Biblio

A QPSK-assisted MIMO equalization is investigated to compensate bandwidth limitation for 800-Gb/s/λ DP-256QAM systems with only 25G-class optics. Compared with conventional MIMO equalization, the proposed equalization scheme exhibits 1.8-dB OSNR improvement at 15% FEC limit.

Design for trust approaches can protect an MPSoC system from hardware Trojan attack due to the high penetration of third-party intellectual property. However, this incurs significant design cost by purchasing IP cores from various IP vendors, and the IP vendors providing particular IP are always limited, making these approaches unable to be performed in practice. This paper treats IP vendor as constraint, and tasks are scheduled with a minimized security constraint violations, furthermore, the area of MPSoC is also optimized during scheduling. Experimental results demonstrate the effectiveness of our proposed algorithm, by reducing 0.37% security constraint violations.

Microgrids are a promising alternative to the traditional distribution systems due to their highly desirable features, such as, reliability, resiliency, and efficiency. This paper covers the design, simulation, and economic analysis of a theoretically designed modern, mixed-use commercial and residential building on a feeder in Charleston, SC, USA. The designed system is simulated in PSCAD/EMTDC. The system combines a natural gas CHP turbine and generator block set, solar photovoltaics (PV), and a battery energy storage system (BESS). It is planned to provide power through a DC lighting bus and an AC to several different commercial load profiles as well as 40 apartments of varying sizes. Additionally, a comprehensive economic analysis is completed with available or estimated pricing to prove the feasibility of such a project.

Space-air-ground integrated network (SAGIN) is emerged as a versatile computing and traffic architecture in recent years. Though SAGIN brings many significant benefits for modern communication and computing services, there are many unprecedented challenges in SAGIN. The one critical challenge in SAGIN is the data security. In SAGIN, because the data will be stored in cleartext on cloud, the sensitive data may suffer from the illegal access by the unauthorized users even the untrusted cloud servers (CSs). Ciphertext-policy attribute-based encryption (CP-ABE), which is a type of attribute-based encryption (ABE), has been regarded as a promising solution to the critical challenge of the data security on cloud. But there are two main blemishes in traditional CP-ABE. The first one is that there is only one attribute authority (AA) in CP-ABE. If the single AA crashs down, the whole system will be shut down. The second one is that the AA cannot effectively manage the life cycle of the users’ private keys. If a user on longer has one attribute, the AA cannot revoke the user’s private key of this attribute. This means the user can still decrypt some ciphertexts using this invalid attribute. In this paper, to solve the two flaws mentioned above, we propose a multi-authority CP-ABE (MA-CP-ABE) scheme with the dynamical key revocation (DKR). Our key revocation supports both user revocation and attribute revocation. And the our revocation is time friendly. What’s more, by using our dynamically tag-based revocation algorithm, AAs can dynamically and directly re-enable or revoke the invalid attributes to users. Finally, by evaluating and implementing our scheme, we can observe that our scheme is more comprehensive and practical for cloud applications in SAGIN.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

Vulnerabilities in privileged software layers have been exploited with severe consequences. Recently, Trusted Execution Environments (TEEs) based technologies have emerged as a promising approach since they claim strong confidentiality and integrity guarantees regardless of the trustworthiness of the underlying system software. In this paper, we consider one of the most prominent TEE technologies, referred to as Intel Software Guard Extensions (SGX). Despite many formal approaches, there is still a lack of formal proof of some critical processes of Intel SGX, such as remote attestation. To fill this gap, we propose a fully automated, rigorous, and sound formal approach to specify and verify the Enhanced Privacy ID (EPID)-based remote attestation in Intel SGX under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. The evaluation indicates that the confidentiality of attestation keys is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX attestation during formal specification.

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.

