Biblio

{1 We present a construction for exact-repair regenerating codes with an information-theoretic secrecy guarantee against an eavesdropper with access to the content of (up to) ℓ nodes. The proposed construction works for the entire range of per-node storage and repair bandwidth for any distributed storage system with parameters (n

In a linear code, a code symbol with (r, δ)-locality can be repaired by accessing at most r other code symbols in case of at most δ - 1 erasures. A q-ary (n, k, r, δ) locally repairable codes (LRC) in which every code symbol has (r, δ)-locality is said to be optimal if it achieves the Singleton-like bound derived by Prakash et al.. In this paper, we study the classification of optimal ternary (n, k, r, δ)-LRCs (δ \textbackslashtextgreater 2). Firstly, we propose an upper bound on the minimum distance of optimal q-ary LRCs in terms of the field size. Then, we completely determine all the 6 classes of possible parameters with which optimal ternary (n, k, r, δ)-LRCs exist. Moreover, explicit constructions of all these 6 classes of optimal ternary LRCs are proposed in the paper.

Wireless technology has seen a tremendous growth in the recent past. Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme has been utilized in almost all the advanced wireless techniques because of the advantages it offers. Hence in this aspect, SystemVue based OFDM transceiver has been developed with AWGN as the channel noise. To mitigate the channel noise Convolutional code with Viterbi decoder has been depicted. Further to protect the information from the malicious users the data is scrambled with the aid of gold codes. The performance of the transceiver is analysed through various Bit Error Rate (BER) versus Signal to Noise Ratio (SNR) graphs.

With an increasing number of wireless devices, the risk of being eavesdropped increases as well. From information theory, it is well known that wiretap codes can asymptotically achieve vanishing decoding error probability at the legitimate receiver while also achieving vanishing leakage to eavesdroppers. However, under finite blocklength, there exists a tradeoff among different parameters of the transmission. In this work, we propose a flexible wiretap code design for Gaussian wiretap channels under finite blocklength by neural network autoencoders. We show that the proposed scheme has higher flexibility in terms of the error rate and leakage tradeoff, compared to the traditional codes.

In the deep learning tasks, we can design different network models to address different tasks (classification, detection, segmentation). But traditional deep learning networks simply increase the depth and breadth of the network. This leads to a higher complexity of the model. We propose Adaptively Weighted Channel Feature Network of Mixed Convolution Kernel(SKENet). SKENet extract features from different kernels, then mixed those features by elementwise, lastly do sigmoid operator on channel features to get adaptive weightings. We did a simple classification test on the CIFAR10 amd CIFAR100 dataset. The results show that SKENet can achieve a better result in a shorter time. After that, we did an object detection experiment on the VOC dataset. The experimental results show that SKENet is far ahead of the SKNet[20] in terms of speed and accuracy.

To solve the problems of weak linkage ability and low intellectualization of strategy allocation in existing network security devices, a new method of cognitive linkage of network security equipment is proposed by learning from human brain. Firstly, the basic connotation and cognitive cycle of cognitive linkage are expounded. Secondly, the main functions of cognitive linkage are clarified. Finally, the cognitive linkage system model is constructed, and the information process flow of cognitive linkage is described. Cognitive linkage of network security equipment provides a new way to effectively enhance the overall protection capability of network security equipment.

Biometrics manages the computerized acknowledgment of people dependent on natural and social attributes. The example acknowledgment framework perceives an individual by deciding the credibility of a particular conduct normal for person. The primary rule of biometric framework is recognizable proof and check. A biometric confirmation framework use fingerprints, face, hand geometry, iris, and voice, mark, and keystroke elements of a person to recognize an individual or to check a guaranteed character. Biometrics authentication is a form of identification and access control process which identify individuals in packs that are under reconnaissance. Biometric security system increase in the overall security and individuals no longer have to deal with lost ID Cards or forgotten passwords. It helps much organization to see everyone is at a certain time when something might have happened that needs reviewed. The current issues in biometric system with individuals and many organization facing are personal privacy, expensive, data's may be stolen.

IoT based technology are drastically accelerating the informationization development of the power grid system of China that consists of a huge number of power terminal devices interconnected by the network of electric power IoT. However, the networked power terminal equipment oriented cyberspace security has continually become a challenging problem as network attack is continually varying and evolving. In this paper, we concentrate on the security risk of power terminal equipment and their vulnerability based on ATP attack detection and defense. We first analyze the attack mechanism of APT security attack based on power terminal equipment. Based on the analysis of the security and attack of power IoT terminal device, an ontology-based knowledge representation method of power terminal device and its vulnerability is proposed.

