Biblio

The objective of this study is to explore changes of trust by a situation where drivers need to intervene. Trust in automation is a key determinant for appropriate interaction between drivers and the system. System transparency and types of system failure influence shaping trust in a supervisory control. Subjective ratings of trust were collected to examine the impact of two factors: system transparency (Detailed vs. Less) and system failure (by Limits vs. Malfunction) in a driving simulator study in which drivers experienced a partially automated vehicle. We examined trust ratings at three points: before and after driver intervention in the automated vehicle, and after subsequent experience of flawless automated driving. Our result found that system transparency did not have significant impacts on trust change from before to after the intervention. System-malfunction led trust reduction compared to those of before the intervention, whilst system-limits did not influence trust. The subsequent experience recovered decreased trust, in addition, when the system-limit occurred to drivers who have detailed information about the system, trust prompted in spite of the intervention. The present finding has implications for automation design to achieve the appropriate level of trust.

As the computer architecture technology evolves, communication protocols have been demanded not only having reliable security but also flexible functionality. Advanced cryptography has been expected as a new generation cryptography which suffices such the requirements. A pairing is one of the key technologies of the cryptography and the pairing has been known as having a substantial amount of construction parameters. Recently, the elliptic curve with embedding degree 14 is evaluated as one of the efficient curves for pairing. In the paper, we implement an optimal ate pairing on the elliptic curve by applying several variants of multiplication algorithms of extension field of degree 7 on multiple devices. The best multiplication algorithm among the candidates is derived. Besides, for efficient calculations, we propose a pseudo 7-sparse algorithm and a fast calculation method of final exponentiation. As a result, we discover the proper multiplication algorithm bases on the rate of addition and multiplications on several different computer platforms. Our proposed pseudo 7-sparse algorithm is approximately 1.54% faster than a regular algorithm on almost all tested platforms. Eventually, for the total execution time of pairing we record 9.33ms on Corei5-9500.

Double differential phase shift keying(DDPSK) modulation is an efficient method to compensate the Doppler shifts, whereas the phase noise will be amplified which results in the signal-to-noise ratio (SNR) loss. In this paper, we propose a novel receiver architecture for underwater acoustic DSSS communications with Doppler shifts. The proposed method adopts not only the DDPSK modulation to compensate the Doppler shifts, but also the improved bit-interleaved coded modulation with iterative decoding (BICM-ID) algorithm for DDPSK to recover the SNR loss. The improved DDPSK demodulator adopts the multi-symbol estimation to track the channel variation, and an extended trellis diagram is constructed for DDPSK demodulator. Theoretical simulation shows that our system can obtain around 10.2 dB gain over the uncoded performance, and 7.4 dB gain over the hard-decision decoding performance. Besides, the experiment conducted in the Songhua Lake also shows that the proposed receiver can achieve lower BER performance when Doppler shifts exists.

Data tracing is an important topic in the era of digital economy when data are considered as one of the core factors in economic activities. However, the current data traceability systems often fail to obtain public trust due to their centralization and opaqueness. Blockchain possesses natural technical features such as data tampering resistance, anonymity, encryption security, etc., and shows great potential of improving the data tracing credibility. In this paper, we propose a blockchain-PROV-based multi-center data provenance solution in where the PROV model standardizes the data record storage and provenance on the blockchain automatically and intelligently. The solution improves the transparency and credibility of the provenance data, such as to help the efficient control and open sharing of data assets.

Chaos-based encryption is one of the promising cryptography techniques that can be used. Although chaos-based encryption provides excellent security, the finite precision of number representation in computers affects decryption accuracy negatively. In this paper, a way to mitigate some problems regarding finite precision is analyzed. We show that the use of maps with small Lyapunov exponents can improve the performance of chaotic encryption scheme, making it suitable for image encryption.

Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are used as one of the defense mechanisms. However, static blacklists generated by signature-based approaches can neither keep up nor detect never-seen-before malicious domain names. To address this weakness, we applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a greater accuracy compared to a previously reported model. Additionally, we propose a new adversarial machine learning-based method to generate never-before-seen malware-related domain families. We augment the training dataset with new samples to make the training of the models more effective in detecting never-before-seen malicious domain names. To protect blacklists of malicious domain names against adversarial access and modifications, we devise secure data containers to store and transfer blacklists.

