Biblio

Magnetized Liner Inertial Fusion (MagLIF) is a magneto-inertial fusion concept that relies on fuel magnetization, laser preheat, and a magnetically driven implosion to produce fusion conditions. In MagLIF, the target is a roughly 10 mm long, 5 mm diameter, 0.5 mm thick, cylindrical beryllium shell containing 1 mg/cm 3 D 2 gas. An axial magnetic field on the order of 10 T is applied to the target, and several kJ of laser energy is deposited into the fuel. Up to 20 MA of current is driven axially through the beryllium target, causing it to implode over approximately 100 ns. The implosion produces a 100-μm diameter, 8-mm tall fuel column with a burn-averaged ion temperature of several keV, that generates 10 11 -10 13 DD neutrons.

A source code static analysis became an industrial standard for program source code issues early detection. As one of requirements to such kind of analysis is high performance to provide response of automatic code checking tool as early as possible as far as such kind of tools integrates to Continuous testing and Integration systems. In this paper we propose a source code static analysis algorithm for solving performance issue of source code static analysis tool in general way.

The Z machine is a pulsed power generator located at Sandia National Laboratories in Albuquerque, New Mexico. It is capable of producing a \textbackslashtextgreater20 MA current pulse that is directed onto an experimental load. While a diverse array of experiments are conducted on the Z machine, including x-ray production and dynamic materials science experiments, the focus of this presentation are the Magnetic Liner Inertial Fusion (MagLIF) experiments. In these experiments, an axial magnetic field is applied to the load region, where a cylindrical, fuel-filled metal liner is imploded. We explore the effects of this field on the ability to efficiently couple the generator current to the load, and the extent to which this field interrupts the magnetic insulation of the inner-most transmission line. We find that at the present-day applied field values, the effects of the applied field on current coupling are negligible. Estimates of the potential impact on current coupling of the larger applied field values planned for future experiments are also given. Shunted current is measured with B-dot probes and flyer velocimetry techniques. Analytical calculations, 2D particle-in-cell simulations, and experimental measurements will be presented.

Security is a requirement in society, yet its wide implementation is held back because of high expenses, and barriers to the use of technology. Experimental implementation of security at low cost will only help in promoting the technology at more affordable prices. This paper describes the design of a security system of surveillance using Raspberry Pi and Arduino UNO. The design senses the presence of \$a\$ human in a surveillance area and immediately sets off the buzzer and simultaneously starts capturing video of the motion it had detected and stores it in a folder. When the design senses a motion, it immediately sends an SMS to the user. The user of this design can see the live video of the motion it detects using the internet connection from a remote area. Our objective of making a low-cost surveillance area security system has been mostly fulfilled. Although this is a low-cost project, features can be compared with existing commercially available systems.

Many information systems have been around for several decades, and most of them have their underlying databases. The data accumulated in those databases over the years could be a very valuable asset, which must be protected. The first role of database auditing is to ensure and confirm that security measures are set correctly. However, tracing user behavior and collecting a rich audit trail enables us to use that trail in a more proactive ways. As an example, audit trail could be analyzed ad hoc and used to prevent intrusion, or analyzed afterwards, to detect user behavior patterns, forecast workloads, etc. In this paper, we present a simple, secure, configurable, role-separated, and effective in-database auditing subsystem, which can be used as a base for access control, intrusion detection, fraud detection and other security-related analyses and procedures. It consists of a management relations, code and data object generators and several administrative tools. This auditing subsystem, implemented in several information systems, is capable of keeping the entire audit trail (data history) of a database, as well as all the executed SQL statements, which enables different security applications, from ad hoc intrusion prevention to complex a posteriori security analyses.

Scam websites or illegitimate internet portals are widely used to mislead users into fraud or malicious attacks, which may involve compromise of vital information. Scammers misuse the secrecy and anonymity of the internet of facade their true identity and purposes behind numerous disguises. These can include false security alerts, information betrayal, and other misleading presentations to give the impression of legality and lawfulness. The proposed research is a web-based application - Scam Website Analyser- which enables checking whether a website is a scammed one.. The main aim of the research is to improve security and prevent scams of public websites. It ensures maintaining the details of scam websites in a database and also requests the websites of other databases using external APIs. The basic idea behind the research is the concept of user -orienteers where the user is able to get information about scam websites and prevent themselves from using those sites in future.