The inforrmation age continues to transform the mechanics of integrating electric power devices and systems, from coordinated operations based purely on the physics of electric power engineering to an increasing blend of power with information and communication technology. Integrating electric system components is not just about attaching wires. It requires the connection of computer-based automation systems to associated sensing and communication equipment. The architectural impacts are significant. Well-considered and commonly held concepts, principles, and organizational structures continue to emerge to address the complexity of the integrated operational challenges that drive our society to expect more flexibility in configuring the electric power system, while simultaneously achieving greater efficiency, reliability, and resilience. Architectural concepts, such as modularity and composability, contribute to the creation of structures that enable the connection of power system equipment characterized by clearly defined interfaces consisting of physical and cyberlinks. The result of successful electric power system component connection is interoperation: the discipline that drives integration to be simple and reliable.

Anonymous Communication (AC) hides traffic patterns and protects message metadata from being leaked during message transmission. Many practical AC systems have been proposed aiming to reduce communication latency and support a large number of users. However, how to design AC systems which possess strong security property and at the same time achieve optimal performance (i.e., the lowest latency or highest horizontal scalability) has been a challenging problem. In this paper, we propose an ObliComm framework, which consists of six modular AC subroutines. We also present a strong security definition for AC, named oblivious communication, encompassing confidentiality, unobservability, and a new requirement sending-and-receiving operation hiding. The AC subroutines in ObliComm allow for modular construction of oblivious communication systems in different network topologies. All constructed systems satisfy oblivious communication definition and can be provably secure in the universal composability (UC) framework. Additionally, we model the relationship between the network topology and communication measurements by queuing theory, which enables the system's efficiency can be optimized and estimated by quantitative analysis and calculation. Through theoretical analyses and empirical experiments, we demonstrate the efficiency of our scheme and soundness of the queuing model.

The Energy Internet is an advanced smart grid solution to increase energy efficiency by jointly operating multiple energy resources via the Internet. However, such an increasing integration of energy resources requires secure and efficient communication in the Energy Internet. To address such a requirement, we propose a new secure key bootstrapping protocol to support the integration and operation of energy resources. By using a universal composability model that provides a strong security notion for designing and analyzing cryptographic protocols, we define an ideal functionality that supports several cryptographic primitives used in this paper. Furthermore, we provide an ideal functionality for key bootstrapping and secure communication, which allows exchanged session keys to be used for secure communication in an ideal manner. We propose the first secure key bootstrapping protocol that enables a user to verify the identities of other users before key bootstrapping. We also present a secure communication protocol for unicast and multicast communications. The ideal functionalities help in the design and analysis of the proposed protocols. We perform some experiments to validate the performance of our protocols, and the results show that our protocols are superior to the existing related protocols and are suitable for the Energy Internet. As a proof of concept, we apply our functionalities to a practical key bootstrapping protocol, namely generic bootstrapping architecture.

The prevalence of social botnets has increased public distrust of social media networks. Current methods exist for detecting bot activity on Twitter, Reddit, Facebook, and other social media platforms. Most of these detection methods rely upon observing user behavior for a period of time. Unfortunately, the behavior observation period allows time for a botnet to successfully propagate one or many posts before removal. In this paper, we model the post propagation patterns of normal users and social botnets. We prove that a botnet may exploit deterministic propagation actions to elevate a post even with a small botnet population. We propose a probabilistic model which can limit the impact of social media botnets until they can be detected and removed. While our approach maintains expected results for non-coordinated activity, coordinated botnets will be detected before propagation with high probability.

Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.

Over the last decade, a technology called Internet of Things (IoT) has been evolving at a rapid pace. It enables the development of endless applications in view of availability of affordable components which provide smart ecosystems. The IoT devices are constrained devices which are connected to the internet and perform sensing tasks. Each device is identified by their unique address and also makes use of the Constrained Application Protocol (CoAP) as one of the main web transfer protocols. It is an application layer protocol which does not maintain secure channels to transfer information. For authentication and end-to-end security, Datagram Transport Layer Security (DTLS) is one of the possible approaches to boost the security aspect of CoAP, in addition to which there are many suggested ways to protect the transmission of sensitive information. CoAP uses DTLS as a secure protocol and UDP as a transfer protocol. Therefore, the attacks on UDP or DTLS could be assigned as a CoAP attack. An attack on DTLS could possibly be launched in a single session and a strong authentication mechanism is needed. Man-In-The-Middle attack is one the peak security issues in CoAP as cited by Request For Comments(RFC) 7252, which encompasses attacks like Sniffing, Spoofing, Denial of Service (DoS), Hijacking, Cross-Protocol attacks and other attacks including Replay attacks and Relay attacks. In this work, a client-server architecture is setup, whose end devices communicate using CoAP. Also, a proxy system was installed across the client side to launch an active interception between the client and the server. The work will further be enhanced to provide solutions to mitigate these attacks.