Hiding information is a process to hide data or include it in different digital media such as image, audio, video, and text. However, there are many techniques to achieve the process of hiding information in the image processing, in this paper, a new method has been proposed for hidden data mechanism (which is a text file), then a transposition cipher method has been employed for encryption completed. It can be used to build an encrypted text and also to increase security against possible attacks while sending it over the World Wide Web. A genetic algorithm has been affected in the adjustment of the encoded text and DNA in the creation of an encrypted text that is difficult to detect and then include in the image and that affected the image visual quality. The proposed method outperforms the state of arts in terms of efficiently retrieving the embedded messages. Performance evaluation has been recorded high visual quality scores for the (SNR (single to noise ratio), PSNR (peak single to noise ratio) and MSE (mean square error).

The trend of rapid development of mobile technologies has highlighted new challenges and threats regarding the information security by the using cookies in mobile learning (m-learning) systems. In order to overcome these challenges and threats, this paper has identified two main objectives. First, to give a review of most common types to cookies and second, to consider the challenges and threats regarding cookies with aspects that are directly related to issues of security and privacy. With these objectives is possible to bridge security gaps in m-learning systems. Moreover, the identified potential challenges and threats are discussed with the given proposals of pragmatic solutions for their mitigating or reducing. The findings of this research may help students to rise security awareness and security behavior in m-learning systems, and to better understand on-going security challenges and threats in m-learning systems.

In a Smart City, new technologies such as big data analytics, data fusion and artificial intelligence will increase awareness by measuring many phenomena and storing a huge amount of data. 5G will allow communication of these data among different infrastructures instantaneously. In a Smart City, security aspects are going to be a major concern. Some drawbacks, such as vulnerabilities of a highly integrated system and information overload, must be considered. To overcome these downsides, an innovative predictive model for Smart City security risk assessment has been developed. Risk metrics and indicators are defined by considering data coming from a wide range of sensors. An innovative ``what if'' algorithm is introduced to identify critical infrastructures functional relationship. Therefore, it is possible to evaluate the effects of an incident that involves one infrastructure over the others.

With increase in distributed energy resources (DERs) and smart loads, each energy resource and load need a separate power conversion system leading to complex coordination and interaction, reduced energy conversion efficiency, coordinating compliance to grid standards (IEEE 1547) from multiple sources, reduced security. Also, multiple vendors with legacy system designs and proprietary communications interfaces result in redundancy and increase in cost of power electronics systems. This paper presents an energy router concept for buildings applications which provides autonomous power flow between sources and loads with a novel agent-based software interface.

The real-time information of enemy locations is capable to transform the outcome of combat operations. Such information gathered using connected soldiers on the Internet of Battlefield Things (IoBT) is highly beneficial to create situational awareness (SA) and to plan an effective war strategy. This article presents the novel enemy localization method that uses the soldier's own locations and their gunshot direction. The hardware prototype has been developed that uses a triangulation for an enemy localization in two soldiers and a single enemy scenario. 4.24±1.77 m of average localization error and ±4° of gunshot direction error has been observed during this prototype testing. This basic model is further extended using three-stage software simulation for multiple soldiers and multiple enemy scenarios with the necessary assumptions. The effective algorithm has been proposed, which differentiates between the ghost and true predictions by analyzing the groups of subsequent shooting intents (i.e., frames). Four different complex scenarios are tested in the first stage of the simulation, around three to six frames are required for the accurate enemy localization in the relatively simple cases, and nine frames are required for the complex cases. The random error within ±4° in gunshot direction is included in the second stage of the simulation which required almost double the number of frames for similar four cases. As the number of frames increases, the accuracy of the proposed algorithm improves and better ghost point elimination is observed. In the third stage, two conventional clustering algorithms are implemented to validate the presented work. The comparative analysis shows that the proposed algorithm is faster, computationally simple, consistent, and reliable compared with others. Detailed analysis of hardware and software results for various scenarios has been discussed in this article.

