Biblio

In this paper, an image encryption scheme based on dynamic DNA sequence and two dimension logistic map is proposed. Firstly two different pseudo random sequences are generated using two dimension Sine-Henon alteration map. These sequences are used for altering the positions of each pixel of plain image row wise and column wise respectively. Secondly each pixels of distorted image and values of random sequences are converted into a DNA sequence dynamically using one dimension logistic map. Reversible DNA operations are applied between DNA converted pixel and random values. At last after decoding the results of DNA operations cipher image is obtained. Different theoretical analyses and experimental results proved the effectiveness of this algorithm. Large key space proved that it is possible to protect different types of attacks using our proposed encryption scheme.

The objective of this paper was to propose a 5D combined chaotic system used for image encryption by scrambling and DNA encryption. The initial chaotic values were calculated with a set of equations. The chaotic sequences were used for pixel scrambling, bit scrambling, DNA encryption and DNA complementary function. The average of NPCR, UACI and entropy values of the 6 images used for testing were 99.61, 33.51 and 7.997 respectively. The correlation values obtained for the encrypted image were much lower than the corresponding original image. The histogram of the encrypted image was flat. Based on the theoretical results from the tests performed on the proposed system it can be concluded that the system is suited for practical applications, since it offers high security.

In today's world, securing data is becoming one of the main issues, the elaboration of the fusion of cryptography and steganography are contemplating as the sphere of on-going research. This can be gain by cryptography, steganography, and fusion of these two, where message firstly encoding using any cryptography techniques and then conceal into any cover medium using steganography techniques. Biological structure of DNA is used as the cover medium due to high storage capacity, simple encoding method, massive parallelism and randomness DNA cryptography can be used in identification card and tickets. Currently work in this field is still in the developmental stage and a lot of investigation is required to reach a fully-fledged stage. This paper provides a review of the existing method of DNA based cryptography

We consider a one-time digital signature scheme recently proposed by Persichetti and show that a successful key recovery attack can be mounted with limited complexity. The attack we propose exploits a single signature intercepted by the attacker, and relies on a statistical analysis performed over such a signature, followed by information set decoding. We assess the attack complexity and show that a full recovery of the secret key can be performed with a work factor that is far below the claimed security level. The efficiency of the attack is motivated by the sparsity of the signature, which leads to a significant information leakage about the secret key.

The paper deals with the design and principles of functioning of code-based schemes for formation and verification of electronic digital signature. Comparative studies of the effectiveness of the known CFS scheme and the proposed scheme have been carried out, as well as their possibilities, disadvantages and prospects for use in the post-quantum period.

Named Data Networking (NDN) is a new network architecture that has been projected as the future of internet architecture. Unlike the traditional internet approach which currently relies on client-server communication models to communicate each other, NDN relies on data as an entity. Hence the users only need the content and applications based on data naming, as there is no IP addresses needed. NDN is different than TCP/IP technology as NDN signs the data with Digital Signature to secure each data authenticity. Regarding huge number of uses on IP-based network, and the minimum number of NDN-based network implementation, the NDN-IP gateway are needed to map and forward the data from IP-based network to NDN-based network, and vice versa. These gateways are called Custom-Router Gateway in this study. The Custom-Router Gateway requires a new mechanism in conducting Digital Signature so that authenticity the data can be verified when it passes through the NDN-IP Custom-Router Gateway. This study propose a method to process the Digital Signature for the packet flows from IP-based network through NDN-based network. Future studies are needed to determine the impact of Digital Signature processing on the performance in forwarding the data from IP-based to NDN-based network and vice versa.

With the widespread use of location-based services and the development of localization systems, user's locations and even sensitive information can be easily accessed by some untrusted entities, which means privacy concerns should be taken seriously. In this paper, we propose a differential privacy framework to preserve users' location privacy and provide location-based services. We propose the metrics of location privacy, service quality and differential privacy to introduce a location privacy preserving mechanism, which can help users find the tradeoff or optimal strategy between location privacy and service quality. In addition, we design an adversary model to infer users' true locations, which can be used by application service providers to improve service quality. Finally, we present simulation results and analyze the performance of our proposed system.