The accuracy of the image classification by the convolutional neural network is exceeding the ability of human being and contributes to various fields. However, the improvement of the image recognition technology gives a great blow to security system with an image such as CAPTCHA. In particular, since the character string CAPTCHA has already added distortion and noise in order not to be read by the computer, it becomes a problem that the human readability is lowered. Adversarial examples is a technique to produce an image letting an image classification by the machine learning be wrong intentionally. The best feature of this technique is that when human beings compare the original image with the adversarial examples, they cannot understand the difference on appearance. However, Adversarial examples that is created with conventional FGSM cannot completely misclassify strong nonlinear networks like CNN. Osadchy et al. have researched to apply this adversarial examples to CAPTCHA and attempted to let CNN misclassify them. However, they could not let CNN misclassify character images. In this research, we propose a method to apply FGSM to the character string CAPTCHAs and to let CNN misclassified them.

We present a security analysis of several gesture CAPTCHA challenges designed to operate on mobiles. Mobile gesture CAPTCHA challenges utilize the accelerometer and the gyroscope inputs from a mobile to allow a human to solve a simple test by physically manipulating the device. We have evaluated the security of gesture CAPTCHA in mobile devices and found them resistant to a range of common automated attacks. Our study has shown that using an accelerometer and the gyroscope readings as an input to solve the CAPTCHA is difficult for malware, but easy for a real user. Gesture CAPTCHA is effective in differentiating between humans and machines.

A website appears in search results based on web indexing conducted by a search engine bot (e.g., a web crawler). Some webpages do not want to be found easily because they include sensitive information. There are several methods to prevent web crawlers from indexing in search engine database. However, such webpages can still be indexed by malicious web crawlers. Through this study, we explore a paradox perspective on a new use of captchas for search prevention. Captchas are used to prevent web crawlers from indexing by converting sensitive words to captchas. We have implemented the web-based captcha conversion tool based on our search prevention algorithm. We also describe our proof of concept with the web-based chat application modified to utilize our algorithm. We have conducted the experiment to evaluate our idea on Google search engine with two versions of webpages, one containing plain text and another containing sensitive words converted to captchas. The experiment results show that the sensitive words on the captcha version of the webpages are unable to be found by Google's search engine, while the plain text versions are.

CAPTCHA is a web-based authentication method used by websites to distinguish between humans (valid users) and bots (attackers). Audio captcha is an accessible captcha meant for the visually disabled section of users such as color-blind, blind, near-sighted users. Firstly, this paper analyzes how secure current audio captchas are from attacks using machine learning (ML) and deep learning (DL) models. Each audio captcha is made up of five, seven or ten random digits[0-9] spoken one after the other along with varying background noise throughout the length of the audio. If the ML or DL model is able to correctly identify all spoken digits and in the correct order of occurance in a single audio captcha, we consider that captcha to be broken and the attack to be successful. Throughout the paper, accuracy refers to the attack model's success at breaking audio captchas. The higher the attack accuracy, the more unsecure the audio captchas are. In our baseline experiments, we found that attack models could break audio captchas that had no background noise or medium background noise with any number of spoken digits with nearly 99% to 100% accuracy. Whereas, audio captchas with high background noise were relatively more secure with attack accuracy of 85%. Secondly, we propose that the concepts of adversarial examples algorithms can be used to create a new kind of audio captcha that is more resilient towards attacks. We found that even after retraining the models on the new adversarial audio data, the attack accuracy remained as low as 25% to 36% only. Lastly, we explore the benefits of creating adversarial audio captcha through different algorithms such as Basic Iterative Method (BIM) and deepFool. We found that as long as the attacker has less than 45% sample from each kinds of adversarial audio datasets, the defense will be successful at preventing attacks.

The Internet plays an increasingly important role in people's lives, but it also brings security problems. CAPTCHA, which stands for Completely Automated Public Turing Test to Tell Computers and Humans Apart, has been widely used as a security mechanism. This paper outlines the scientific and technological progress in both the design and attack of CAPTCHAs related to these three CAPTCHA categories. It first presents a comprehensive survey of recent developments for each CAPTCHA type in terms of usability, robustness and their weaknesses and strengths. Second, it summarizes the attack methods for each category. In addition, the differences between the three CAPTCHA categories and the attack methods will also be discussed. Lastly, this paper provides suggestions for future research and proposes some problems worthy of further study.

