Visible to the public Biblio

Found 5879 results

Filters: Keyword is composability  [Clear All Filters]
2023-05-12
Power, Conor, Staszewski, Robert Bogdan, Blokhina, Elena.  2022.  Cryogenic Transistor Confinement Well Simulation through Material and Carrier Transport Decoupling. 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). :1–2.
We propose a methodology for the simulation of electrostatic confinement wells in transistors at cryogenic temperatures. This is considered in the context of 22-nm fully depleted silicon-on-insulator transistors due to their potential for imple-menting quantum bits in scalable quantum computing systems. To overcome thermal fluctuations and improve decoherence times in most quantum bit implementations, they must be operated at cryogenic temperatures. We review the dominant sources of electric field at these low temperatures, including material interface work function differences and trapped interface charges. Intrinsic generation and dopant ionisation are shown to be negligible at cryogenic temperatures when using a mode of operation suitable for confinement. We propose studying cryogenic electrostatic confinement wells in transistors using a finite-element model simulation, and decoupling carrier transport generated fields.
Liu, Pan, Tang, Zhangchun, Gao, Qiang, Xiong, Wenbin.  2022.  Physical Design of Local-volume Ignition for Inertial Confinement Fusion. 2022 International Conference on Applied Physics and Computing (ICAPC). :94–99.
Inertial Confinement Fusion(ICF) uses the inertia of the substance itself to confine the nest-temperature thermonuclear fuel plasma to achieve thermonuclear fusion and obtain fusion energy. In the design of the local-volume ignition target capsule, the ignition zone and the main combustion zone are separated by heavy medium. The ignition zone is located in the center of the system (the part of the fusion combustion). The mass is small and can be compressed to high density and the overall temperature is raised to the ignition state (local-volume ignition). The temperature increase and density increase of the local volume ignition are relatively decoupled in time. The multi-step enhanced shock wave heats the fuel temperature drop, after which the collision effect accelerates the metal shell layer by layer, and uses the inertia of high-Z metal shell with a larger residual mass to achieve effective compression of the fuel areal after the driving source ends for a long time. Local volume ignition has the advantages of no need to reshape the radiation driving pulse, resistance to the influence of hot electrons, less demanding compression symmetry, and large combustion gain.
Provencher, C. M., Johnson, A. J., Carroll, E. G., Povilus, A. P., Javedani, J., Stygar, W. A., Kozioziemski, B. J., Moody, J. D., Tang, V..  2022.  A Pulsed Power Design Optimization Code for Magnetized Inertial Confinement Fusion Experiments at the National Ignition Facility. 2022 IEEE International Conference on Plasma Science (ICOPS). :1–1.
The MagNIF team at LLNL is developing a pulsed power platform to enable magnetized inertial confinement fusion and high energy density experiments at the National Ignition Facility. A pulsed solenoidal driver capable of premagnetizing fusion fuel to 40T is predicted to increase performance of indirect drive implosions. We have written a specialized Python code suite to support the delivery of a practical design optimized for target magnetization and risk mitigation. The code simulates pulsed power in parameterized system designs and converges to high-performance candidates compliant with evolving engineering constraints, such as scale, mass, diagnostic access, mechanical displacement, thermal energy deposition, facility standards, and component-specific failure modes. The physics resolution and associated computational costs of our code are intermediate between those of 0D circuit codes and 3D magnetohydrodynamic codes, to be predictive and support fast, parallel simulations in parameter space. Development of a reduced-order, physics-based target model is driven by high-resolution simulations in ALE3D (an institutional multiphysics code) and multi-diagnostic data from a commissioned pulser platform. Results indicate system performance is sensitive to transient target response, which should include magnetohydrodynamic expansion, resistive heating, nonlinear magnetic diffusion, and phase change. Design optimization results for a conceptual NIF platform are reported.
ISSN: 2576-7208
Carroll, E. G., Bracamontes, G., Piston, K., James, G. F., Provencher, C. M., Javedani, J., Stygar, W. A., Povilus, A. P., Vonhof, S., Yanagisawa, D. K. et al..  2022.  A New Pulsed Power System for Generating Up To 40t Magnetic Seeds Fields for Cryogenic Inertial Confinement Fusion Experiments on The National Ignition Facility. 2022 IEEE International Conference on Plasma Science (ICOPS). :1–1.
