of three search strategies to assist their human counterpart. After each trial, the autonomous agent reported the number of outliers it found, and the human subject was asked to determine the total number of outliers in the area. Some autonomous agents reported only a fraction of the outliers they encountered, thus coding a varying level of agent capability. Human subjects then evaluated statements related to the behavior, reliability, and trust of the agent. The results showed increased measures of trust and reliability with increasing capability. Additionally, the most legible search strategies received the highest average ratings in a measure of familiarity. Remarkably, given no prior information about capabilities or strategies that they would see, subjects were able to determine consistent trustworthiness of the agent. Furthermore, both capability and strategy of the agent had statistically significant effects on the human’s trust in the agent.

Security patterns are proven solutions to recurring problems in software development. The growing importance of secure software development has introduced diverse research efforts on security patterns that mostly focused on classification schemes, evolution and evaluation of the patterns. Despite a huge mature history of research and popularity among researchers, security patterns have not fully penetrated software development practices. Besides, software security education has not been benefited by these patterns though a commonly stated motivation is the dissemination of expert knowledge and experience. This is because the patterns lack a simple embodiment to help students learn about vulnerable code, and to guide new developers on secure coding. In order to address this problem, we propose to conduct intelligent data mining in the context of software engineering to discover learner-friendly software security patterns. Our proposed model entails knowledge discovery from large scale published real-world vulnerability histories in software applications. We harness association rule mining for frequent pattern discovery to mine easily comprehensible and explainable learner-friendly rules, mainly of the type "flaw implies fix" and "attack type implies flaw", so as to enhance training in secure coding which in turn would augment secure software development. We propose to build a learner-friendly intelligent tutoring system (ITS) based on the newly discovered security patterns and rules explored. We present our proposed model based on association rule mining in secure software development with the goal of building this ITS. Our proposed model and prototype experiments are discussed in this paper along with challenges and ongoing work.

We present a scalable architecture based on a trained filter bank for input pre-processing and a recurrent neural network (RNN) for the detection of atrial fibrillation in electrocardiogram (ECG) signals, with the focus on enabling a very efficient hardware implementation as application-specific integrated circuit (ASIC). Our already very efficient base architecture is further improved by replacing the RNN with a delta-encoded gated recurrent unit (GRU) and adding a confidence measure (CM) for terminating the computation as early as possible. With these optimizations, we demonstrate a reduction of the processing load of 58 % on an internal dataset while still achieving near state-of-the-art classification results on the Physionet ECG dataset with only 1202 parameters.

Fulcrum decoders can trade off between computational complexity and the number of received packets. This allows heterogeneous nodes to decode at different level of complexity in accordance with their computing power. Variations of Fulcrum codes, like dynamic sparsity and expansion packets (DSEP) have significantly reduced the encoders and decoders' complexity by using dynamic sparsity and expansion packets. However, limited effort had been done for recoders of Fulcrum codes and their variations, limiting their full potential when being deployed at multi-hop networks. In this paper, we investigate the drawback of the conventional Fulcrum recoding and introduce a novel recoding scheme for the family of Fulcrum codes by limiting the buffer size, and thus memory needs. Our evaluations indicate that DSEP recoding mechamism increases the recoding goodput by 50%, and reduces the decoding overhead by 60%-90% while maintaining high decoding goodput at receivers and small memory usage at recoders compared with the conventional Fulcrum recoding. This further reduces the resources needed for Fulcrum codes at the recoders.

Aimed at the high requirement of timeliness in the process of information assurance, this paper describes the average time delay of information transmission in the system, and designs a timeliness index that can quantitatively describe the ability of early warning information assurance. In response to the problem that system capability cannot meet operational requirements due to enemy attacks, this paper analyzes the structure of the early warning information system, Early warning information complex network model is established, based on the timeliness index, a genetic algorithm based on simulated annealing with special chromosome coding is proposed.the algorithm is used to adjust the network model structure, the ability of early warning information assurance has been improved. Finally, the simulation results show the effectiveness of the proposed method.

A new method for judging degree sequence is shown by means of perfect ice-flower systems made by operators - stars (particular complete bipartite graphs), and moreover this method can be used to build up degree sequences and perfect ice-flower systems. Graphic lattice, graph-graphic lattice, caterpillar-graphic lattice and topological coding lattice are defined. We establish some connections between traditional lattices and graphic lattices trying to provide new techniques for Lattice-based cryptosystem and post-quantum cryptography, and trying to enrich the theoretical knowledge of topological coding.

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github11https://github.com/HeroadZ/KiL.

As more and more visible light communication (VLC) and visible light sensing (VLS) systems are mounted on today’s light fixtures, how to guarantee the authenticity of the visible light (VL) signal in these systems becomes an urgent problem. This is because almost all of today’s light fixtures are unprotected and can be openly accessed by almost anyone, and hence are subject to tampering and substitution attacks. In this paper, by exploiting the intrinsic linear superposition characteristics of visible light, we propose VL-Watchdog, a scalable and always-on signal-level spoofing detection framework that is applicable to both VLC and VLS systems. VL-Watchdog is based on redundant orthogonal encoding of the transmitted visible light, and can be implemented as a small hardware add-on to an existing VL system. The effectiveness of the proposed framework was validated through extensive numerical evaluations against a comprehensive set of factors.