We present novel CAPTCHA challenges based on user gestures designed for mobile. A gesture CAPTCHA challenge is a security mechanism to prevent malware from gaining access to network resources from mobile. Mobile devices contain a number of sensors that record the physical movement of the device. We utilized the accelerometer and gyroscope data as inputs to our novel CAPTCHAs to capture the physical manipulation of the device. We conducted an experimental study on a group of people. We discovered that younger people are able to solve this type of CAPTCHA challenges successfully in a short amount of time. We found that using accelerometer readings produces issues for some older people.

Ze the quality of channels into either completely noisy or noieseless channels. This paper presents extrinsic information transfer (EXIT) analysis for iterative decoding of Polar codes to reveal the mechanism of channel transformation. The purpose of understanding the transformation process are to comprehend the placement process of information bit and frozen bit and to comprehend the security standard of Polar codes. Mutual information derived based on the concept of EXIT chart for check nodes and variable nodes of low density parity check (LDPC) codes and applied to Polar codes. This paper explores the quality of the polarized channels in finite blocklength. The finite block-length is of our interest since in the fifth telecommunications generation (5G) the block length is limited. This paper reveals the EXIT curve changes of Polar codes and explores the polarization characteristics, thus, high value of mutual informations for frozen bit are needed to be detectable. If it is the other way, the error correction capability of Polar codes would be drastically decreases. These results are expected to be a reference for developments of Polar codes for 5G technologies and beyond.

In practical wireless communication systems, the channel conditions of legitimate users can not always be better than those of eavesdroppers. This realistic fact brings the challenge for the design of secure transmission over wiretap channels which requires that the eavesdropping channel conditions should be worse than legitimate channels. In this paper, we present a robust chaos-based information masking polar coding scheme for enhancing reliability and security performances under realistic channel conditions for practical systems. In our design, we mask the original information, wherein the masking matrix is determined by chaotic sequences. Then the masked information is encoded by the secure polar coding scheme. After the channel polarization achieved by the polar coding, we could identify the bit-channels providing good transmission conditions for legitimate users and the bit-channels with bad conditions for eavesdroppers. Simulations are performed over the additive white Gaussian noise (AWGN) and slow flat-fading Rayleigh channels. The results demonstrate that compared with existing schemes, the proposed scheme can achieve better reliability and security even when the eavesdroppers have better channel conditions than legitimate users, hence the practicability is greatly enhanced.

A detailed model of an attack on the power grid involves both a preparation stage as well as an execution stage of the attack. This paper introduces a novel Hybrid Attack Model (HAM) that combines Probabilistic Learning Attacker, Dynamic Defender (PLADD) model and a Markov Chain model to simulate the planning and execution stages of a bad data injection attack in power grid. We discuss the advantages and limitations of the prior work models and of our proposed Hybrid Attack Model and show that HAM is more effective compared to individual PLADD or Markov Chain models.

Near Field Communication (NFC) is a short- range wireless communication technology that supports several features, one of which is an electronic payment. NFC works at a limited distance to exchange information. In terms of security, NFC technology has a gap for attackers to carry out attacks by forwarding information illegally using the target NFC network. A relay attack that occurs due to the theft of some data by an attacker by utilizing close communication from NFC is one of them. Relay attacks can cause a lot of loss in terms of material sacrifice. It takes countermeasures to overcome the problem of electronic payments with NFC technology. Detection of anomalous data is one way that can be done. In an attack, several abnormalities can be detected which can be used to prevent an attack. Markov Chain is one method that can be used to detect relay attacks that occur in electronic payments using NFC. The result shows Markov chain can detect anomalies in relay attacks in the case of electronic payment.

Bluetooth enables excellent mobility in Brain Computer Interface (BCI) research and other use cases including ambulatory care, telemedicine, fitness tracking and mindfulness training. Although significant research exists for an all-encompassing BCI performance rating, almost all the literature addresses performance in terms of brain state or brain function classification accuracy. For the few published experiments that address BCI hardware performance, they too, focused on improving classification accuracy. This paper explores some of the more recent studies and proposes a trusted performance rating for BCI applications based on the enhanced privacy, yet reduced bandwidth needs of mobile EEG-based BCI applications. This paper proposes a set of Bluetooth operating parameters required to meet the performance, usability and privacy requirements of reliable and secure mobile neuro-feedback applications. It presents a rating model, "Trusted Mobile BCI", based on those operating parameters, and validated the model with studies that leveraged mobile BCI technology.