A new pulse power system is being developed with the goal of generating up to 40T seed magnetic fields for increasing the fusion yield of indirect drive inertial confinement fusion (ICF) experiments on the National Ignition Facility. This pulser is located outside of the target chamber and delivers a current pulse to the target through a coaxial cable bundle and custom flex-circuit strip-lines integrated into a cryogenic target positioner. At the target, the current passes through a multi-turn solenoid wrapped around the outside of a hohlraum and is insulated with Kapton coating. A 11.33 uF capacitor, charged up to 40 kV and switched by spark-gap, drives up to 40 kA of current before the coil disassembles. A custom Python design optimization code was written to maximize peak magnetic field strength while balancing competing pulser, load and facility constraints. Additionally, using an institutional multi-physics code, ALE3D, simulations that include coil dynamics such as temperature dependent resistance, coil forces and motion, and magnetic diffusion were conducted for detailed analysis of target coils. First experiments are reported as well as comparisons with current modelling efforts.
ISSN: 2576-7208
Chen, Haojie, Rao, Bo, Zhou, Song, Liang, Yunfeng, Li, Yangbo, Ren, Zhengkang, Mao, Feiyue, Zhao, Chuanxu, Li, Shuhao, Hu, Bo et al..  2022.  The installation of the island divertor coils on the J–TEXT tokamak. 2022 IEEE 5th International Electrical and Energy Conference (CIEEC). :2808–2811.
In order to investigate the effect of island divertor on the peak heat load reduction in a tokamak, a new island divertor was developed and installed in J-TEXT tokamak. The engineering design takes into account the complexity of the device based on the physical design, and also needs to ensure the insulation performance of the coil. Before installing the coil, electromagnetic forces on conductors and thermal conditions were simulated, the electromagnetic force on the magnetic island divertor coil will not cause damage to the coil, and there will be no thermal failure behavior.
Chen, C., Becker, J. R., Farrell, J. J..  2022.  Energy Confinement Time in a Magnetically Confined Thermonuclear Fusion Reactor. 2022 IEEE International Conference on Plasma Science (ICOPS). :1–1.
The single most important scientific question in fusion research may be confinement in a fusion plasma [1] . A recently-developed theoretical model [2] is reviewed for the confinement time of ion kinetic energy in a material where fusion reactions occur. In the theoretical model where ion stopping was considered as a key mechanism for ion kinetic energy loss, an estimate was obtained for the confinement time of ion kinetic energy in a D-T plasma - and found to be orders of magnitude lower than required in the Lawson criterion. As ions transfer their kinetic energies to electrons via ion stopping and thermalization between the ions and the electrons takes place, spontaneous electron cyclotron radiation is identified as a key mechanism for electron kinetic energy loss in a magnetically confined plasma. The energy confinement time is obtained and found in agreement with measurements from TFTR [1] and Wendelstein 7-X [3] . An advanced Lawson criterion is obtained for a magnetically confined thermonuclear fusion reactor.
ISSN: 2576-7208
Reid, R., Smith, J. R..  2022.  Revisiting Centrifugal Confinement for high Temperature Plasmas. 2022 IEEE International Conference on Plasma Science (ICOPS). :1–2.
Traditional magnetic mirrors are appealing because of their comparably simple geometry which lends itself to cost-effective construction. However, magnetic mirrors suffer from several inherent problems that make them poor choices for confining and heating plasmas. The chief concerns are the loss-cone instability which continuously saps hot particles from the trap and the interchange instability which effectively transports hot plasma from the core of the trap to the edges where it is lost to the walls. Centrifugal confinement schemes address these concerns with the addition of supersonic poloidal rotation which can effectively shut off the loss-cone. In addition, velocity shear in the flow may mitigate or even turn off the interchange instability if high enough rotation speeds can be achieved. Previous experiments have verified the efficacy of centrifugal confinement but have been unable to achieve sufficient rotation velocities to entirely shut down the interchange modes. [1] The rotation velocity in these experiments was limited by the Critical-Ionization-Velocity (CIV) instability. [3] We plan an experiment to verify that the CIV is the limiting factor in supersonic plasma centrifuges and to explore strategies for avoiding the CIV limit and achieving sufficient rotation speeds to enable stable plasma confinement.
