Biblio

Derivatives are key to numerous science, engineering, and machine learning applications. While existing tools generate derivatives of programs in a single language, modern parallel applications combine a set of frameworks and languages to leverage available performance and function in an evolving hardware landscape. We propose a scheme for differentiating arbitrary DAG-based parallelism that preserves scalability and efficiency, implemented into the LLVM-based Enzyme automatic differentiation framework. By integrating with a full-fledged compiler backend, Enzyme can differentiate numerous parallel frameworks and directly control code generation. Combined with its ability to differentiate any LLVM-based language, this flexibility permits Enzyme to leverage the compiler tool chain for parallel and differentiation-specitic optimizations. We differentiate nine distinct versions of the LULESH and miniBUDE applications, written in different programming languages (C++, Julia) and parallel frameworks (OpenMP, MPI, RAJA, Julia tasks, MPI.jl), demonstrating similar scalability to the original program. On benchmarks with 64 threads or nodes, we find a differentiation overhead of 3.4–6.8× on C++ and 5.4–12.5× on Julia.

Cyber threat intelligence (CTI) is vital for enabling effective cybersecurity decisions by providing timely, relevant, and actionable information about emerging threats. Monitoring the dark web to generate CTI is one of the upcoming trends in cybersecurity. As a result, developing CTI capabilities with the dark web investigation is a significant focus for cybersecurity companies like Deepwatch, DarkOwl, SixGill, ThreatConnect, CyLance, ZeroFox, and many others. In addition, the dark web marketplace (DWM) monitoring tools are of much interest to law enforcement agencies (LEAs). The fact that darknet market participants operate anonymously and online transactions are pseudo-anonymous makes it challenging to identify and investigate them. Therefore, keeping up with the DWMs poses significant challenges for LEAs today. Nevertheless, the offerings on the DWM give insights into the dark web economy to LEAs. The present work is one such attempt to describe and analyze dark web market data collected for CTI using a dark web crawler. After processing and labeling, authors have 53 DWMs with their product listings and pricing.

Information exchange occurs all the time in today’s internet era. Some of the data are public, and some are private. Asymmetric cryptography plays a critical role in securing private data transfer. However, technological advances caused private data at risk due to the presence of quantum computers. Therefore, we need a new method for securing private data. This paper proposes combining DNA cryptography methods based on the NTRU cryptosystem to enhance security data confidentiality. This method is compared with conventional public key cryptography methods. The comparison shows that the proposed method has a slow encryption and decryption time compared to other methods except for RSA. However, the key generation time of the proposed method is much faster than other methods tested except for ECC. The proposed method is superior in key generation time and considerably different from other tested methods. Meanwhile, the encryption and decryption time is slower than other methods besides RSA. The test results can get different results based on the programming language used.

In this paper, we design a new framework that can utilize the current global optimization heuristics for solving the straight-line program (SLP) problem. We combine Paar1, Paar2, BP (Boyar-Peralta), BFI, RNBP (Random-Boyar Peralta), A1, A2, XZLBZ, and LWFWSW (backward search) state-of-the-art heuristics by taking the XOR (exclusive OR) count metrics into consideration. Thus, by using the proposed framework, optimal circuit implementations of a given diffusion (or linear) layer can be found with fewer XOR gate counts.

With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.

Edge detection is the key and difficult point of machine vision and image processing technology. The traditional edge detection algorithm is sensitive to noise and it is difficult to accurately extract the edge of the image, so the effect of image processing is not ideal. To solve this problem, people in the industry use the structural element features of morphological edge detection operator to extract the edge features of the image by carefully designing and combining the structural elements of different sizes and directions, so as to effectively ensure the integrity of edge information in all directions and eliminate large noise at the same time. This paper first introduces the traditional edge detection algorithms, then summarizes the edge detection algorithms based on mathematical morphology in recent years, finds that the selection of multi-scale and multi-directional structural elements is an important research direction, and finally discusses the development trend of mathematical morphology edge detection technology.

With the gradual construction and implementation of cloud computing, the information security problem of the smart grid has surfaced. Therefore, in the construction of the smart grid cloud computing platform, information security needs to be considered in planning, infrastructure, and management at the same time, and it is imminent to build an information network that is secure from terminal to the platform to data. This paper introduces the concept of cloud security technology and the latest development of cloud security technology and discusses the main strategies of cloud security construction in electric power enterprises.

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.