Every so often Humans utilize non-verbal gestures (e.g. facial expressions) to express certain information or emotions. Moreover, countless face gestures are expressed throughout the day because of the capabilities possessed by humans. However, the channels of these expression/emotions can be through activities, postures, behaviors & facial expressions. Extensive research unveiled that there exists a strong relationship between the channels and emotions which has to be further investigated. An Automatic Facial Expression Recognition (AFER) framework has been proposed in this work that can predict or anticipate seven universal expressions. In order to evaluate the proposed approach, Frontal face Image Database also named as Japanese Female Facial Expression (JAFFE) is opted as input. This database is further processed with a frequency domain technique known as Discrete Cosine transform (DCT) and then classified using Artificial Neural Networks (ANN). So as to check the robustness of this novel strategy, the random trial of K-fold cross validation, leave one out and person independent methods is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.

An experiment and numerical simulations analyze low-speed OSC derived XPM-induced phase noise penalty in 100-Gbps WDM systems. WDM transmission performance exhibits signal bit-pattern dependence on OSC, which is due to deterioration in SD-FEC performance.

Quantum low probability of intercept transmits ciphertext in a way that prevents an eavesdropper possessing the decryption key from recovering the plaintext. It is capable of Gbps communication rates on optical fiber over metropolitan-area distances.

The paper introduces a method of efficient partial firmware update with several advantages compared to common methods. The amount of data to transfer for an update is reduced, the energetic efficiency is increased and as the method is designed for over the air update, the radio spectrum occupancy is decreased. Herein described approach uses Lua scripting interface to introduce updatable fragments of invokable native code. This requires a dedicated memory layout, which is herein introduced. This method allows not only to distribute patches for deployed systems, but also on demand add-ons. At the end, the security aspects of proposed firmware update system is discussed and its limitations are presented.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.

The general idea to achieve error detection and correction is to add some extra bit to an original message, in which the receiver can use to check the flexibility of the message which has been delivered, and to recover the noisy data. Turbo code is one of the forward error correction method, which is able to achieve the channel capacity, with nearer Shannon limit, encoding and decoding of text and images are performed. Methods and the working have been explained in this paper. The error has also introduced and detection and correction of errors have been achieved. Transmission will be secure it can secure the information by the theft.

The paper considers, proposes and describes possibilities and ways for application, design and implementation of energy efficient microprocessor based embedded systems for LED lighting control in the traffic. Using LED lighting technology and appropriate designed embedded systems it is possible to implement very efficient and smart systems for very wide range of applications in the traffic. This type of systems can be widely used in many places in the traffic where there is needed quality lighting and low energy consumption. Application of such systems enables to increase energy consumption efficiency, quality of lighting and security of traffic and to decrease total costs for the lighting. Way of design and use of such digital embedded system to effectively increase functionality and efficiency of lighting in the traffic is proposed and described. It is also proposed and described one practically designed and implemented simple and universal embedded system for LED lighting control for many applications in the traffic.

The paper presents the results of load testing of embedded hardware platforms for Internet of Things solutions. Analyzed the available hardware. The operating systems from different manufacturers were consolidated into a single classification, and for the two most popular, load testing was performed by an external and internal wireless network adapter. Developed its own software solution based on the Python programming language. The number of wireless subscribers ranged from 7 to 14. Experimental results will be useful in deploying wireless infrastructure for small commercial and scientific wireless networks.

Designing secure systems is a complex task, particularly for designers who are no security experts. Cyber security plays a key role in embedded systems, especially for the domain of the Internet of Things (IoT). IoT systems of this kind are becoming increasingly important in daily life as they simplify various tasks. They are usually small, either embedded into bigger systems or battery driven, and perform monitoring or one shot tasks. Thus, they are subject to manifold constraints in terms of performance, power consumption, chip area, etc. As they are continuously connected to the internet and utilize our private data to perform their tasks, they are interesting for potential attackers. Cyber security thus plays an important role for the design of an IoT system. As the usage of security measures usually increases both computation time, as well as power consumption, a conflict between these constraints must be solved. For the designers of such systems, balancing these constraints constitutes a highly complex task. In this paper we propose a novel approach for considering possible security attacks on embedded systems, simplifying the consideration of security requirements immediately at the start of the design process. We introduce a security aware design space exploration framework which based on an architectural, behavioral and security attack description, finds the optimal design for IoT systems. We also demonstrate the feasibility and the benefits of our framework based on a door access system use case.