With the development of the Internet of Things (IoT), it has been widely deployed. As many embedded devices are connected to the network and massive amounts of security-sensitive data are stored in these devices, embedded devices in IoT have become the target of attackers. The trusted computing is a key technology to guarantee the security and trustworthiness of devices' execution environment. This paper focuses on security problems on IoT devices, and proposes a security architecture for IoT devices based on the trusted computing technology. This paper implements a security management system for IoT devices, which can perform integrity measurement, real-time monitoring and security management for embedded applications, providing a safe and reliable execution environment and whitelist-based security protection for IoT devices. This paper also designs and implements an embedded security protection system based on trusted computing technology, containing a measurement and control component in the kernel and a remote graphical management interface for administrators. The kernel layer enforces the integrity measurement and control of the embedded application on the device. The graphical management interface communicates with the remote embedded device through the TCP/IP protocol, and provides a feature-rich and user-friendly interaction interface. It implements functions such as knowledge base scanning, whitelist management, log management, security policy management, and cryptographic algorithm performance testing.

Grassmannian frames in composite dimensions D are constructed as a collection of orthogonal bases where each is the element-wise product of a mask sequence with a generalized Hadamard matrix. The set of mask sequences is obtained by exponentiation of a q-root of unity by different quadratic forms with m variables, where q and m are the product of the unique primes and total number of primes, respectively, in the prime decomposition of D. This method is a generalization of a well-known construction of mutually unbiased bases, as well as second-order Reed-Muller Grassmannian frames for power-of-two dimension D = 2m, and allows to derive highly symmetric nested families of frames with finite alphabet. Explicit sets of symmetric matrices defining quadratic forms leading to constructions in non-prime-power dimension with good distance properties are identified.

Web application services have gradually become an important support of Internet services, but are also facing increasingly serious security problems. It is extremely necessary to evaluate the security of Web application services to deal with attacks against them effectively. In this paper, in view of the characteristics of the current attack of Web application services, a Web security analysis model based on the kill chain is established, and the possible attacks against Web application services are analyzed in depth from the perspective of the kill chain. Then, the security of Web application services is evaluated in a quantitative manner. In this way, it can make up the defects of insufficient inspection by the existing security vulnerability model and the security specification of the tracking of Web application services, so as to realize the objective and scientific evaluation of the security state of Web application services.

Due to improving computational capacity of supercomputers, transmitting encrypted packets via one single network path is vulnerable to brute-force attacks. The versatile attackers secretly eavesdrop all the packets, classify packets into different streams, performs an exhaustive search for the decryption key, and extract sensitive personal information from the streams. However, new Internet Protocol (IP) brings great opportunities and challenges for preventing eavesdropping attacks. In this paper, we propose a Programming Protocol-independent Packet Processors (P4) based Network Immune Scheme (P4NIS) against the eavesdropping attacks. Specifically, P4NIS is equipped with three lines of defense to improve the network immunity. The first line is promiscuous forwarding by splitting all the traffic packets in different network paths disorderly. Complementally, the second line encrypts transmission port fields of the packets using diverse encryption algorithms. The encryption could distribute traffic packets from one stream into different streams, and disturb eavesdroppers to classify them correctly. Besides, P4NIS inherits the advantages from the existing encryption-based countermeasures which is the third line of defense. Using a paradigm of programmable data planes-P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping significantly, and increase transmission throughput by 31.7% compared with state-of-the-art mechanisms.

In modern grid systems which is a typical cyber-physical System (CPS), information space and physical space are closely related. Once the communication link is interrupted, it will make a great damage to the power system. If the service path is too concentrated, the risk will be greatly increased. In order to solve this problem, this paper constructs a route planning algorithm that combines node load pressure, link load balance and service delay risk. At present, the existing intelligent algorithms are easy to fall into the local optimal value, so we chooses the deep reinforcement learning algorithm (DRL). Firstly, we build a risk assessment model. The node risk assessment index is established by using the node load pressure, and then the link risk assessment index is established by using the average service communication delay and link balance degree. The route planning problem is then solved by a route planning algorithm based on DRL. Finally, experiments are carried out in a simulation scenario of a power grid system. The results show that our method can find a lower risk path than the original Dijkstra algorithm and the Constraint-Dijkstra algorithm.

To build the fifth generation (5G) mobile network, the sharing structure in the 5G network adopted in industries has gained great research interesting. However, in this structure data are shared among diversity networks, which introduces the threaten of network security, such as covert timing channels. To eliminate the covert timing channel, we propose to inject noise into the covert timing channel. By analyzing the modulation method of covert timing channels, we design the jamming strategy on the covert channel. According to the strategy, the interference algorithm of the covert timing channel is designed. Since the interference algorithm depends heavily on the memory, we construct a distributing jammer. Experiments results show that these covert time channel can be blocked under the distributing jammer.