A unified cloud management platform is the key to efficient and secure management of cloud computing resources. To improve the operation effect of the power cloud service platform, power companies can use the micro-service architecture technology to carry out data processing, information integration, and innovative functional architecture of the power cloud service platform, realize the optimal design of the power cloud service platform and improve the power cloud service platform-security service quality. According to the technical requirements of the power cloud security management platform, this paper designs the technical architecture of the power unified cloud security management platform and expounds on the functional characteristics of the cloud security management platform to verify the feasibility and effectiveness of the cloud security management platform.

Acoustic communication is a key enabler for underwater Internet of Things networks between autonomous underwater platforms. Underwater Internet of Things networks face a harsh communications environment and limited energy resources which makes them susceptible to interference, whether intentional (i.e., jamming) or unintentional. Resilient, power efficient waveforms and modulation schemes are needed for underwater acoustic communications in order to avoid outages and excessive power drain. We explore the impact of modulation scheme on the resiliency of underwater acoustic communications in the presence of channel impairments, interference, and jamming. In particular, we consider BFSK and OFDM schemes for underwater acoustic communications and assess the utility of Polar coding for strengthening resiliency.

With the rapid development of multimedia and short video, there is a growing concern for video copyright protection. Some work has been proposed to add some copyright or fingerprint information to the video to trace the source of the video when it is stolen and protect video copyright. This paper proposes a video watermarking method based on a deep neural network and curriculum learning for watermarking of sliced videos. The first frame of the segmented video is perturbed by an encoder network, which is invisible and can be distinguished by the decoder network. Our model is trained and tested on an online educational video dataset consisting of 2000 different video clips. Experimental results show that our method can successfully discriminate most watermarked and non-watermarked videos with low visual disturbance, which can be achieved even under a relatively high video compression rate(H.264 video compress with CRF 32).

The digital transformation brought on by 5G is redefining current models of end-to-end (E2E) connectivity and service reliability to include security-by-design principles necessary to enable 5G to achieve its promise. 5G trustworthiness highlights the importance of embedding security capabilities from the very beginning while the 5G architecture is being defined and standardized. Security requirements need to overlay and permeate through the different layers of 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture within a risk-management framework that takes into account the evolving security-threats landscape. 5G presents a typical use-case of wireless communication and computer networking convergence, where 5G fundamental building blocks include components such as Software Defined Networks (SDN), Network Functions Virtualization (NFV) and the edge cloud. This convergence extends many of the security challenges and opportunities applicable to SDN/NFV and cloud to 5G networks. Thus, 5G security needs to consider additional security requirements (compared to previous generations) such as SDN controller security, hypervisor security, orchestrator security, cloud security, edge security, etc. At the same time, 5G networks offer security improvement opportunities that should be considered. Here, 5G architectural flexibility, programmability and complexity can be harnessed to improve resilience and reliability. The working group scope fundamentally addresses the following: •5G security considerations need to overlay and permeate through the different layers of the 5G systems (physical, network, and application) as well as different parts of an E2E 5G architecture including a risk management framework that takes into account the evolving security threats landscape. •5G exemplifies a use-case of heterogeneous access and computer networking convergence, which extends a unique set of security challenges and opportunities (e.g., related to SDN/NFV and edge cloud, etc.) to 5G networks. Similarly, 5G networks by design offer potential security benefits and opportunities through harnessing the architecture flexibility, programmability and complexity to improve its resilience and reliability. •The IEEE FNI security WG's roadmap framework follows a taxonomic structure, differentiating the 5G functional pillars and corresponding cybersecurity risks. As part of cross collaboration, the security working group will also look into the security issues associated with other roadmap working groups within the IEEE Future Network Initiative.

The Robotic Operating System (ROS) is a popular framework for robotics research and development. It's a system that provides hardware abstraction with low-level device management to handle communications and services. ROS is a distributed system, which allows various nodes in a network to communicate using a method such as message passing. When integrating systems using ROS, it is vital to consider the security and privacy of the data and information shared across ROS nodes, which is considered to be one of the most challenging aspects of ROS systems. The goal of this study is to examine the ROS architecture, primary components, and versions, as well as the types of vulnerabilities that might compromise the system. In order to achieve the CIA's three fundamental security criteria on a ROS-based platform, we categorized these vulnerabilities and looked into various security solutions proposed by researchers. We provide a comparative analysis of the ROS-related security solutions, the security threats and issues they addressed, the targeted architecture of the protection or defense system, the solution's evaluation methodology and the evaluation metric, and the limitations that might be viewed as unresolved issues for the future course of action. Finally, we look into future possibilities and open challenges to assist researchers to develop more secure and efficient ROS systems.