There have been developed image encryption algorithm using chaotic map and DNA pseudo-symbols sequence gained on the basis of real DNA symbols. In the suggested algorithm, the address for the selecting of DNA symbols sequence from Gene Bank, encoding rule of the DNA symbols, also the initial parameters of the chaotic map are determined on the secret key basis. Image pixels modification is based on the numerical values of the chaotic points sets coordinates obtained with the chaos play description of the DNA pseudo-symbols and the transference of pixels is based on displacement table constructed with the chaotic map.

To address the concerns posed by certain security attacks on communication protocol, this paper proposes a Quantum Key Exchange algorithm coupled with an encoding scheme based on Fermat Numbers and DNA sequences. The concept of Watson-Crick’s transformation of DNA sequences and random property of the Fermat Numbers is applied for protection of the communication system by means of dual encryption. The key generation procedure is governed by a quantum bit rotation mechanism. The total process is illustrated with an example. Also, security analysis of the encryption and decryption process is also discussed.

Cloud computing has revolutionized the way how users store, process, and use data. It has evolved over the years to put forward various sophisticated models that offer enhanced performance. The growth of electronic data stored in the Cloud has made it crucial to access data without data loss and leakage. Security threats still prevent significant corporations that use sensitive data to employ cloud computing to handle their data. Traditional cryptographic techniques like DES, AES, etc... provide data confidentiality but are computationally complex. To overcome such complexities, a unique field of cryptography known as DNA Cryptography came into existence. DNA cryptography is a new field of cryptography that utilizes the chemical properties of DNA for secure data encoding. DNA cryptographic algorithms are much faster than traditional cryptographic methods and can bring about greater security with lesser computational costs. In this paper, we have proposed an enhanced DNA cryptosystem involving operations such as encryption, encoding table generation, and decryption based on the chemical properties of DNA. The performance analysis has proven that the proposed DNA cryptosystem is secure and efficient in Cloud data storage.

This paper aims to present a performance comparison of new authenticated encryption (AE) algorithm with the objective of high network security and better efficiency as compared to the defacto standard. This algorithm is based on a critical property of nonce-misuse-resistance incorporating DNA computation for securing the key, here the processing unit of DNA block converts the input key into its equivalent DNA base formats based on the ASCII code table. The need for secure exchange of keys through a public channel has become inevitable and thus, the proposed architecture will enhance the secrecy by using DNA cryptography. These implementations consider Advanced Encryption Standard in Galois Counter mode (AES-GCM) as a standard for comparison.

This paper proposes an image encryption technology based on six-dimensional hyperchaotic system and DNA encoding, in order to solve the problem of low security in existing image encryption algorithms. First of all, the pixel values of the R, G, and B channels are divided into blocks and zero-filled. Secondly, the chaotic sequence generated by the six-dimensional hyperchaotic system and logistic mapping is used for DNA coding and DNA operations. Third, the decoded three-channel pixel values are scrambled through diagonal traversal. Finally, merge the channels to generate a ciphertext image. According to simulation experiments and related performance analysis, the algorithm has high security performance, good encryption and decryption effects, and can effectively resist various common attack methods.

Cryptography is the science of encryption and decryption of data using the techniques of mathematics to achieve secure communication. This enables the user to send the data in an insecure channel. These channels are usually vulnerable to security attacks due to the data that they possess. A lot of work is being done these days to protect data and data communication. Hence securing them is the utmost concern. In recent times a lot of researchers have come up with different cryptographic techniques to protect the data over the network. One such technique used is DNA cryptography. The proposed approach employs a DNA sequencing-based encoding and decoding mechanism. The data is secured over the network using a secure authentication and key agreement procedure. A significant amount of work is done to show how DNA cryptography is secure when compared to other forms of cryptography techniques over the network.

IoT is still an emerging technology without a lot of standards around it, which makes it difficult to integrate it into existing businesses, what's more, with restricted assets and expanding gadgets that essentially work with touchy information. Thus, information safety has become urgent for coders and clients. Thus, painstakingly chosen and essentially tested encryption calculations should be utilized to grow the gadgets productively, to decrease the danger of leaking the delicate information. This investigation looks at the ECC calculation (Elliptic Curve Cryptography) and Rivest-Shamir-Adleman (RSA) calculation. Furthermore, adding the study of DNA encoding operation in DNA computing with ECC to avoid attackers from getting access to the valuable data.

