Biblio

Cyberspace is the fifth largest activity space after land, sea, air and space. Safeguarding Cyberspace Security is a major issue related to national security, national sovereignty and the legitimate rights and interests of the people. With the rapid development of artificial intelligence technology and its application in various fields, cyberspace security is facing new challenges. How to help the network security personnel grasp the security trend at any time, help the network security monitoring personnel respond to the alarm information quickly, and facilitate the tracking and processing of the monitoring personnel. This paper introduces a method of using situational awareness micro application actual combat attack and defense robot to quickly feed back the network attack information to the monitoring personnel, timely report the attack information to the information reporting platform and automatically block the malicious IP.

Recently, in solving problems of sound radiation by systems of piezoceramic radiators, new approaches have emerged, which make it possible to significantly approximate the design parameters of systems to the actually measured ones. These approaches are associated with taking into account the specific features of these systems performing two functions - the function of converting electrical energy into acoustic energy and the function of forming the latter in the surrounding space. The peculiarity of the first function is the interconnection of the electric, mechanical and acoustic fields during energy conversion. The peculiarity of the second function is the interaction of the radiators in the system during the formation of its acoustic field. The aim of the work is to study the effect of acoustic interaction of cylindrical piezoceramic radiators in the composition of flat systems on their physical fields. Using the method of coupled fields in multiply connected domains, using the addition theorems for cylindrical wave functions, we obtain analytical relations that allow one to calculate the numerical results for the parameters of three interconnected physical fields that ensure the emission of sound by plane systems. Their analysis showed that with the radial symmetry of electrical excitation of cylindrical radiators, the conversion of electrical energy into mechanical energy is carried out on one - zero mode of oscillation. The placement of the radiators in the composition of the flat systems leads to the appearance of the effect of acoustic interaction between them in an external field, due to the multiple exchange of radiated and scattered waves. This effect destroys the radial symmetry of the acoustic loading of a single radiator. The violation of symmetry in the conversion of mechanical energy into acoustic energy leads to the appearance of oscillations that follow the zero mode. As a result, there is an effective redistribution of energy “pumped” into the radiators in the zero mode, between subsequent oscillations of the radiators. In turn, the emergence of new modes changes the acoustic field of a flat system. The results show the need to take into account the above features of the physical fields of the radiators in the composition of flat systems when choosing methods and developing methods for measuring field characteristics.

The main limitation of acoustic particle separation for microfluidic application is its low sorting efficiency. This is due to the weak coupling of surface acoustic waves (SAWs) into the microchannel. In this work, we demonstrate bulk acoustic wave (BAW) particle sorting using capacitive micromachined ultrasonic transducers (CMUTs) for the first time. A collapsed mode CMUT was driven in air to generate acoustic pressure within the silicon substrate in the in-plane direction of the silicon die. This acoustic pressure was coupled into a water droplet, positioned at the side of the CMUT die, and measured with an optical hydrophone. By using a beam steering approach, the ultrasound generated from 32 CMUT elements were added in-phase to generate a maximum peak-to-peak pressure of 0.9 MPa. Using this pressure, 10 µm latex beads were sorted almost instantaneously.

Acoustic wave (AW) synthesis methodologies have become popular among AW filter designers because they provide a fast and precise seed to start with the design of AW devices. Nowadays, with the increasing complexity of carrier aggregation, there is a strong necessity to develop synthesis methods more focused on multiport filtering schemes. However, when dealing with multiport filtering functions, numerical accuracy plays an important role to succeed with the synthesis process since polynomial degrees are much higher as compared to the standalone filter case. In addition to polynomial degree, the number set of polynomial coefficients is also an important source of error during the extraction of the circuital elements of the filter. Nonetheless, in this paper is demonstrated that coupling matrix approaches are the best choice when the objective is to synthesize filtering functions with complex roots in their characteristic polynomials, which is the case of the channel polynomials of the multiport device.

The Internet of Things (IoT) is a technology that has evolved to make day-to-day life faster and easier. But with the increase in the number of users, the IoT network is prone to various security and privacy issues. And most of these issues/attacks occur during the routing of the data in the IoT network. Therefore, for secure routing among resource-constrained nodes of IoT, the RPL protocol has been standardized by IETF. But the RPL protocol is also vulnerable to attacks based on resources, topology formation and traffic flow between nodes. The attacks like DoS, Blackhole, eavesdropping, flood attacks and so on cannot be efficiently defended using RPL protocol for routing data in IoT networks. So, defense mechanisms are used to protect networks from routing attacks. And are classified into Secure Routing Protocols (SRPs) and Intrusion Detection systems (IDs). This paper gives an overview of the RPL attacks and the defense mechanisms used to detect or mitigate the RPL routing attacks in IoT networks.

