Biblio

This paper presents CaptchaGG, a model for recognizing linear graphical CAPTCHAs. As in the previous society, CAPTCHA is becoming more and more complex, but in some scenarios, complex CAPTCHA is not needed, and usually, linear graphical CAPTCHA can meet the corresponding functional scenarios, such as message boards of websites and registration of accounts with low security. The scheme is based on convolutional neural networks for feature extraction of CAPTCHAs, recurrent neural forests A neural network that is too complex will lead to problems such as difficulty in training and gradient disappearance, and too simple will lead to underfitting of the model. For the single problem of linear graphical CAPTCHA recognition, the model which has a simple architecture, extracting features by convolutional neural network, sequence modeling by recurrent neural network, and finally classification and recognition, can achieve an accuracy of 96% or more recognition at a lower complexity.

We show that a new type of dielectric cavity featuring deep sub-wavelength light confinement allows a significant speedup of all-optical signal processing functionalities, without compromising the energy efficiency. The effect is due to enhanced diffusion dynamics in an unconventional geometry.

This paper presents a case study for designing and implementing a secure communication protocol over a Controller Area Network (CAN). The CAN based protocol uses a hybrid encryption method on a relatively simple hardware / software environment. Moreover, the blockchain technology is proposed as a working solution to provide an extra secure level of the proposed system.

Automatic speech recognition (ASR) models are used widely in applications for voice navigation and voice control of domestic appliances. ASRs have been misused by attackers to generate malicious outputs by attacking the deep learning component within ASRs. To assess the security and robustnesss of ASRs, we propose techniques within our framework SPAT that generate blackbox (agnostic to the DNN) adversarial attacks that are portable across ASRs. This is in contrast to existing work that focuses on whitebox attacks that are time consuming and lack portability. Our techniques generate adversarial attacks that have no human audible difference by manipulating the input speech signal using a psychoacoustic model that maintains the audio perturbations below the thresholds of human perception. We propose a framework SPAT with three attack generation techniques based on the psychoacoustic concept and frame selection techniques to selectively target the attack. We evaluate portability and effectiveness of our techniques using three popular ASRs and two input audio datasets using the metrics- Word Error Rate (WER) of output transcription, Similarity to original audio, attack Success Rate on different ASRs and Detection score by a defense system. We found our adversarial attacks were portable across ASRs, not easily detected by a state-of the-art defense system, and had significant difference in output transcriptions while sounding similar to original audio.

FPGA bitstream protection schemes are often the first line of defense for secure hardware designs. In general, breaking the bitstream encryption would enable attackers to subvert the confidentiality and infringe on the IP. Or breaking the authenticity enables manipulating the design, e.g., inserting hardware Trojans. Since FPGAs see widespread use in our interconnected world, such attacks can lead to severe damages, including physical harm. Recently we [1] presented a surprising attack — Starbleed — on Xilinx 7-Series FPGAs, tricking an FPGA into acting as a decryption oracle. For their UltraScale(+) series, Xilinx independently upgraded the security features to AES-GCM, RSA signatures, and a periodic GHASH-based checksum to validate the bitstream during decryption. Hence, UltraScale(+) devices were considered not affected by Starbleed-like attacks [2], [1].We identified novel security weaknesses in Xilinx UltraScale(+) FPGAs if configured outside recommended settings. In particular, we present four attacks in this situation: two attacks on the AES encryption and novel GHASH-based checksum and two authentication downgrade attacks. As a major contribution, we show that the Starbleed attack is still possible within the UltraScale(+) series by developing an attack against the GHASH-based checksum. After describing and analyzing the attacks, we list the subtle configuration changes which can lead to security vulnerabilities and secure configurations not affected by our attacks. As Xilinx only recommends configurations not affected by our attacks, users should be largely secure. However, it is not unlikely that users employ settings outside the recommendations, given the rather large number of configuration options and the fact that Security Misconfiguration is among the leading top 10 OWASP security issues. We note that these security weaknesses shown in this paper had been unknown before.