In the original algorithm for grey correlation analysis, the detected edge is comparatively rough and the thresholds need determining in advance. Thus, an adaptive edge detection method based on grey correlation analysis is proposed, in which the basic principle of the original algorithm for grey correlation analysis is used to get adaptively automatic threshold according to the mean value of the 3×3 area pixels around the detecting pixel and the property of people's vision. Because the false edge that the proposed algorithm detected is relatively large, the proposed algorithm is enhanced by dealing with the eight neighboring pixels around the edge pixel, which is merged to get the final edge map. The experimental results show that the algorithm can get more complete edge map with better continuity by comparing with the traditional edge detection algorithms.

This article describes the development of two mobile applications for learning Digital Electronics. The first application is an interactive app for iOS where you can study the different digital circuits, and which will serve as the basis for the second: a game of questions in augmented reality.

Deep Neural Networks (DNNs) are susceptible to model stealing attacks, which allows a data-limited adversary with no knowledge of the training dataset to clone the functionality of a target model, just by using black-box query access. Such attacks are typically carried out by querying the target model using inputs that are synthetically generated or sampled from a surrogate dataset to construct a labeled dataset. The adversary can use this labeled dataset to train a clone model, which achieves a classification accuracy comparable to that of the target model. We propose "Adaptive Misinformation" to defend against such model stealing attacks. We identify that all existing model stealing attacks invariably query the target model with Out-Of-Distribution (OOD) inputs. By selectively sending incorrect predictions for OOD queries, our defense substantially degrades the accuracy of the attacker's clone model (by up to 40%), while minimally impacting the accuracy (\textbackslashtextless; 0.5%) for benign users. Compared to existing defenses, our defense has a significantly better security vs accuracy trade-off and incurs minimal computational overhead.

The continuous progress of electronic equipments has influenced car manufacturers, leading to the integration of the latest infotainment technologies and providing connection to external devices, such as mobile phones. Modern cars work with ECUs (Electronic Control Units) that handle user interactions and sensor data, by also sending information to actuators using simple, reliable and efficient networks with fast protocols, like CAN (Controller Area Network). This is the most used vehicular protocol, which allows interconnecting different ECUs, making them interact in a synergic manner. On the down side, there is a security risk related to the exposition of malicious ECU's frames-possibly generated by compromised devices-which can lead to the possibility to remote control all the car equipments (like brakes and others) by an attacker. We propose a solution to this problem, designing an authentication and encryption system above CAN, called AuthentiCAN. Our proposal is tailored for the evolution of CAN called CAN-FD, and avoids the possibility for an attacker to inject malicious frames that are not discarded by the destination ECUs. Also, we avoid the possibility for an attacker to learn the interactions that occur across ECUs, with the objective of maliciously replaying messages-which would lead the actuator's logic to be no longer compliant with the actual data sources. We also present a simulation study of our solution, where we provide an assessment of its overhead, e.g. in terms of reduction of the throughput of data-unit transfer over CAN-FD, caused by the added security features.

Air-gapped networks achieve security by using the physical isolation to keep the computers and network from the Internet. However, magnetic covert channels based on CPU utilization have been proposed to help secret data to escape the Faraday-cage and the air-gap. Despite the success of such cover channels, they suffer from the high risk of being detected by the transmitter computer and the challenge of installing malware into such a computer. In this paper, we propose MagView, a distributed magnetic cover channel, where sensitive information is embedded in other data such as video and can be transmitted over the air-gapped internal network. When any computer uses the data such as playing the video, the sensitive information will leak through the magnetic covert channel. The "separation" of information embedding and leaking, combined with the fact that the covert channel can be created on any computer, overcomes these limitations. We demonstrate that CPU utilization for video decoding can be effectively controlled by changing the video frame type and reducing the quantization parameter without video quality degradation. We prototype MagView and achieve up to 8.9 bps throughput with BER as low as 0.0057. Experiments under different environment are conducted to show the robustness of MagView. Limitations and possible countermeasures are also discussed.