The “Internet of Things” (IoT) is internetworking of physical devices known as 'things', algorithms, equipment and techniques that allow communication with another device, equipment and software over the network. And with the advancement in data communication, every device must be connected via the Internet. For this purpose, we use resource-constrained sensor nodes for collecting data from homes, offices, hospitals, industries and data centers. But various vulnerabilities may ruin the functioning of the sensor nodes. Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized, secure routing protocol designed for the 6LoWPAN IoT network. It's a proactive routing protocol that works on the destination-oriented topology to perform safe routing. The Sinkhole is a networking attack that destroys the topology of the RPL protocol as the attacker node changes the route of all the traffic in the IoT network. In this paper, we have given a survey of Sinkhole attacks in IoT and proposed different methods for preventing and detecting these attacks in a low-power-based IoT network.

The “Internet of Things (IoT)” is a term that describes physical sensors, processing software, power and other technologies to connect or interchange information between systems and devices through the Internet and other forms of communication. RPL protocol can efficiently establish network routes, communicate routing information, and adjust the topology. The 6LoWPAN concept was born out of the belief that IP should protect even the tiniest devices, and for low-power devices, minimal computational capabilities should be permitted to join IoT. The DIS-Flooding against RPL-based IoT with its mitigation techniques are discussed in this paper.

The Internet of Things (IoT) continuously grows as applications require connectivity and sensor networks are being deployed in multiple application domains. With the increased applicability demand, the need for testing and development frameworks also increases. This paper presents a novel simulation framework for testing IPv6 over Low Power Wireless Personal Networks (6LoWPAN) networks using the Mininet-WiFi simulator. The goal of the simulation framework is to allow easier automation testing of large-scale networks and to also allow easy configuration. This framework is a starting point for many development scenarios targeting traffic management, Quality of Service (QoS) or security network features. A basic smart city simulation is presented which demonstrates the working principles of the framework.

Internet of Things (IoT) evolution calls for stringent communication demands, including low delay and reliability. At the same time, wireless mesh technology is used to extend the communication range of IoT deployments, in a multi-hop manner. However, Wireless Mesh Networks (WMNs) are facing link failures due to unstable topologies, resulting in unsatisfied IoT requirements. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but necessitates adaptability to the challenging conditions of WMNs.In this work, we argue that Software-Defined Networking (SDN) is an ideal solution to fill this gap and introduce an integrated SDN-NDN deployment over WMNs involving: (i) global view of the network in real-time; (ii) centralized decision making; and (iii) dynamic NDN adaptation to network changes. The proposed system is deployed and evaluated over the wiLab.1 Fed4FIRE+ test-bed. The proof-of-concept results validate that the centralized control of SDN effectively supports the NDN operation in unstable topologies with frequent dynamic changes, such as the WMNs.

Zero trust security model has been picking up adoption in various organizations due to its various advantages. Data quality is still one of the fundamental challenges in data curation in many organizations where data consumers don’t trust data due to associated quality issues. As a result, there is a lack of confidence in making business decisions based on data. We design a model based on the zero trust security model to demonstrate how the trust of data consumers can be established. We present a sample application to distinguish the traditional approach from the zero trust based data quality framework.

The growing complexity of wireless networks has sparked an upsurge in the use of artificial intelligence (AI) within the telecommunication industry in recent years. In network slicing, a key component of 5G that enables network operators to lease their resources to third-party tenants, AI models may be employed in complex tasks, such as short-term resource reservation (STRR). When AI is used to make complex resource management decisions with financial and service quality implications, it is important that these decisions be understood by a human-in-the-loop. In this paper, we apply state-of-the-art techniques from the field of Explainable AI (XAI) to the problem of STRR. Using real-world data to develop an AI model for STRR, we demonstrate how our XAI methodology can be used to explain the real-time decisions of the model, to reveal trends about the model’s general behaviour, as well as aid in the diagnosis of potential faults during the model’s development. In addition, we quantitatively validate the faithfulness of the explanations across an extensive range of XAI metrics to ensure they remain trustworthy and actionable.

In this paper, we investigate a networked control problem in the presence of Denial-of-Service (DoS) attacks, which prevent transmissions over the communication network. The communication between the process and controller is also subject to bit rate constraints. For mitigating the influences of DoS attacks and bit rate constraints, we develop a variable bit rate (VBR) encoding-decoding protocol and quantized controller to stabilize the control system. We show that the system’s resilience against DoS under VBR is preserved comparing with those under constant bit rate (CBR) quantized control, with fewer bits transmitted especially when the attack levels are low. The proposed VBR quantized control framework in this paper is general enough such that the results of CBR quantized control under DoS and moreover the results of minimum bit rate in the absence of DoS can be recovered.