MIL-STD-1553 is a military standard that defines the specification of a serial communication bus that has been implemented in military and aerospace avionic platforms for over 40 years. MIL-STD-1553 was designed for a high level of fault tolerance while less attention was paid to cyber security issues. Thus, as indicated in recent studies, it is exposed to various threats. In this article, we suggest enhancing the security of MIL-STD-1553 communication buses by integrating a machine learning-based intrusion detection system (IDS); such anIDS will be capable of detecting cyber attacks in real time. The IDS consists of two modules: 1) a remote terminal (RT) authentication module that detects illegitimately connected components and data transfers and 2) a sequence-based anomaly detection module that detects anomalies in the operation of the system. The IDS showed high detection rates for both normal and abnormal behavior when evaluated in a testbed using real 1553 hardware, as well as a very fast and accurate training process using logs from a real system. The RT authentication module managed to authenticate RTs with +0.99 precision and +0.98 recall; and detect illegitimate component (or a legitimate component that impersonates other components) with +0.98 precision and +0.99 recall. The sequence-based anomaly detection module managed to perfectly detect both normal and abnormal behavior. Moreover, the sequencebased anomaly detection module managed to accurately (i.e., zero false positives) model the normal behavior of a real system in a short period of time ( 22 s).

Parallel with the developing world, transportation technologies have started to expand and change significantly year by year. This change brings with it some inevitable problems. Increasing human population and growing transportation-needs result many accidents in urban and rural areas, and this recursively results extra traffic problems and fuel consumption. It is obvious that the issues brought by this spiral loop needed to be solved with the use of some new technological achievements. In this context, self-driving cars or automated vehicles concepts are seen as a good solution. However, this also brings some additional problems with it. Currently many cars are provided with some digital security systems, which are examined in two phases, internal and external. These systems are constructed in the car by using some type of embedded system (such as the Controller Area Network (CAN)) which are needed to be protected form outsider cyberattacks. These attack can be detected by several ways such as rule based system, anomaly based systems, list based systems, etc. The current literature showed that researchers focused on the use of some artificial intelligence techniques for the detection of this type of attack. In this study, an intrusion detection system based on machine learning is proposed for the CAN security, which is the in-vehicle communication structure. As a result of the study, it has been observed that the decision tree-based ensemble learning models results the best performance in the tested models. Additionally, all models have a very good accuracy levels.

Human identification is a well-researched topic that keeps evolving. Advancement in technology has made it easy to train models or use ones that have been already created to detect several features of the human face. When it comes to identifying a human face from the side, there are many opportunities to advance the biometric identification research further. This paper investigates the human face identification based on their side profile by extracting the facial features and diagnosing the feature sets with geometric ratio expressions. These geometric ratio expressions are computed into feature vectors. The last stage involves the use of weighted means to measure similarity. This research addresses the problem of using an eXplainable Artificial Intelligence (XAI) approach. Findings from this research, based on a small data-set, conclude that the used approach offers encouraging results. Further investigation could have a significant impact on how face profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89.

Approximate nearest neighbour search (ANNS) in high dimensional space is a fundamental problem in many applications, such as multimedia database, computer vision and information retrieval. Among many solutions, data-sensitive hashing-based methods are effective to this problem, yet few of them are designed for external storage scenarios and hence do not optimized for I/O efficiency during the query processing. In this paper, we introduce a novel data-sensitive indexing and query processing framework for ANNS with an emphasis on optimizing the I/O efficiency, especially, the sequential I/Os. The proposed index consists of several lists of point IDs, ordered by values that are obtained by learned hashing (i.e., mapping) functions on each corresponding data point. The functions are learned from the data and approximately preserve the order in the high-dimensional space. We consider two instantiations of the functions (linear and non-linear), both learned from the data with novel objective functions. We also develop an I/O efficient ANNS framework based on the index. Comprehensive experiments on six benchmark datasets show that our proposed methods with learned index structure perform much better than the state-of-the-art external memory-based ANNS methods in terms of I/O efficiency and accuracy.