Assuring Cybersecurity for the Internet of things (IoT) remains a significant challenge. Most IoT devices have minimal computational power and should be secured with lightweight security techniques (optimized computation and energy tradeoff). Furthermore, IoT devices are mainly designed to have long lifetimes (e.g., 10–15 years), forcing the designers to open the system for possible future updates. Here, we developed a lightweight and reconfigurable security architecture for IoT devices. Our research goal is to create a simple authentication protocol based on physical unclonable function (PUF) for FPGA-based IoT devices. The main challenge toward realization of this protocol is to make it make it resilient against machine learning attacks and it shall not use cryptography primitives.

Web Scraping is the technique of extracting desired data in an automated way by scanning the internal links and content of a website, this activity usually performed by systematically programmed bots. This paper explains our proposed solution to protect the blog content from theft and from being copied to other destinations by mitigating the scraping bots. To achieve our purpose we applied two steps in two levels, the first one, on the main blog page level, mitigated the work of crawler bots by adding extra empty articles anchors among real articles, and the next step, on the article page level, we add a random number of empty and hidden spans with randomly generated text among the article's body. To assess this solution we apply it to a local project developed using PHP language in Laravel framework, and put four criteria that measure the effectiveness. The results show that the changes in the file size before and after the application do not affect it, also, the processing time increased by few milliseconds which still in the acceptable range. And by using the HTML-similarity tool we get very good results that show the symmetric over style, with a few bit changes over the structure. Finally, to assess the effects on the bots, scraper bot reused and get the expected results from the programmed middleware. These results show that the solution is feasible to be adopted and use to protect blogs content.

In these days, malware attacks target different kinds of devices as IoT, mobiles, servers even the cloud. It causes several hardware damages and financial losses especially for big companies. Malware attacks represent a serious issue to cybersecurity specialists. In this paper, we propose a new approach to detect unknown malware families based on machine learning classification and visualization technique. A malware binary is converted to grayscale image, then for each image a GIST descriptor is used as input to the machine learning model. For the malware classification part we use 3 machine learning algorithms. These classifiers are so efficient where the highest precision reach 98%. Once we train, test and evaluate models we move to simulate 2 new malware families. We do not expect a good prediction since the model did not know the family; however our goal is to analyze the behavior of our classifiers in the case of new family. Finally, we propose an approach using a filter to know either the classification is normal or it's a zero-day malware.

There are numerous areas relied on random numbers. As one knows, in Cryptography, randomness plays a vital role from key generation to encrypting the systems. If randomness is not created effectively, the whole system is vulnerable to security threats where an outsider can easily predict the algorithm used to generate the random numbers in the system. Another main application where one would not touch is the role of random numbers in different devices mainly storage-related like Solid State Drives, Universal Serial Bus (USB), Secure Digital (SD) cards random performance testing. This paper focuses on various novel algorithms to generate random numbers for efficient performance evaluation of different drives. The main metrics for performance testing is random read and write performance. Here, the biggest challenge to test the random performance of the drive is not only the extent to which randomness is created but also the testing should cover the entire device (say complete NAND, NOR, etc.). So, the random number generator should generate in such a way that the random numbers should not be able to be predicted and must generate the numbers covering the entire range. This paper proposes different methods for such generators and towards the end, discusses the implementation in Field Programmable Gate Array (FPGA).

This article is about the current situation of cybercrime activity in the world. Research was planned to seek the possible protection measures taking into account the last events which might create an appropriate background for increasing of ransomware damages and cybercrime attacks. Nowadays, the most spread types of cybercrimes are fishing, theft of personal or payment data, cryptojacking, cyberespionage and ransomware. The last one is the most dangerous. It has ability to spread quickly and causes damages and sufficient financial loses. The major problem of this ransomware type is unpredictability of its behavior. It could be overcome only after the defined ransom was paid. This conditions created an appropriate background for the activation of cyber criminals’ activity even the organization of cyber gangs – professional, well-organized and well-prepared (tactical) group. So, researches conducted in this field have theoretical and practical value in the scientific sphere of research.