ISSN: 2576-7208
2023-05-11
Jawdeh, Shaya Abou, Choi, Seungdeog, Liu, Chung-Hung.  2022.  Model-Based Deep Learning for Cyber-Attack Detection in Electric Drive Systems. 2022 IEEE Applied Power Electronics Conference and Exposition (APEC). :567–573.
Modern cyber-physical systems that comprise controlled power electronics are becoming more internet-of-things-enabled and vulnerable to cyber-attacks. Therefore, hardening those systems against cyber-attacks becomes an emerging need. In this paper, a model-based deep learning cyber-attack detection to protect electric drive systems from cyber-attacks on the physical level is proposed. The approach combines the model physics with a deep learning-based classifier. The combination of model-based and deep learning will enable more accurate cyber-attack detection results. The proposed cyber-attack detector will be trained and simulated on a PM based electric drive system to detect false data injection attacks on the drive controller command and sensor signals.
ISSN: 2470-6647
Li, Hongwei, Chasaki, Danai.  2022.  Network-Based Machine Learning Detection of Covert Channel Attacks on Cyber-Physical Systems. 2022 IEEE 20th International Conference on Industrial Informatics (INDIN). :195–201.
Most of the recent high-profile attacks targeting cyber-physical systems (CPS) started with lengthy reconnaissance periods that enabled attackers to gain in-depth understanding of the victim’s environment. To simulate these stealthy attacks, several covert channel tools have been published and proven effective in their ability to blend into existing CPS communication streams and have the capability for data exfiltration and command injection.In this paper, we report a novel machine learning feature engineering and data processing pipeline for the detection of covert channel attacks on CPS systems with real-time detection throughput. The system also operates at the network layer without requiring physical system domain-specific state modeling, such as voltage levels in a power generation system. We not only demonstrate the effectiveness of using TCP payload entropy as engineered features and the technique of grouping information into network flows, but also pitch the proposed detector against scenarios employing advanced evasion tactics, and still achieve above 99% detection performance.
Karayat, Ritik, Jadhav, Manish, Kondaka, Lakshmi Sudha, Nambiar, Ashwath.  2022.  Web Application Penetration Testing & Patch Development Using Kali Linux. 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS). 1:1392–1397.
Nowadays, safety is a first-rate subject for all applications. There has been an exponential growth year by year in the number of businesses going digital since the few decades following the birth of the Internet. In these technologically advanced times, cyber security is a must mainly for internet applications, so we have the notion of diving deeper into the Cyber security domain and are determined to make a complete project. We aim to develop a website portal for ease of communication between us and the end user. Utilizing the power of python scripting and flask server to make independent automated tools for detection of SQLI, XSS & a Spider(Content Discovery Tool). We have also integrated skipfish as a website vulnerability scanner to our project using python and Kali Linux. Since conducting a penetration test on another website without permission is not legal, we thought of building a dummy website prone to OS Command Injection in addition to the above-mentioned attacks. A well-documented report will be generated after the penetration test/ vulnerability scan. In case the website is vulnerable, patching of the website will be done with the user's consent.
ISSN: 2575-7288
Saxena, Aditi, Arora, Akarshi, Saxena, Saumya, Kumar, Ashwni.  2022.  Detection of web attacks using machine learning based URL classification techniques. 2022 2nd International Conference on Intelligent Technologies (CONIT). :1–13.
For a long time, online attacks were regarded to pose a severe threat to web - based applications, websites, and clients. It can bypass authentication methods, steal sensitive information from datasets and clients, and also gain ultimate authority of servers. A variety of ways for safeguarding online apps have been developed and used to deal the website risks. Based on the studies about the intersection of cybersecurity and machine learning, countermeasures for identifying typical web assaults have recently been presented (ML). In order to establish a better understanding on this essential topic, it is necessary to study ML methodologies, feature extraction techniques, evaluate datasets, and performance metrics utilised in a systematic manner. In this paper, we go through web security flaws like SQLi, XSS, malicious URLs, phishing attacks, path traversal, and CMDi in detail. We also go through the existing security methods for detecting these threats using machine learning approaches for URL classification. Finally, we discuss potential research opportunities for ML and DL-based techniques in this category, based on a thorough examination of existing solutions in the literature.
Chen, Jianhua, Yang, Wenchuan, Cui, Can, Zhang, Yang.  2022.  Research and Implementation of Intelligent Detection for Deserialization Attack Traffic. 2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST). :1206–1211.