As a typical representative of a new generation military information technology, the value and significance of Internet of Battlefield Things (IoBT) has been widely recognized by the world's military forces. At the same time, Internet of Battlefield Things (IoBT) is facing serious scalability and security challenges. This paper presents the basic concept and six-domain model of IoBT, explains the integration security framework of IoBT and blockchain. Furthermore, we design and build a novel IoT framework called IoBTChain based on blockchain and smart contracts, which adopts a credit-based resource management system to control the amount of resources that an IoBT device can obtain from a cloud server based on pre-defined priority rules, application types, and behavior history. We illustrate the deployment procedure of blockchain and smart contracts, the device registration procedure on blockchain, the IoBT behavior regulation workflow and the pricing-based resource allocation algorithm.

This paper shows the benefits of synchrophasor technology for islanding detection and resynchronization in the control room at Empresa de Transmisión Eléctrica Dominicana (ETED) in the Dominican Republic. EPG's Real Time Dynamics Monitoring System (RTDMS®) deployed at ETED was tested during operator training with the event data after an islanding event occurred on October 26, 2019, which caused the ETED System to split into two islands. RTDMS's islanding detection algorithm quickly detected and identified the event. The islanding situation was not clear for operators during the time of the event with the use of traditional SCADA tools. The use of synchophasor technology also provides valuable information for a quick and safe resynchronization. By monitoring the system frequency in each island and voltage angle differences between islands, operators can know the exact time of circuit breaker closure for a successful resynchronization. Synchrophasors allow the resynchronization in a relatively short time, avoiding the risk of additional load loss, generator outages or even a wider system blackout.

Data sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted -ℓ1(IRW - ℓ1) minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.

In the context of a rapidly changing and increasingly complex (industrial) production landscape, securing the (communication) infrastructure is becoming an ever more important but also more challenging task - accompanied by the application of radio communication. A worthwhile and promising approach to overcome the arising attack vectors, and to keep private networks private, are Physical Layer Security (PhySec) implementations. The paper focuses on the transfer of the IEEE802.11 (WLAN) PhySec - Secret Key Generation (SKG) algorithms to Next Generation Mobile Networks (NGMNs), as they are the driving forces and key enabler of future industrial networks. Based on a real world Long Term Evolution (LTE) testbed, improvements of the SKG algorithms are validated. The paper presents and evaluates significant improvements in the establishment of channel profiles, whereby especially the Bit Disagreement Rate (BDR) can be improved substantially. The combination of the Discrete Cosine Transformation (DCT) and the supervised Machine Learning (ML) algorithm - Linear Regression (LR) - provides outstanding results, which can be used beyond the SKG application. The evaluation also emphasizes the appropriateness of PhySec for securing private networks.

Wireless security is confronted with the complexity of the secret key distribution process, which is difficult to implement on an Ad Hoc network without a key management infrastructure. The symmetric key extraction mechanism from a response channel in a wireless environment is a very promising alternative solution with the simplicity of the key distribution process. Various mechanisms have been proposed for extracting the symmetric key, but many mechanisms produce low rates of the symmetric key due to the high bit differences that occur. This led to the fact that the reconciliation phase was unable to make corrections, as a result of which many key bits were lost, and the time required to obtain a symmetric key was increased. In this paper, we propose the use of an interval approach that divides the response channel into segments at specific intervals to reduce the key bit difference and increase the key rates. The results of tests conducted in the wireless environment show that the use of these mechanisms can increase the rate of the keys up to 35% compared to existing mechanisms.

Side-channel analysis (SCA) is a big threat to the security of connected embedded devices. Over the last few years, physical non-invasive SCA attacks utilizing the electromagnetic (EM) radiation (EM side-channel `leakage') from a crypto IC has gained huge momentum owing to the availability of the low-cost EM probes and development of the deep-learning (DL) based profiling attacks. In this paper, our goal is to understand the source of the EM leakage by analyzing a white-box modeling of the EM leakage from the crypto IC, leading towards a low-overhead generic countermeasure. To kill this EM leakage from its source, the solution utilizes a signature attenuation hardware (SAH) encapsulating the crypto core locally within the lower metal layers such that the critical correlated crypto current signature is significantly attenuated before it passes through the higher metal layers to connect to the external pin. The protection circuit utilizing AES256 as the crypto core is fabricated in 65nm process and shows for the first time the effects of metal routing on the EM leakage. The \textbackslashtextgreater 350× signature attenuation of the SAH together with the local lower metal routing ensured that the protected AES remains secure even after 1B measurements for both EM and power SCA, which is an 100× improvement over the state-of-the-art with comparable overheads. Overall, with the combination of the 2 techniques - signature suppression and local lower metal routing, we are able to kill the EM side-channel leakage at its source such that the correlated signature is not passed through the top-level metals, MIM capacitors, or on-board inductors, which are the primary sources of EM leakage, thereby preventing EM SCA attacks.