The Supervisory control and data acquisition (SCADA) systems have been continuously leveraging the evolution of network architecture, communication protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the internet of things (IoT). However, SCADA system has become the most profitable and alluring target for ransomware attackers. This paper proposes the deep learning-based novel ransomware detection framework in the SCADA controlled electric vehicle charging station (EVCS) with the performance analysis of three deep learning algorithms, namely deep neural network (DNN), 1D convolution neural network (CNN), and long short-term memory (LSTM) recurrent neural network. All three-deep learning-based simulated frameworks achieve around 97% average accuracy (ACC), more than 98% of the average area under the curve (AUC) and an average F1-score under 10-fold stratified cross-validation with an average false alarm rate (FAR) less than 1.88%. Ransomware driven distributed denial of service (DDoS) attack tends to shift the state of charge (SOC) profile by exceeding the SOC control thresholds. Also, ransomware driven false data injection (FDI) attack has the potential to damage the entire BES or physical system by manipulating the SOC control thresholds. It's a design choice and optimization issue that a deep learning algorithm can deploy based on the tradeoffs between performance metrics.

Being compliant with the GDPR (and data protection regulations in general) is a difficult task, that calls for manifold, computer-based automated support. In this context, several use cases related to the management and the enforcement of privacy policies and consent call for a machine-understandable policy language, equipped with reliable algorithms for compliance checking and explanations. In this paper, we outline a set of requirements for such languages and algorithms, and address such requirements with a framework based on a profile of OWL2 and a set of policy serializations based on popular formats such as ODRL and JSON. Such ``external'' policy syntax is translated into the ``internal'' OWL2 syntax, thereby enabling semantic compliance checking and explanations using specialized OWL2 reasoners. We provide a precise definition of both the OWL2 profile and the external policy language based on JSON.

Surveillance systems involve a camera module (at a fixed location) connected/streaming video via Internet Protocol to a (video) server. In our IMPRINT consortium project, by mounting miniaturised camera module/s on mobile quadruped-lizard like robots, we developed a stealth surveillance system, which could be very useful as a monitoring system in hostage situations. In this paper, we report about the communication system that enables secure transmission of: Live-video from robots to a server, GPS-coordinates of robots to the server and Navigation-commands from server to robots. Since the end application is for stealth surveillance, often can involve sensitive data, data security is a crucial concern, especially when data is transmitted through the internet. We use the RC4 algorithm for video transmission; while the AES algorithm is used for GPS data and other commands’ data transmission. Advantages of the developed system is easy to use for its web interface which is provided on the control station. This communication system, because of its internet-based communication, it is compatible with any operating system environment. The lightweight program runs on the control station (on the server side) and robot body that leads to less memory consumption and faster processing. An important requirement in such hostage surveillance systems is fast data processing and data-transmission rate. We have implemented this communication systems with a single-board computer having GPU that performs better in terms of speed of transmission and processing of data.

Content-centric networking is emerging as a credible alternative to host-centric networking, especially in scenarios of large-scale content distribution and where privacy requirements are crucial. Recently, research on content-centric networking has focused on security aspects and proposed solutions aimed to protect the network from attacks targeting the content delivery protocols. Content-centric networks are based on the strong assumption of being able to access genuine content from genuine nodes, which is however unrealistic and could open the door to disruptive attacks. Network node misbehavior, either due to poisoning attacks or malfunctioning, can act as a persistent threat that goes unnoticed and causes dangerous consequences. In this paper, we propose a novel certification methodology for content-centric networks that improves transparency and increases trustworthiness of the network and its nodes. The proposed approach builds on behavioral analysis and implements a continuous certification process that collects evidence from the network nodes and verifies their non-functional properties using a rule-based inference model. Utility, performance, and soundness of our approach have been experimentally evaluated on a simulated Named Data Networking (NDN) network targeting properties availability, integrity, and non-repudiation.