Internet service providers (ISP) rely on network traffic classifiers to provide secure and reliable connectivity for their users. Encrypted traffic introduces a challenge as attacks are no longer viable using classic Deep Packet Inspection (DPI) techniques. Distinguishing encrypted from non-encrypted traffic is the first step in addressing this challenge. Several attempts have been conducted to identify encrypted traffic. In this work, we compare the detection performance of DPI, traffic pattern, and randomness tests to identify encrypted traffic in different levels of granularity. In an experimental study, we evaluate these candidates and show that a traffic pattern-based classifier outperforms others for encryption detection.

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

Anomalous behaviour in subsystems of complex machines often affect overall performance even without failures. We devise unsupervised methods to detect times with degraded performance, and localize correlated signals, evaluated on a system with over 4000 monitored signals. From incidents comprising both downtimes and degraded performance, our approach localizes relevant signals within 1.2% of the parameter space.

Currently, many organizations are moving to new digital management systems, which is accompanied not only by the introduction of new approaches based on the use of information technology, but also by a change in the organizational and management environment. Risk management is a process necessary to maintain the competitive advantage of an organization, but it can also become involved in the course of digitalization itself, which means that risk management also needs to change to meet modern conditions and ensure the effectiveness of the organization. This article discusses the risk management process in the digital environment. The main approach to the organization of this process is outlined, taking into account the use of information tools, together with the stages of this process, which directly affect the efficiency of the company. The risks that are specific to a digital organization are taken into account. Modern requirements for risk management for organizations are studied, ways of their implementation are outlined. The result is a risk management process that functions in a digital organization.

In the context of IoT (Internet of Things), Device Management (DM), i.e., remote administration of IoT devices, becomes essential to keep them connected, updated and secure, thus increasing their lifespan through firmware and configuration updates and security patches. Legacy DM solutions are adequate when dealing with home devices (such as Television set-top boxes) but need to be extended to adapt to new IoT requirements. Indeed, their manual operation by system administrators requires advanced knowledge and skills. Further, the static DM platform — a component above IoT platforms that offers advanced features such as campaign updates / massive operation management — is unable to scale and adapt to IoT dynamicity. To cope with this, this work, performed in an industrial context at Orange, proposes a self-adaptive architecture with runtime horizontal scaling of DM servers, with an autonomic Auto-Scaling Manager, integrating in the loop constraint programming for decision-making, validated with a meaningful industrial use-case.

Resilience and antifragility under duress present significant challenges for autonomic and self-adaptive systems operating in contested environments. In such settings, the system has to continually plan ahead, accounting for either an adversary or an environment that may negate its actions or degrade its capabilities. This will involve projecting future states, as well as assessing recovery options, counter-measures, and progress towards system goals. For antifragile systems to be effective, we envision three self-* properties to be of key importance: self-exploration, self-learning and self-training. Systems should be able to efficiently self-explore – using adversarial search – the potential impact of the adversary’s attacks and compute the most resilient responses. The exploration can be assisted by prior knowledge of the adversary’s capabilities and attack strategies, which can be self-learned – using opponent modelling – from previous attacks and interactions. The system can self-train – using reinforcement learning – such that it evolves and improves itself as a result of being attacked. This paper discusses those visions and outlines their realisation in AWaRE, a cyber-resilient and self-adaptive multi-agent system.

The construction of a strong and smart grid is inseparable from the advancement of the power system, and the effective application of modern communication technologies allows the traditional grid to better transform into the energy Internet. With the advent of 5G, people pay close attention to the application of network slicing, not only as an emerging technology, but also as a new business model. In this article, we consider the delay requirements of certain services in the power grid. First, we analyze the security issues in network slicing and model the 5G core network slicing supply as a mixed integer linear programming problem. On this basis, a heuristic algorithm is proposed. According to the topological properties, resource utilization and delay of the slice nodes, the importance of them is sorted using the VIKOR method. In the slice link configuration stage, the shortest path algorithm is used to obtain the slice link physical path. Considering the delay of the slice link, a strategy for selecting the physical path is proposed. Simulations show that the scheme and algorithm proposed in this paper can achieve a high slice configuration success rate while ensuring the end-to-end delay requirements of the business, and meet the 5G core network slice security requirements.