Recent technological advancements have proliferated the use of small embedded devices for collecting, processing, and transferring the security-critical information. The Internet of Things (IoT) has enabled remote access and control of these network-connected devices. Consequently, an attacker can exploit security vulnerabilities and compromise these devices. In this context, the secure boot becomes a useful security mechanism to verify the integrity and authenticity of the software state of the devices. However, the current secure boot schemes focus on detecting the presence of potential malware on the device but not on disinfecting and restoring the software to a benign state. This manuscript presents CARE - the first secure boot framework that provides malicious code modification attack detection, resilience, and onboard recovery mechanism for the compromised devices. The framework uses a prototype hybrid CARE: Code Authentication and Resilience Engine to verify the integrity and authenticity of the software and restore it to a benign state. It uses Physical Memory Protection (PMP) and other security enchaining techniques of RISC-V processor to provide resilience from modern attacks. The state-of-the-art comparison and performance analysis results indicate that the proposed secure boot framework provides promising resilience and recovery mechanism with very little (8%) performance and resource overhead.

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

With the objective to eliminate the input current sensor in a totem-pole boost power factor corrector (PFC) for its low-cost design, a novel discretized sampling-based robust control scheme is proposed in this work. The proposed control methodology proves to be beneficial due to its ease of implementation and its ability to support high-frequency operation, while being able to eliminate one sensor and, thus, enhancing reliability and cost-effectiveness. In addition, detailed closed-loop stability analysis is carried out for the controller in discrete domain to ascertain brisk dynamic operation when subjected to sudden load fluctuations. To establish the robustness of the proposed control scheme, a detailed sensitivity analysis of the closed-loop performance metrics with respect to undesired changes and inherent uncertainty in system parameters is presented in this article. A comparison with the state-of-the-art (SOA) methods is provided, and conclusive results in terms of better dynamic performance are also established. To verify and elaborate on the specifics of the proposed scheme, a detailed simulation study is conducted, and the results show 25% reduction in response time as compared to SOA approaches. A 500-W boost PFC prototype is developed and tested with the proposed control scheme to evaluate and benchmark the system steady-state and dynamic performance. A total harmonic distortion of 1.68% is obtained at the rated load with a resultant power factor of 0.998 (lag), which proves the effectiveness and superiority of the proposed control scheme.

Efficient low-power accelerators for Convolutional Neural Networks (CNNs) largely benefit from quantization and approximation, which are typically applied layer-wise for efficient hardware implementation. In this work, we present a novel strategy for efficient combination of these concepts at a deeper level, which is at each channel or kernel. We first apply layer-wise, low bit-width, linear quantization and truncation-based approximate multipliers to the CNN computation. Then, based on a state-of-the-art resiliency analysis, we are able to apply a kernel-wise approximation and quantization scheme with negligible accuracy losses, without further retraining. Our proposed strategy is implemented in a specialized framework for fast design space exploration. This optimization leads to a boost in estimated power savings of up to 34% in residual CNN architectures for image classification, compared to the base quantized architecture.

In recent years, Deep Neural Networks (DNNs) have made inroads into a number of applications involving pattern recognition - from facial recognition to self-driving cars. Some of these applications, such as self-driving cars, have real-time requirements, where specialized DNN hardware accelerators help meet those requirements. Since DNN execution time is dominated by convolution, Multiply-and-Accumulate (MAC) units are at the heart of these accelerators. As hardware accelerators push the performance limits with strict power constraints, reliability is often compromised. In particular, power-constrained DNN accelerators are more vulnerable to transient and intermittent hardware faults due to particle hits, manufacturing variations, and fluctuations in power supply voltage and temperature. Methods such as hardware replication have been used to deal with these reliability problems in the past. Unfortunately, the duplication approach is untenable in a power constrained environment. This paper introduces a low-cost error-resiliency scheme that targets MAC units employed in conventional DNN accelerators. We evaluate the reliability improvements from the proposed architecture using a set of 6 CNNs over varying bit error rates (BER) and demonstrate that our proposed solution can achieve more than 99% of fault coverage with a 5-bits arithmetic code, complying with the ASIL-D level of ISO26262 standards with a negligible area and power overhead. Additionally, we evaluate the proposed detection mechanism coupled with a word masking correction scheme, demonstrating no loss of accuracy up to a BER of 10-2.