As the IPv6 protocol has been rapidly developed and applied, the security of IPv6 networks has become the focus of academic and industrial attention. Despite the fact that the IPv6 protocol is designed with security in mind, due to insufficient defense measures of current firewalls and intrusion detection systems for IPv6 networks, the construction of covert channels using fields not defined or reserved in IPv6 protocols may compromise the information systems. By discussing the possibility of constructing storage covert channels within IPv6 protocol fields, 10 types of IPv6 covert channels are constructed with undefined and reserved fields, including the flow label field, the traffic class field of IPv6 header, the reserved fields of IPv6 extension headers and the code field of ICMPv6 header. An IPv6 covert channel detection method based on field matching (CC-Guard) is proposed, and a typical IPv6 network environment is built for testing. In comparison with existing detection tools, the experimental results show that the CC-Guard not only can detect more covert channels consisting of IPv6 extension headers and ICMPv6 headers, but also achieves real-time detection with a lower detection overhead.

Redactable blockchain allows modifiers or voting committees with modification privileges to edit the data on the chain. Among them, trapdoor holders in chameleon-based hash redactable blockchains can quickly compute hash collisions for arbitrary data without breaking the link of the hash-chain. However, chameleon-based hash redactable blockchain schemes have difficulty solving issues such as editing operations with different granularity or conflicts and auditing modifiers that abuse editing privileges. To address the above challenges, we propose a redactable blockchain with Cross-audit and Diversity Editing (CDEdit). The proposed scheme distributes subdivided transaction-level and block-level tokens to the matching modifier committee to weaken the influence of central power. A number of modifiers are unpredictably selected based on reputation value proportions and the mapping of the consistent hash ring to enable diversity editing operations, and resist Sybil attacks. Meanwhile, an adaptive cross-auditing protocol is proposed to adjust the roles of modifiers and auditors dynamically. This protocol imposes a reputation penalty on the modifiers of illegal edits and solves the problems of abuse of editing privileges and collusion attacks. In addition, We used ciphertext policy attribute-based encryption (CP-ABE) and chameleon hashes with ephemeral trapdoor (CHET) for data modification, and present a system steps and security analysis of CDEdit. Finally, the extensive comparisons and evaluations show that our scheme costs less time overhead than other schemes and is suitable for complex application scenarios, e.g. IoT data management.

The access control mechanism of most consortium blockchain is implemented through traditional Certificate Authority scheme based on trust chain and centralized key management such as PKI/CA at present. However, the uneven power distribution of CA nodes may cause problems with leakage of certificate keys, illegal issuance of certificates, malicious rejection of certificates issuance, manipulation of issuance logs and metadata, it could compromise the security and dependability of consortium blockchain. Therefore, this paper design and implement a Certificate Authority scheme based on trust ring model that can not only enhance the reliability of consortium blockchain, but also ensure high performance. Combined public key, transformation matrix and elliptic curve cryptography are applied to the scheme to generate and store keys in a cluster of CA nodes dispersedly and securely for consortium nodes. It greatly reduced the possibility of malicious behavior and key leakage. To achieve the immutability of logs and metadata, the scheme also utilized public blockchain and smart contract technology to organize the whole procedure of certificate issuance, the issuance logs and metadata for certificate validation are stored in public blockchain. Experimental results showed that the scheme can surmount the disadvantages of the traditional scheme while maintaining sufficiently good performance, including issuance speed and storage efficiency of certificates.

Spreading codes are the core of the spread spectrum transmission. In this paper, a novel channel-dependent code allocation procedure for enhancing security in multi-carrier code division multiple access (MC-CDMA) system is proposed and investigated over frequency-selective fading. The objective of the proposed technique is to assign the codes to every subcarrier of active/legitimate receivers (Rxs) based on their channel frequency response (CFR). By that, we ensure security for legitimate Rxs against eavesdropping while preserving mutual confidentiality between the legitimate Rxs themselves. To do so, two assigning modes; fixed assigning mode (FAM) and adaptive assigning mode (AAM), are exploited. The effect of the channel estimation error and the number of legitimate Rxs on the bit error rate (BER) performance is studied. The presented simulations show that AAM provides better security with a complexity trade-off compared to FAM. While the latter is more robust against the imperfection of channel estimation.