K-nearest neighbor algorithm that searches the K closest samples in a high dimensional feature space is one of the most fundamental tasks in machine learning and image retrieval applications. Computational storage device that combines computing unit and storage module on a single board becomes popular to address the data bandwidth bottleneck of the conventional computing system. In this paper, we propose a nearest neighbor search acceleration platform based on computational storage device, which can process a large-scale image dataset efficiently in terms of speed, energy, and cost. We believe that the proposed acceleration platform is promising to be deployed in cloud datacenters for data-intensive applications.

To ensure comprehensive information protection, it is necessary to use various means of information protection, distributed by levels and segments of the information system. This creates a contradiction, which consists in the presence of many different means of information protection and the inability to ensure their joint coordinated application in ensuring the protection of information due to the lack of an automated control system. One of the tasks that contribute to the solution of this problem is the task of generating a feasible organizational structure and the structure of such an automated control system, the results of which would provide these options and choose the one that is optimal under given initial parameters and limitations. The problem is solved by reducing the General task with particular splitting the original graph of the automated cyber defense control system into subgraphs. As a result, the organizational composition and the automated cyber defense management system structures will provide a set of acceptable variants, on the basis of which the optimal choice is made under the given initial parameters and restrictions. As a result, admissible variants for the formation technique of organizational structure and structure by the automated control system of cyber defense is received.

We present results from a vibrating mesh nebulizer for which the mesh is a micro-machined silicon membrane perforated with up to a thousand micron-sized, pyramidal holes. Finite element modelling is used to better understand the measured results of the nebulizer when tested in the dry state as well as when loaded with a liquid. In particular, we found that the frequency response of the system is well represented by the superposition of the frequency response of its two main subcomponents: the piezo driving unit and the silicon membrane. As such, the system is found to have resonance peaks for which the complete assembly flexes in addition to peaks that correspond to the flexural resonance modes of the silicon membrane on its own. Similarly, finite element modelling was used to understand differences observed between the frequency response measured on the nebulizer in the dry condition compared to its wet or liquid loaded operation. It was found that coupling between the structural and the acoustic domains shifts the resonance peaks significantly to the left of the frequency plot. In fact, it was found that at the operating frequency of the nebulizer, the system resonates in a (0,3) when the membrane is loaded with a liquid compared with a (0,2) resonance mode when it is operating in the dry state.

Information-Centric Networking (ICN) is among the promising architecture that can drive the need and versatility towards the future generation (xG) needs. In the future, support for network communication relies on the area of telemedicine, autonomous vehicles, and disaster recovery. In the disaster recovery case, there is a high possibility where the communication path is severed. Multicast communication and DTN-friendly route algorithm are becoming suitable options to send a packet message to get a faster response and to see any of the nodes available for service, this approach could give burden to the core network. Also, during disaster cases, many people would like to communicate, receive help, and find family members. Flooding the already disturbed/severed network will further reduce communication performance efficiency even further. Thus, this study takes into consideration prioritization factors to allow networks to process and delivering priority content. For this purpose, the proposed technique introduces the Routable Prefix Identifier (RP-ID) that takes into account the prioritization factor to enable optimization in Delay Tolerant ICN communication.

5G and beyond 5G low power wireless networks make Internet of Things (IoT) and Cyber-Physical Systems (CPS) applications capable of serving massive amounts of devices and machines. Due to the broadcast nature of wireless networks, it is crucial to secure the communication between these devices and machines from spoofing and interception attacks. This paper is concerned with the security of carrier frequency offset (CFO) based continuous physical layer authentication. The interaction between an attacker and a defender is modeled as a dynamic discrete leader-follower game with imperfect information. In the considered model, a legitimate user (Alice) communicates with the defender/operator (Bob) and is authorized by her CFO continuously. The attacker (Eve), by listening/eavesdropping the communication between Alice and Bob, tries to learn the CFO characteristics of Alice and aims to inject malicious packets to Bob by impersonating Alice. First, by showing that the optimal attacker strategy is a threshold policy, an optimization problem of the attacker with exponentially growing action space is reduced to a tractable integer optimization problem with a single parameter, then the corresponding defender cost is derived. Extensive simulations illustrate the characteristics of optimal strategies/utilities of the players depending on the actions, and show that the defender’s optimal false positive rate causes attack success probabilities to be in the order of 0.99. The results show the importance of the parameters while finding the balance between system security and efficiency.