Software and firmware vulnerabilities pose security threats to photovoltaic (PV) systems. When patches are not available or cannot be timely applied to fix vulnerabilities, it is important to mitigate vulnerabilities such that they cannot be exploited by attackers or their impacts will be limited when exploited. However, the vulnerability mitigation problem for PV systems has received little attention. This paper analyzes known security vulnerabilities in PV systems, proposes a multi-level mitigation framework and various mitigation strategies including neural network-based attack detection inside inverters, and develops a prototype system as a proof-of-concept for building vulnerability mitigation into PV system design.

Privacy protection is a vital part of information security. However, the excessive collections and uses of personal information have intensified in the area of mobile apps (applications). To comprehend the current situation of APP personal information security problem of APP, this paper uses a combined approach of static analysis technology, dynamic analysis technology, and manual review to detect and analyze the installed file of mobile apps. 40 mobile apps are detected as experimental samples. The results demonstrate that this combined approach can effectively detect various issues of personal information security problem in mobile apps. Statistics analysis of the experimental results demonstrate that mobile apps have outstanding problems in some aspects of personal information security such as privacy policy, permission application, information collection, data storage, etc.

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.

Vehicular ad-hoc networks (VANETs) are one of the most stochastic networks in terms of node movement patterns. Due to the high speed of vehicles, nodes form temporary clusters and shift between clusters rapidly, which limits the usable computational complexity for quality of service (QoS) and security enhancements. Hence, VANETs are one of the most insecure networks and are prone to various attacks like Masquerading, Distributed Denial of Service (DDoS) etc. Various algorithms have been proposed to safeguard VANETs against these attacks, which vary concerning security and QoS performance. These algorithms include linear rule-checking models, software-defined network (SDN) rules, blockchain-based models, etc. Due to such a wide variety of model availability, it becomes difficult for VANET designers to select the most optimum security framework for the network deployment. To reduce the complexity of this selection, the paper reviews statistically investigate a wide variety of modern VANET-based security models. These models are compared in terms of security, computational complexity, application and cost of deployment, etc. which will assist network designers to select the most optimum models for their application. Moreover, the paper also recommends various improvements that can be applied to the reviewed models, to further optimize their performance.

A modular static analysis decomposes a program's analysis into analyses of its parts, or components. An intercomponent analysis instructs an intra-component analysis to analyse each component independently of the others. Additional analyses are scheduled for newly discovered components, and for dependent components that need to account for newly discovered component information. Modular static analyses are scalable, can be tuned to a high precision, and support the analysis of programs that are highly dynamic, featuring e.g., higher-order functions or dynamically allocated processes.In this paper, we present the engineering aspects of MAF, a static analysis framework for implementing modular analyses for higher-order languages. For any such modular analysis, the framework provides a reusable inter-component analysis and it suffices to implement its intra-component analysis. The intracomponent analysis can be composed from several interdependent and reusable Scala traits. This design facilitates changing the analysed language, as well as the analysis precision with minimal effort. We illustrate the use of MAF through its instantiation for several different analyses of Scheme programs.

Physical layer security has gained importance with the widespread use of wireless communication systems. Multiantenna systems and multi-point transmission techniques in 5G and beyond are promising techniques not only for enhancing data rates, but also physical layer security. Coordinated multipoint transmission is used for enhancing the service quality and decreasing inter-cell interference especially for cell-edge users. In this study, analysis of physical layer security enhancement via multi-antenna technologies and coordinated multi-point for 5G and beyond networks is provided. The proposed scheme is evaluated on calculations from real-life mobile network topologies. As a figure of performance, the secure and successful detection probability is computed with varying antenna array size, number of coordinated transmission points, and different service requirements.

In this paper, physical layer security (PLS) analysis of a cooperative wireless communication system in which the source and destination nodes communicate via a relay employing decode-and-forward protocol is performed for double Rayleigh fading channel model. For the system where the source, relay and target have single antenna, an eavesdropper with multiantenna listens the source and relay together by using maximum-ratio-combining, secrecy outage and positive secrecy capacity possibilities are obtained in closed-form. The theoretical results are verified by Monte-Carlo simulations. From the results, it is observed that as the number of antennas of the eavesdropper is increased, the PLS performance of the system worsens.

This paper explores a practical approach to securing large wireless networks by applying Physical Layer Security (PLS). To date, PLS has mostly been seen as an information theory concept with few practical implementations. We present an Access Point (AP) selection algorithm that uses PLS to find an AP that offers the highest secrecy capacity to a legitimate user. We then propose an implementation of this algorithm using the novel concept of spectrum programming which extends Software-Defined Networking to the physical and data-link layers and makes wireless network management and control more flexible and scalable than traditional platforms. Our Wi-Fi network evaluation results show that our approach outperforms conventional solutions in terms of security, but at the expense of communication capacity, thus identifying a trade-off between security and performance. These results encourage implementation and extension to further wireless technologies.