An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

As a collection of innovative technologies, blockchain has solved the problem of reliable transmission and exchange of information on untrusted networks. The underlying implementation is the basis for the reliability of blockchain, which consists of various cryptographic algorithms for the use of identity authentication and privacy protection of distributed ledgers. The cryptographic algorithm plays a vital role in the blockchain, which guarantees the confidentiality, integrity, verifiability and non-repudiation of the blockchain. In order to get the most suitable cryptographic algorithm for the blockchain system, this paper proposed a method using Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and score the comprehensive performance of the three types of cryptographic algorithms applied in the blockchain, including symmetric cryptographic algorithms, asymmetric cryptographic algorithms and hash algorithms. This paper weighs the performance differences of cryptographic algorithms considering the aspects of security, operational efficiency, language and hardware support and resource consumption. Finally, three cryptographic algorithms are selected that are considered to be the most suitable ones for block-chain systems, namely ECDSA, sha256 and AES. This result is also consistent with the most commonly used cryptographic algorithms in the current blockchain development direction. Therefore, the reliability and practicability of the algorithm evaluation pro-posed in this paper has been proved.

The fuzzy query scheme based on vector index uses Bloom filter to construct vector index for key words. Then the statistical attack based on the deviation of frequency distribution of the vector index brings out the sensitive information disclosure. Using the noise vector, a fuzzy query scheme resistant to the statistical attack serving for encrypted database, i.e. S-BF, is introduced. With the noise vector to clear up the deviation of frequency distribution of vector index, the statistical attacks to the vector index are resolved. Demonstrated by lab experiment, S-BF scheme can achieve the secure fuzzy query with the powerful privation protection capability for encrypted cloud database without the loss of fuzzy query efficiency.

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information. The need to develop and evaluate DeepFake detection algorithms calls for datasets of DeepFake videos. However, current DeepFake datasets suffer from low visual quality and do not resemble DeepFake videos circulated on the Internet. We present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5,639 high-quality DeepFake videos of celebrities generated using improved synthesis process. We conduct a comprehensive evaluation of DeepFake detection methods and datasets to demonstrate the escalated level of challenges posed by Celeb-DF.

By the multi-layer nonlinear mapping and the semantic feature extraction of the deep learning, a deep learning network is proposed for video face detection to overcome the challenge of detecting faces rapidly and accurately in video with changeable background. Particularly, a pre-training procedure is used to initialize the network parameters to avoid falling into the local optimum, and the greedy layer-wise learning is introduced in the pre-training to avoid the training error transfer in layers. Key to the network is that the probability of neurons models the status of human brain neurons which is a continuous distribution from the most active to the least active and the hidden layer’s neuron number decreases layer-by-layer to reduce the redundant information of the input data. Moreover, the skin color detection is used to accelerate the detection speed by generating candidate regions. Experimental results show that, besides the faster detection speed and robustness against face rotation, the proposed method possesses lower false detection rate and lower missing detection rate than traditional algorithms.

Cyber-Physical Systems (CPS) will form the basis for the world's critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cyber security. Aside from traditional information systems threats, CPS faces new challenges due to the heterogeneity of devices and protocols. In this paper, we investigate how Feature Selection may improve intrusion detection accuracy. In particular, we propose an adapted Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to improve the classification performance in CPS perception layer. Our numerical results reveal that GRASP metaheuristic overcomes traditional filter-based feature selection methods for detecting four attack classes in CPSs.

In-Band Full-Duplex (IBFD) Underwater Acoustic (UWA) communication technology plays a major role in enhancing the performance of Underwater acoustic sensor networks (UWSN). Self-Interference (SI) is one of the main inherent challenges affecting the performance of IBFD UWA communication. To reconstruct the SI signal and counteract the SI effect, this is important to estimate the short range channel through which the SI signal passes. Inaccurate estimation will result in the performance degradation of IBFD UWA communication. From the perspective of engineering implementation, we consider that the UWA communication modem shell has a significant influence on the short-range SI channel, which will limit the efficiency of self-interference cancellation in the analog domain to some degree. Therefore we utilize a simplified model to simulate the influence of the structure of the IBFD UWA communication modem on the receiving end. This paper studies the effect of acoustic-shell coupling on near-end self-interference signal of IBFD UWA communication modem. Some suggestions on the design of shell structure of IBFD UWA communication modem are given.