In recent years, as an important part of the Internet, web applications have gradually penetrated into life. Now enterprises, units and institutions are using web applications regardless of size. Intrusion detection to effectively identify malicious traffic has become an inevitable requirement for the development of network security technology. In addition, the proportion of deserialization vulnerabilities is increasing. Traditional intrusion detection mostly focuses on the identification of SQL injection, XSS, and command execution, and there are few studies on the identification of deserialization attack traffic. This paper use a method to extracts relevant features from the deserialized traffic or even the obfuscated deserialized traffic by reorganizing the traffic and running the relevant content through simulation, and combines deep learning technology to make judgments to efficiently identify deserialization attacks. Finally, a prototype system was designed to capture related attacks in real-world. The technology can be used in the field of malicious traffic detection and help combat Internet crimes in the future.
Zhang, Zhi Jin, Bloch, Matthieu, Saeedifard, Maryam.  2022.  Load Redistribution Attacks in Multi-Terminal DC Grids. 2022 IEEE Energy Conversion Congress and Exposition (ECCE). :1–7.
The modernization of legacy power grids relies on the prevalence of information technology (IT). While the benefits are multi-fold and include increased reliability, more accurate monitoring, etc., the reliance on IT increases the attack surface of power grids by making them vulnerable to cyber-attacks. One of the modernization paths is the emergence of multi-terminal dc systems that offer numerous advantages over traditional ac systems. Therefore, cyber-security issues surrounding dc networks need to be investigated. Contributing to this effort, a class of false data injection attacks, called load redistribution (LR) attacks, that targets dc grids is proposed. These attacks aim to compromise the system load data and lead the system operator to dispatch incorrect power flow commands that lead to adverse consequences. Although similar attacks have been recently studied for ac systems, their feasibility in the converter-based dc grids has yet to be demonstrated. Such an attack assessment is necessary because the dc grids have a much smaller control timescale and are more dependent on IT than their traditional ac counterparts. Hence, this work formulates and evaluates dc grid LR attacks by incorporating voltage-sourced converter (VSC) control strategies that appropriately delineate dc system operations. The proposed attack strategy is solved with Gurobi, and the results show that both control and system conditions can affect the success of an LR attack.
ISSN: 2329-3748
Zhu, Lei, Huang, He, Gao, Song, Han, Jun, Cai, Chao.  2022.  False Data Injection Attack Detection Method Based on Residual Distribution of State Estimation. 2022 12th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). :724–728.
While acquiring precise and intelligent state sensing and control capabilities, the cyber physical power system is constantly exposed to the potential cyber-attack threat. False data injection (FDI) attack attempts to disrupt the normal operation of the power system through the coupling of cyber side and physical side. To deal with the situation that stealthy FDI attack can bypass the bad data detection and thus trigger false commands, a system feature extraction method in state estimation is proposed, and the corresponding FDI attack detection method is presented. Based on the principles of state estimation and stealthy FDI attack, we analyze the impacts of FDI attack on measurement residual. Gaussian fitting method is used to extract the characteristic parameters of residual distribution as the system feature, and attack detection is implemented in a sliding time window by comparison. Simulation results prove that the proposed attack detection method is effectiveness and efficiency.
ISSN: 2642-6633
Teo, Jia Wei, Gunawan, Sean, Biswas, Partha P., Mashima, Daisuke.  2022.  Evaluating Synthetic Datasets for Training Machine Learning Models to Detect Malicious Commands. 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :315–321.
Electrical substations in power grid act as the critical interface points for the transmission and distribution networks. Over the years, digital technology has been integrated into the substations for remote control and automation. As a result, substations are more prone to cyber attacks and exposed to digital vulnerabilities. One of the notable cyber attack vectors is the malicious command injection, which can lead to shutting down of substations and subsequently power outages as demonstrated in Ukraine Power Plant Attack in 2015. Prevailing measures based on cyber rules (e.g., firewalls and intrusion detection systems) are often inadequate to detect advanced and stealthy attacks that use legitimate-looking measurements or control messages to cause physical damage. Additionally, defenses that use physics-based approaches (e.g., power flow simulation, state estimation, etc.) to detect malicious commands suffer from high latency. Machine learning serves as a potential solution in detecting command injection attacks with high accuracy and low latency. However, sufficient datasets are not readily available to train and evaluate the machine learning models. In this paper, focusing on this particular challenge, we discuss various approaches for the generation of synthetic data that can be used to train the machine learning models. Further, we evaluate the models trained with the synthetic data against attack datasets that simulates malicious commands injections with different levels of sophistication. Our findings show that synthetic data generated with some level of power grid domain knowledge helps train robust machine learning models against different types of attacks.