Compressive Covariance Sampling (CCS) is a strategy used to recover the covariance matrix (CM) directly from compressive measurements. Several works have proven the advantages of CSS in Compressive Spectral Imaging (CSI) but most of these algorithms require multiple random projections of the scene to obtain good reconstructions. However, several low-resolution copies of the scene can be captured in a single snapshot through a lenslet array. For this reason, this paper proposes a sensing protocol and a single snapshot CCS optical architecture using a lenslet array based on the Dual Dispersive Aperture Spectral Imager(DD-CASSI) that allows the recovery of the covariance matrix with a single snapshot. In this architecture uses the lenslet array allows to obtain different projections of the image in a shot due to the special coded aperture. In order to validate the proposed approach, simulations evaluated the quality of the recovered CM and the performance recovering the spectral signatures against traditional methods. Results show that the image reconstructions using CM have PSNR values about 30 dB, and reconstructed spectrum has a spectral angle mapper (SAM) error less than 15° compared to the original spectral signatures.

Style transfer means using a neural network to extract the content of one image and the style of the other image. The two are combined to get the final result, broadly applied in social communication, animation production, entertainment items. Using style transfer, users can share and exchange images; painters can create specific art styles more readily with less creation cost and production time. Therefore, style transfer is widely concerned recently due to its various and valuable applications. In the past few years, the paper reviews style transfer and chooses three representative works to analyze in detail and contrast with each other, including StyleGAN, CycleGAN, and TL-GAN. Moreover, what function an ideal model of style transfer should realize is discussed. Compared with such a model, potential problems and prospects of different methods to achieve style transfer are listed. A couple of solutions to these drawbacks are given in the end.

Even if there is a rapid proliferation with the advantages of low cost, the emerging on-demand cloud services have led to an increase in cybercrime activities. Cyber criminals are utilizing cloud services through its distributed nature of infrastructure and create a lot of challenges to detect and investigate the incidents by the security personnel. The tracing of command flow forms a clue for the detection of malicious activity occurring in the system through System Calls Analysis (SCA). As machine learning based approaches are known to automate the work in detecting malwares, simple Support Vector Machine (SVM) based approaches are often reporting low value of accuracy. In this work, a malware classification system proposed with the supervised machine learning of unknown malware instances through Support Vector Machine - Stochastic Gradient Descent (SVM-SGD) algorithm. The performance of the system evaluated on CIC-IDS2017 dataset with labelled attacks. The system is compared with traditional signature based detection model and observed to report less number of false alerts with improved accuracy. The signature based detection gets an accuracy of 86.12%, while the SVM-SGD gets the best accuracy of 99.13%. The model is found to be lightweight but efficient in detecting malware with high degree of accuracy.

The fundamental limits of high-current conduction and response of metal conductors to large, fast current pulses are of interest to high-speed fuses, exploding wires and foils, and magnetically driven dynamic material property and inertial confinement fusion experiments. A collaboration between the University of Nevada, Reno, University of New Mexico, and Sandia National Laboratory has fielded an electrically thick (R 400-μm \textbackslashtextgreater skin-depth) cylindrical metal rod platform in a Z-pinch configuration driven by the Sandia 100-ns, 900-kA Mykonos linear transformer driver 1 . Photonic Doppler velocimetry (PDV) measuring the expansion velocity of the uncoated surface of aluminum rods 2 was used to benchmark equation of state (EOS) and electrical conductivity models used in magnetohydrodynamics simulations using the Los Alamos National Laboratory (LANL) code FLAG 3 . The metal surface was found to expand along the liquid-vapor coexistence curve in density-temperature space for 90 ns of the rod’s expansion for both tabular EOSs with Van der Waals loops and with Maxwell constructions under the vapor dome. As the slope of the coexistence curve varies across EOS models, the metal surface in simulation was found to heat and expand at different rates depending on the model used. The expansion velocities associated with EOS models were then compared against the PDV data to validate the EOS used in simulations of similar systems. Here, the most recent aluminum EOS (SESAME 93722) 4 was found to drive a simulated velocity that best compared with the experimental data due to its relatively steep coexistence curve and high critical point.