For multiple embedded computers working together, the functional failure resulting from the underlying hardware or system crash will cause a sudden abort of applications. Different types of applications may have security requirements for data isolation and access control. Therefore, we propose a scheme to implement multi-level security domain dynamic management oriented embedded computers based on MILS architecture. Firstly, the scheme builds local security policy items and access control lists according to type, function and security level. After that, security domain of all applications is constructed to achieve the safety purpose that applications can perform migration cross partitions and cross platforms. Our experiments and analysis show that the proposed scheme is feasible and correct.

To solve the high-resolution three-dimensional (3D) microwave imaging is a challenging topic due to its inherent unmanageable computation. Recently, deep learning techniques that can fully explore the prior of meaningful pattern embodied in data have begun to show its intriguing merits in various areas of inverse problem. Motivated by this observation, we here present a deep-learning-inspired approach to the high-resolution 3D microwave imaging in the context of Generative Adversarial Network (GAN), termed as GANMI in this work. Simulation and experimental results have been provided to demonstrate that the proposed GANMI can remarkably outperform conventional methods in terms of both the image quality and computational time.

Person re-identification(Person Re-ID) means that images of a pedestrian from cameras in a surveillance camera network can be automatically retrieved based on one of this pedestrian's image from another camera. The appearance change of pedestrians under different cameras poses a huge challenge to person re-identification. Person re-identification systems based on deep learning can effectively extract the appearance features of pedestrians. In this paper, the feature enhancement experiment is conducted, and the result showed that the current person reidentification datasets are relatively small and cannot fully meet the need of deep training. Therefore, this paper studied the method of using generative adversarial network to extend the person re-identification datasets and proposed a label smoothing regularization for outliers with weight (LSROW) algorithm to make full use of the generated data, effectively improved the accuracy of person re-identification.

In this paper, we present a semi-supervised remote sensing change detection method based on graph model with Generative Adversarial Networks (GANs). Firstly, the multi-temporal remote sensing change detection problem is converted as a problem of semi-supervised learning on graph where a majority of unlabeled nodes and a few labeled nodes are contained. Then, GANs are adopted to generate samples in a competitive manner and help improve the classification accuracy. Finally, a binary change map is produced by classifying the unlabeled nodes to a certain class with the help of both the labeled nodes and the unlabeled nodes on graph. Experimental results carried on several very high resolution remote sensing image data sets demonstrate the effectiveness of our method.

Efficient monitoring of high speed computer networks operating with a 100 Gigabit per second (Gbps) data throughput requires a suitable hardware acceleration of its key components. We present a platform capable of automated designing of hash functions suitable for network flow hashing. The platform employs a multi-objective linear genetic programming developed for the hash function design. We evolved high-quality hash functions and implemented them in a field programmable gate array (FPGA). Several evolved hash functions were combined together in order to form a new reconfigurable hash function. The proposed reconfigurable design significantly reduces the area on a chip while the maximum operation frequency remains very close to the fastest hash functions. Properties of evolved hash functions were compared with the state-of-the-art hash functions in terms of the quality of hashing, area and operation frequency in the FPGA.

In order to study the application of improved image hashing algorithm in image tampering detection, based on compressed sensing and ring segmentation, a new image hashing technique is studied. The image hash algorithm based on compressed sensing and ring segmentation is proposed. First, the algorithm preprocesses the input image. Then, the ring segment is used to extract the set of pixels in each ring region. These aggregate data are separately performed compressed sensing measurements. Finally, the hash value is constructed by calculating the inner product of the measurement vector and the random vector. The results show that the algorithm has good perceived robustness, uniqueness and security. Finally, the ROC curve is used to analyze the classification performance. The comparison of ROC curves shows that the performance of the proposed algorithm is better than FM-CS, GF-LVQ and RT-DCT.