In the present era of the internet, image watermarking schemes are used to provide content authentication, security and reliability of various multimedia contents. In this paper image watermarking scheme which utilizes the properties of Integer Wavelet Transform (IWT), Schur decomposition and Singular value decomposition (SVD) based is proposed. In the suggested method, the cover image is subjected to a 3-level Integer wavelet transform (IWT), and the HH3 subband is subjected to Schur decomposition. In order to retrieve its singular values, the upper triangular matrix from the HH3 subband’s Schur decomposition is then subjected to SVD. The watermark image is first encrypted using a chaotic map, followed by the application of a 3-level IWT to the encrypted watermark and the usage of singular values of the LL-subband to embed by manipulating the singular values of the processed cover image. The proposed scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping) and noise (Gaussian, Salt & Pepper Noise). The suggested scheme provides strong robustness against numerous attacks and chaotic encryption provides security to watermark.

Physical Unclonable Functions (PUFs) are the secured hardware primitives to authenticate Integrated Circuits (ICs) from various unauthorized attacks. The secured key generation mechanism through PUFs is based on random Process Variations (PVs) inherited by the CMOS transistors. In this paper, we proposed a chaotic-based challenge generation mechanism to feed the arbiter PUFs. The chaotic property is introduced to increase the non-linearity in the arbitration mechanism thereby the uncertainty of the keys is attained. The chaotic sequences are easy to generate, difficult to intercept, and have the additional advantage of being in a large number Challenge-Response Pair (CRP) generation. The proposed design has a significant advantage in key generation with improved uniqueness and diffuseness of 47.33%, and 50.02% respectively. Moreover, the enhancement in the reliability of 96.14% and 95.13% range from −40C to 125C with 10% fluctuations in supply voltage states that it has prominent security assistance to the Internet of Things (IoT) enabled devices against malicious attacks.

Cloud computing is becoming a demanding technology due to its flexibility, sensibility and remote accessibility. Apart from these applications of cloud computing, privacy and security are two terms that pose a circumstantial discussion. Various authors have argued on this topic that cloud computing is more secure than other data sharing and storing methods. The conventional data storing system is a computer system or smartphone storage. The argument debate also states that cloud computing is vulnerable to enormous types of attacks which make it a more concerning technology. This current study has also tried to draw the circumstantial and controversial debate on the security and privacy system of cloud computing. Primary research has been conducted with 65 cloud computing experts to understand whether a cloud computing security technique is highly secure or not. An online survey has been conducted with them where they provided their opinions based on the security and privacy system of cloud computing. Findings showed that no particular technology is available which can provide maximum security. Although the respondents agreed that blockchain is a more secure cloud computing technology; however, the blockchain also has certain threats which need to be addressed. The study has found essential encryption systems that can be integrated to strengthen security; however, continuous improvement is required.

With the development of the digital development transformation of the power grid, the classification of power unstructured encrypted data is an important basis for data security protection. However, most studies focus on exact match classification or single-keyword fuzzy match classification. This paper proposes a fuzzy matching classification method for power unstructured encrypted data. The data owner generates an index vector based on the power unstructured file, and the data user generates a query vector by querying the file through the same process. The index and query vector are uploaded to the cloud server in encrypted form, and the cloud server calculates the relevance score and sorts it, and returns the classification result with the highest score to the user. This method realizes the multi-keyword fuzzy matching classification of unstructured encrypted data of electric power, and through the experimental simulation of a large number of data sets, the effect and feasibility of the method are proved.

Smart phones have become the preferred way for Chinese Internet users currently. The mobile phone traffic is large from the operating system. These traffic is mainly generated by the services. In the context of the universal encryption of the traffic, classification identification of mobile encryption services can effectively reduce the difficulty of analytical difficulty due to mobile terminals and operating system diversity, and can more accurately identify user access targets, and then enhance service quality and network security management. The existing mobile encryption service classification methods have two shortcomings in feature selection: First, the DL model is used as a black box, and the features of large dimensions are not distinguished as input of classification model, which resulting in sharp increase in calculation complexity, and the actual application is limited. Second, the existing feature selection method is insufficient to use the time and space associated information of traffic, resulting in less robustness and low accuracy of the classification. In this paper, we propose a feature enhancement method based on adjacent flow contextual features and evaluate the Apple encryption service traffic collected from the real world. Based on 5 DL classification models, the refined classification accuracy of Apple services is significantly improved. Our work can provide an effective solution for the fine management of mobile encryption services.