Qbea'h, Mohammad, Alrabaee, Saed, Alshraideh, Mohammad, Sabri, Khair Eddin.  2022.  Diverse Approaches Have Been Presented To Mitigate SQL Injection Attack, But It Is Still Alive: A Review. 2022 International Conference on Computer and Applications (ICCA). :1–5.
A huge amount of stored and transferred data is expanding rapidly. Therefore, managing and securing the big volume of diverse applications should have a high priority. However, Structured Query Language Injection Attack (SQLIA) is one of the most common dangerous threats in the world. Therefore, a large number of approaches and models have been presented to mitigate, detect or prevent SQL injection attack but it is still alive. Most of old and current models are created based on static, dynamic, hybrid or machine learning techniques. However, SQL injection attack still represents the highest risk in the trend of web application security risks based on several recent studies in 2021. In this paper, we present a review of the latest research dealing with SQL injection attack and its types, and demonstrating several types of most recent and current techniques, models and approaches which are used in mitigating, detecting or preventing this type of dangerous attack. Then, we explain the weaknesses and highlight the critical points missing in these techniques. As a result, we still need more efforts to make a real, novel and comprehensive solution to be able to cover all kinds of malicious SQL commands. At the end, we provide significant guidelines to follow in order to mitigate such kind of attack, and we strongly believe that these tips will help developers, decision makers, researchers and even governments to innovate solutions in the future research to stop SQLIA.
Tanaka, Tatsuki, Sugawara, Takeshi.  2022.  Laser-Based Signal-Injection Attack on Piezoresistive MEMS Pressure Sensors. 2022 IEEE Sensors. :1–4.
As more and more information systems rely sen-sors for their critical decisions, there is a growing threat of injecting false signals to sensors in the analog domain. In particular, LightCommands showed that MEMS microphones are susceptible to light, through the photoacoustic and photoelectric effects, enabling an attacker to silently inject voice commands to smart speakers. Understanding such unexpected transduction mechanisms is essential for designing secure and reliable MEMS sensors. Is there any other transduction mechanism enabling laser-induced attacks? We positively answer the question by experimentally evaluating two commercial piezoresistive MEMS pressure sensors. By shining a laser light at the piezoresistors through an air hole on the sensor package, the pressure reading changes by ±1000 hPa with 0.5 mW laser power. This phenomenon can be explained by the photoelectric effect at the piezoresistors, which increases the number of carriers and decreases the resistance. We finally show that an attacker can induce the target signal at the sensor reading by shining an amplitude-modulated laser light.
ISSN: 2168-9229
2023-04-28
Feng, Chunhua.  2022.  Discussion on the Ways of Constructing Computer Network Security in Colleges: Considering Complex Worm Networks. 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC). :1650–1653.
This article analyzes the current situation of computer network security in colleges and universities, future development trends, and the relationship between software vulnerabilities and worm outbreaks. After analyzing a server model with buffer overflow vulnerabilities, a worm implementation model based on remote buffer overflow technology is proposed. Complex networks are the medium of worm propagation. By analyzing common complex network evolution models (rule network models, ER random graph model, WS small world network model, BA scale-free network model) and network node characteristics such as extraction degree distribution, single source shortest distance, network cluster coefficient, richness coefficient, and close center coefficient.
Baksi, Rudra Prasad.  2022.  Pay or Not Pay? A Game-Theoretical Analysis of Ransomware Interactions Considering a Defender’s Deception Architecture 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). :53–54.
Malware created by the Advanced Persistent Threat (APT) groups do not typically carry out the attacks in a single stage. The “Cyber Kill Chain” framework developed by Lockheed Martin describes an APT through a seven stage life cycle [5] . APT groups are generally nation state actors [1] . They perform highly targeted attacks and do not stop until the goal is achieved [7] . Researchers are always working toward developing a system and a process to create an environment safe from APT type attacks [2] . In this paper, the threat considered is ransomware which are developed by APT groups. WannaCry is an example of a highly sophisticated ransomware created by the Lazurus group of North Korea and its level of sophistication is evident from the existence of a contingency plan of attack upon being discovered [3] [6] . The major contribution of this research is the analysis of APT type ransomware using game theory to present optimal strategies for the defender through the development of equilibrium solutions when faced with APT type ransomware attack. The goal of the equilibrium solutions is to help the defender in preparedness before the attack and in minimization of losses during and after the attack.