With tons of efforts spent on its mitigation, Cross-site scripting (XSS) remains one of the most prevalent security threats on the internet. Decades of exploitation and remediation demonstrated that code inspection and testing alone does not eliminate XSS vulnerabilities in complex web applications with a high degree of confidence. This paper introduces Google's secure-by-design engineering paradigm that effectively prevents DOM-based XSS vulnerabilities in large-scale web development. Our approach, named API hardening, enforces a series of company-wide secure coding practices. We provide a set of secure APIs to replace native DOM APIs that are prone to XSS vulnerabilities. Through a combination of type contracts and appropriate validation and escaping, the secure APIs ensure that applications based thereon are free of XSS vulnerabilities. We deploy a simple yet capable compile-time checker to guarantee that developers exclusively use our hardened APIs to interact with the DOM. We make various of efforts to scale this approach to tens of thousands of engineers without significant productivity impact. By offering rigorous tooling and consultant support, we help developers adopt the secure coding practices as seamlessly as possible. We present empirical results showing how API hardening has helped reduce the occurrences of XSS vulnerabilities in Google's enormous code base over the course of two-year deployment.

In this paper, we propose a polar coding based scheme for set reconciliation between two network nodes. The system is modeled as a well-known Slepian-Wolf setting induced by a fixed number of deletions. The set reconciliation process is divided into two phases: 1) a deletion polar code is employed to help one node to identify the possible deletion indices, which may be larger than the number of genuine deletions; 2) a lossless compression polar code is then designed to feedback those indices with minimum overhead. Our scheme can be viewed as a generalization of polar codes to some emerging network-based applications such as the package synchronization in blockchains. The total overhead is linear to the number of packages, and immune to the package size.

Skyrmion racetrack memory (Sk-RM) is a new storage technology in which skyrmions are used to represent data bits to provide high storage density. During the reading procedure, the skyrmion is driven by a current and sensed by a fixed read head. However, synchronization errors may happen if the skyrmion does not pass the read head on time. In this paper, a polar coding scheme is proposed to correct the synchronization errors in the Sk-RM. Firstly, we build two error correction models for the reading operation of Sk-RM. By connecting polar codes with the marker codes, the number of deletion errors can be determined. We also redesign the decoding algorithm to recover the information bits from the readout sequence, where a tighter bound of the segmented deletion errors is derived and a novel parity check strategy is designed for better decoding performance. Simulation results show that the proposed coding scheme can efficiently improve the decoding performance.

Nowadays cloud computing has become the crucial part of IT and most important thing is information security in cloud environment. Range of users can access the facilities and use cloud according to their feasibility. Cloud computing is utilized as safe storage of information but still data security is the biggest concern, for example, secrecy, data accessibility, data integrity is considerable factor for cloud storage. Cloud service providers provide the facility to clients that they can store the data on cloud remotely and access whenever required. Due to this facility, it gets necessary to shield or cover information from unapproved access, hackers or any sort of alteration and malevolent conduct. It is inexpensive approach to store the valuable information and doesn't require any hardware and software to hold the data. it gives excellent work experience but main measure is just security. In this work security strategies have been proposed for cloud data protection, capable to overpower the shortcomings of conventional data protection algorithms and enhancing security using steganography algorithm, encryption decryption techniques, compression and file splitting technique. These techniques are utilized for effective results in data protection, Client can easily access our developed desktop application and share the information in an effective and secured way.

Given that an increasingly larger part of an organization's activity is taking place online, especially in the current situation caused by the COVID-19 pandemic, network log data collected by organizations contain an accurate image of daily activity patterns. In some scenarios, it may be useful to share such data with other parties in order to improve collaboration, or to address situations such as cyber-security incidents that may affect multiple organizations. However, in doing so, serious privacy concerns emerge. One can uncover a lot of sensitive information when analyzing an organization's network logs, ranging from confidential business interests to personal details of individual employees (e.g., medical conditions, political orientation, etc). Our objective is to enable organizations to share information about their network logs, while at the same time preserving data privacy. Specifically, we focus on enabling encrypted search at network flow granularity. We consider several state-of-the-art searchable encryption flavors for this purpose (including hidden vector encryption and inner product encryption), and we propose several customized encoding techniques for network flow information in order to reduce the overhead of applying state-of-the-art searchable encryption techniques, which are notoriously expensive.

Supervisory control and data acquisition (SCADA) networks provide high situational awareness and automation control for industrial control systems, whilst introducing a wide range of access points for cyber attackers. To address these issues, a line of machine learning or deep learning based intrusion detection systems (IDSs) have been presented in the literature, where a large number of attack examples are usually demanded. However, in real-world SCADA networks, attack examples are not always sufficient, having only a few shots in many cases. In this paper, we propose a novel few-shot learning based IDS, named FS-IDS, to detect cyber attacks against SCADA networks, especially when having only a few attack examples in the defenders’ hands. Specifically, a new method by orchestrating one-hot encoding and principal component analysis is developed, to preprocess SCADA datasets containing sufficient examples for frequent cyber attacks. Then, a few-shot learning based preliminary IDS model is designed and trained using the preprocessed data. Last, a complete FS-IDS model for SCADA networks is established by further training the preliminary IDS model with a few examples for cyber attacks of interest. The high effectiveness of the proposed FS-IDS, in detecting cyber attacks against SCADA networks with only a few examples, is demonstrated by extensive experiments on a real SCADA dataset.