With the development of technology, mobile phones are an indispensable part of human life. Factors such as brand, internal memory, wifi, battery power, camera and availability of 4G are now modifying consumers' decisions on buying mobile phones. But people fail to link those factors with the price of mobile phones; in this case, this paper is aimed to figure out the problem by using machine learning algorithms like Support Vector Machine, Decision Tree, K Nearest Neighbors and Naive Bayes to train the mobile phone dataset before making predictions of the price level. We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall and F1 score. This not only helps customers have a better choice on the mobile phone but also gives advice to businesses selling mobile phones that the way to set reasonable prices with the different features they offer. This idea of predicting prices level will give support to customers to choose mobile phones wisely in the future. The result illustrates that among the 4 classifiers, SVM returns to the most desirable performance with 94.8% of accuracy, 97.3 of F1 score (without feature selection) and 95.5% of accuracy, 97.7% of F1 score (with feature selection).

The experimental results demonstrated that, With the development of cloud computing, more and more people use cloud computing to do all kinds of things. However, for cloud computing, the most important thing is to ensure the stability of user data and improve security at the same time. From an analysis of the experimental results, it can be found that Cloud computing makes extensive use of technical means such as computing virtualization, storage system virtualization and network system virtualization, abstracts the underlying physical facilities into external unified interfaces, maps several virtual networks with different topologies to the underlying infrastructure, and provides differentiated services for external users. By comparing and analyzing the experimental results, it is clear that virtualization technology will be the main way to solve cloud computing security. Virtualization technology introduces a virtual layer between software and hardware, provides an independent running environment for applications, shields the dynamics, distribution and differences of hardware platforms, supports the sharing and reuse of hardware resources, provides each user with an independent and isolated computer environment, and facilitates the efficient and dynamic management and maintenance of software and hardware resources of the whole system. Applying virtualization technology to cloud security reduces the hardware cost and management cost of "cloud security" enterprises to a certain extent, and improves the security of "cloud security" technology to a certain extent. This paper will outline the basic cloud computing security methods, and focus on the analysis of virtualization cloud security technology

With the popularization of cloud computing and the deepening of its application, more and more cloud block storage systems have been put into use. The performance optimization of cloud block storage systems has become an important challenge facing today, which is manifested in the reduction of system performance caused by the unbalanced resource load of cloud block storage systems. Accurately predicting the I/O load status of the cloud block storage system can effectively avoid the load imbalance problem. However, the cloud block storage system has the characteristics of frequent random reads and writes, and a large amount of I/O requests, which makes prediction difficult. Therefore, we propose a novel I/O load prediction method for XB-IOPS feature engineering. The feature engineering is designed according to the I/O request pattern, I/O size and I/O interference, and realizes the prediction of the actual load value at a certain moment in the future and the average load value in the continuous time interval in the future. Validated on a real dataset of Alibaba Cloud block storage system, the results show that the XB-IOPS feature engineering prediction model in this paper has better performance in Alibaba Cloud block storage devices where random I/O and small I/O dominate. The prediction performance is better, and the prediction time is shorter than other prediction models.

Traditional power consumption management systems are not showing enough reliability and thus, smart grid technology has been introduced to reduce the excess power wastages. In the context of smart grid systems, network communication is another term that is used for developing the network between the users and the load profiles. Cloud computing and clustering are also executed for efficient power management. Based on the facts, this research is going to identify wireless network communication systems to monitor and control smart grid power consumption. Primary survey-based research has been carried out with 62 individuals who worked in the smart grid system, tracked, monitored and controlled the power consumptions using WSN technology. The survey was conducted online where the respondents provided their opinions via a google survey form. The responses were collected and analyzed on Microsoft Excel. Results show that hybrid commuting of cloud and edge computing technology is more advantageous than individual computing. Respondents agreed that deep learning techniques will be more beneficial to analyze load profiles than machine learning techniques. Lastly, the study has explained the advantages and challenges of using smart grid network communication systems. Apart from the findings from primary research, secondary journal articles were also observed to emphasize the research findings.