Deng, Zijie, Feng, Guocong, Huang, Qingshui, Zou, Hong, Zhang, Jiafa.  2022.  Research on Enterprise Information Security Risk Assessment System Based on Bayesian Neural Network. 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). :938–941.
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.
Jain, Ashima, Tripathi, Khushboo, Jatain, Aman, Chaudhary, Manju.  2022.  A Game Theory based Attacker Defender Model for IDS in Cloud Security. 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom). :190–194.

Cloud security has become a serious challenge due to increasing number of attacks day-by-day. Intrusion Detection System (IDS) requires an efficient security model for improving security in the cloud. This paper proposes a game theory based model, named as Game Theory Cloud Security Deep Neural Network (GT-CSDNN) for security in cloud. The proposed model works with the Deep Neural Network (DNN) for classification of attack and normal data. The performance of the proposed model is evaluated with CICIDS-2018 dataset. The dataset is normalized and optimal points about normal and attack data are evaluated based on the Improved Whale Algorithm (IWA). The simulation results show that the proposed model exhibits improved performance as compared with existing techniques in terms of accuracy, precision, F-score, area under the curve, False Positive Rate (FPR) and detection rate.

Dutta, Ashutosh, Hammad, Eman, Enright, Michael, Behmann, Fawzi, Chorti, Arsenia, Cheema, Ahmad, Kadio, Kassi, Urbina-Pineda, Julia, Alam, Khaled, Limam, Ahmed et al..  2022.  Security and Privacy. 2022 IEEE Future Networks World Forum (FNWF). :1–71.
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.
ISSN: 2770-7679
Iqbal, Sarfraz.  2022.  Analyzing Initial Design Theory Components for Developing Information Security Laboratories. 2022 6th International Conference on Cryptography, Security and Privacy (CSP). :36–40.
Online information security labs intended for training and facilitating hands-on learning for distance students at master’s level are not easy to develop and administer. This research focuses on analyzing the results of a DSR project for design, development, and implementation of an InfoSec lab. This research work contributes to the existing research by putting forth an initial outline of a generalized model for design theory for InfoSec labs aimed at hands-on education of students in the field of information security. The anatomy of design theory framework is used to analyze the necessary components of the anticipated design theory for InfoSec labs in future.
Wang, Man.  2022.  Research on Network Confrontation Information Security Protection System under Computer Deep Learning. 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA). :1442–1447.
Aiming at the single hopping strategy in the terminal information hopping active defense technology, a variety of heterogeneous hopping modes are introduced into the terminal information hopping system, the definition of the terminal information is expanded, and the adaptive adjustment of the hopping strategy is given. A network adversarial training simulation system is researched and designed, and related subsystems are discussed from the perspective of key technologies and their implementation, including interactive adversarial training simulation system, adversarial training simulation support software system, adversarial training simulation evaluation system and adversarial training Mock Repository. The system can provide a good environment for network confrontation theory research and network confrontation training simulation, which is of great significance.
Zhu, Yuwen, Yu, Lei.  2022.  A Modeling Method of Cyberspace Security Structure Based on Layer-Level Division. 2022 IEEE 5th International Conference on Computer and Communication Engineering Technology (CCET). :247–251.
As the cyberspace structure becomes more and more complex, the problems of dynamic network space topology, complex composition structure, large spanning space scale, and a high degree of self-organization are becoming more and more important. In this paper, we model the cyberspace elements and their dependencies by combining the knowledge of graph theory. Layer adopts a network space modeling method combining virtual and real, and level adopts a spatial iteration method. Combining the layer-level models into one, this paper proposes a fast modeling method for cyberspace security structure model with network connection relationship, hierarchical relationship, and vulnerability information as input. This method can not only clearly express the individual vulnerability constraints in the network space, but also clearly express the hierarchical relationship of the complex dependencies of network individuals. For independent network elements or independent network element groups, it has flexibility and can greatly reduce the computational complexity in later applications.