Nowadays the use of Wi-Fi has been widely used in public places, such as in restaurants. The use of Wi-Fi in public places has a very large security vulnerability because it is used by a wide variety of visitors. Therefore, this study was conducted to evaluate the security of the WLAN network in restaurants. The methods used are Vulnerability Assessment and Penetration Testing. Penetration Testing is done by conducting several attack tests such as Deauthentication Attack, Evil Twin Attack with Captive Portal, Evil Twin Attack with Sniffing and SSL stripping, and Unauthorized Access.

In this study, a novel decentralized authentication model is proposed for establishing a secure communications structure in VoIP applications. The proposed scheme considers a distributed architecture called the blockchain. With this scheme, we highlight the multimedia data is more resistant to some of the potential attacks according to the centralized architecture. Our scheme presents the overall system authentication architecture, and it is suitable for mutual authentication in terms of privacy and anonymity. We construct an ECC-based model in the encryption infrastructure because our structure is time-constrained during communications. This study differs from prior work in that blockchain platforms with ECC-Based Biometric Signature. We generate a biometric key for creating a unique ID value with ECC to verify the caller and device authentication together in blockchain. We validated the proposed model by comparing with the existing method in VoIP application used centralized architecture.

Keystroke dynamics is a behavioural biometric form of authentication based on the inherent typing behaviour of an individual. While this technique is gaining traction, protecting the privacy of the users is of utmost importance. Fully Homomorphic Encryption is a technique that allows performing computation on encrypted data, which enables processing of sensitive data in an untrusted environment. FHE is also known to be “future-proof” since it is a lattice-based cryptosystem that is regarded as quantum-safe. It has seen significant performance improvements over the years with substantially increased developer-friendly tools. We propose a neural network for keystroke analysis trained using differential privacy to speed up training while preserving privacy and predicting on encrypted data using FHE to keep the users' privacy intact while offering sufficient usability.

With the recognition of cyberspace as an operating domain, concerted effort is now being placed on addressing it in the whole-of-domain manner found in land, sea, undersea, air, and space domains. Among the first steps in this effort is applying the standard supporting concepts of security, defense, and deterrence to the cyber domain. This paper presents an architecture that helps realize forward defense in cyberspace, wherein adversarial actions are repulsed as close to the origin as possible. However, substantial work remains in making the architecture an operational reality including furthering fundamental research cyber science, conducting design trade-off analysis, and developing appropriate public policy frameworks.

The notion of Zero Trust Architecture (ZTA) has been introduced as a fine-grained defense approach. It assumes that no entities outside and inside the protected system can be trusted and therefore requires articulated and high-coverage deployment of security controls. However, ZTA is a complex notion which does not have a single design solution; rather it consists of numerous interconnected concepts and processes that need to be assessed prior to deciding on a solution. In this paper, we outline a ZTA design methodology based on cyber risks and the identification of known high security risks. We then discuss challenges related to the design and deployment of ZTA and related solutions. We also discuss the role that service technology can play in ZTA.

Despite the performance of state-of-the-art Artificial Intelligence (AI) systems, some sectors hesitate to adopt AI because of a lack of trust in these systems. This attitude is prevalent among high-risk areas, where there is a reluctance to remove humans entirely from the loop. In these scenarios, Augmentation provides a preferred alternative over complete Automation. Instead of replacing humans, AI Augmentation uses AI to improve and support human operations, creating an environment where humans work side by side with AI systems. In this paper, we discuss how AI Augmentation can provide a path for building Trustworthy AI. We exemplify this approach using Robot Teleoperation. We lay out design guidelines and motivations for the development of AI Augmentation for Robot Teleoperation. Finally, we discuss the design of a Robot Teleoperation testbed for the development of AI Augmentation systems.