Globalization of IC manufacturing has led to increased security concerns, notably IP theft. Several logic locking techniques have been developed for protecting designs, but they typically display very large overhead, and are generally susceptible to deciphering attacks. In this paper, we propose latch-based logic locking, which manipulates both the flow of data and logic in the design. This method converts an interconnected subset of existing flip-flops to pairs of latches with programmable phase. In tandem, decoy latches and logic are added, inhibiting an attacker from determining the actual design functionality. To validate this technique, we developed and verified a locking insertion flow, analyzed PPA and ATPG overhead on benchmark circuits and industry cores, extended existing attacks to account for the technique, and taped out a demonstration chip. Importantly, we show that the design overhead with this approach is significantly less than with previous logic locking schemes, while resisting model checker-based, oracle-driven attacks. With minimal delay overhead, large numbers of decoy latches can be added, cheaply increasing attack resistance.

With the gradual advancement of smart city construction, various information systems have been widely used in smart cities. In order to obtain huge economic benefits, criminals frequently invade the information system, which leads to the increase of malware. Malware attacks not only seriously infringe on the legitimate rights and interests of users, but also cause huge economic losses. Signature-based malware detection algorithms can only detect known malware, and are susceptible to evasion techniques such as binary obfuscation. Behavior-based malware detection methods can solve this problem well. Although there are some malware behavior analysis works, they may ignore semantic information in the malware API call sequence. In this paper, we design a joint framework based on local and global features for malware detection to solve the problem of network security of smart cities, called LGMal, which combines the stacked convolutional neural network and graph convolutional networks. Specially, the stacked convolutional neural network is used to learn API call sequence information to capture local semantic features and the graph convolutional networks is used to learn API call semantic graph structure information to capture global semantic features. Experiments on Alibaba Cloud Security Malware Detection datasets show that the joint framework gets better results. The experimental results show that the precision is 87.76%, the recall is 88.08%, and the F1-measure is 87.79%. We hope this paper can provide a useful way for malware detection and protect the network security of smart city.

Secure wireless communication is essential for most industrial applications. The secure and reliable control of processes as well as the data integrity of measured values are key targets in these applications. The industrial Internet-of-Things (IIoT) tries to connect an increasing number of sensors wirelessly. The wireless sensors form wireless sensor networks (WSNs). However, wireless sensor nodes are exposed to various security threats ranging from physical modification on the device itself to remote attacks via the communication channel. It is important to secure the complete lifetime of the wireless sensor nodes and other system components. This includes the production phase, shipping, preparation phase and operational phase. This paper presents a lifetime security concept for a wireless sensor network applied in automotive test beds. In this application scenario, the wireless sensor nodes are used to capture various temperatures in an automotive unit under test. In order to indicate the current state of trustworthiness of the system, a trustworthiness indicator is implemented which is shown to the user. An evaluation of the impact of encrypted communication on power consumption shows that the increase is negligible, and can be expected to be provided by the wireless sensor node's power supply without reducing the node lifetime.

Increasing deep neural networks are applied in solving graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep models can be revealed using carefully crafted adversarial examples generated by various adversarial attack methods. To explore this security problem, we define the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) strategy using the gradient information in the trained graph autoencoder (GAE) model. Not surprisingly, GAE can be fooled by an adversarial graph with a few links perturbed on the clean one. The results on comprehensive experiments of different real-world graphs indicate that most deep models and even the state-of-the-art link prediction algorithms cannot escape the adversarial attack, such as GAE. We can benefit the attack as an efficient privacy protection tool from the link prediction of unknown violations. On the other hand, the adversarial attack is a robust evaluation metric for current link prediction algorithms of their defensibility.