Dynamic Security Research Articles

Efficiency of risk management for tunnel security of megaprojects construction in China based on system dynamics

ABSTRACT There have been repeated security incidents in megaproject tunnels, construction security issues are particularly serious, and the management of security risks is receiving increasing attention. This study was conducted to improve the efficiency of tunnel security risk management, the security risk management process was adopted as an entry point, based on the Observe-Orient-Decision-Action (OODA) model, the influencing factors index system for the efficiency of security risk management in megaproject tunnels from four dimensions: observe, orient, decision and action. The AHP-EWM method is applied to assign index weights to model the system dynamics of security risk management efficiency. The Vensim software was used to simulate and analyse the effect of different indices on the efficiency of security risk management under different conditions, which provides a reference for improving the efficiency of security risk management. The results of the study show that building a comprehensive and uninterrupted risk information awareness network, real-time adaptation of risk control strategies based on the complexity of risks, and shortening risk management delays can effectively increase the efficiency of dynamic security risk management. This provides a theoretical and practical reference for effectively improving the efficiency of risk management for tunnel security in megaproject construction.

Enhanced Adaptable and Distributed Access Control Decision Making Model Based on Machine Learning for Policy Conflict Resolution in BYOD Environment

Organisations are adopting new IT strategies such as “Bring Your Own Device” (BYOD) and remote working. These trends are highly beneficial both for enterprise owners and employees in terms of increased productivity and reduced costs. However, security issues such as unauthorised access as well as privacy concerns pose significant obstacles. These can be overcome by adopting access control techniques and a dynamic security and privacy policy that governs these issues where they arise. Policy decision points in traditional access control systems, such as role-based access control (RBAC), attribute-based access control (ABAC), or relationship-based access control (ReBAC), may be limited because the status of access control can vary in response to minor changes in user and resource properties. As a result, system administrators rely on a solution for constructing complex rules with many conditions and permissions for decision control. This results in access control issues, including policy conflicts, decision-making bottlenecks, delayed access response times and mediocre performance. This paper proposes a policy decision-making and access control-based supervised learning algorithm. The algorithm enhances policy decision points (PDPs). This is achieved by transforming the PDP’s problem into a binary classification for security access control that either grants or denies access requests. Also, a vector decision classifier based on the supervised machine learning algorithm is developed to generate an accurate, effective, distributed and dynamic policy decision point (PDP). Performance was evaluated using the Kaggle-Amazon access control policy dataset, which compared the effectiveness of the proposed mechanism to previous research benchmarks in terms of performance, time and flexibility. The proposed solution obtains a high level of privacy for access control policies because the PDP does not communicate directly with the policy administration point (PAP). In conclusion, PDP-based ML generates accurate decisions and can simultaneously fulfill multiple massive policies and huge access requests with 95% Accuracy in a short response time of around 0.15 s without policy conflicts. Access control security is improved by making it dynamic, adaptable, flexible and distributed.

Network security risk detection method for smart microgrid monitoring system

AbstractAlthough the smart microgrid improves the power quality of the power system, it still has some security risks. So, this paper proposes a network security risk detection method of an intelligent microgrid monitoring system based on the artificial immune algorithm. By analyzing the structure of a smart microgrid monitoring system and introducing Policy Protection Detection Response (P2DR) dynamic network security model and ale static network security analysis method, the network security risk of the microgrid monitoring system is evaluated. The theoretical model is analyzed by the overall framework of the artificial immune algorithm, and its term definition is given. The mutation self is detected by self‐dynamic description and immune cell evolution process with the network structure brought into and the risk of the computing network facing different attack subjects is analyzed. This method has a high detection accuracy for more than 10 kinds of network attack modes, the highest detection accuracy is 98%, and the average time is only 0.8 s. Compared with other methods, this method has higher detection accuracy and faster detection speed. It is universal in various network attack modes and can detect the possible network security risks of the intelligent microgrid monitoring system and protect its safe and stable operation.

S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions

The Social Internet of Things (SIoT) paradigm combines the benefits of social networks with IoT networks to create more collaborative and efficient systems, offering enhanced scalability, better navigability, flexibility, and dynamic decision making. However, SIoT also presents challenges related to dynamic friendship selection, privacy and security, interoperability, and standardization. To fully unlock the potential of SIoT, it is crucial to establish semantic interoperability between the various entities, applications, and networks that comprise the system. This paper introduces the Semantic Social Network of Things Middleware (S2NetM), which leverages social relationships to enhance semantic interoperability in SIoT systems. The S2NetM employs semantic reasoning and alignment techniques to facilitate the creation of dynamic, context-aware social networks of things that can collaboratively work together and enable new opportunities for IoT-based solutions. The main contributions of this paper are the specification of the S2NetM and the associated ontology, as well as the discussion of a case study demonstrating the effectiveness of the proposed solution.

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.

Dynamic Heterogeneous Redundancy-Based Joint Safety and Security for Connected Automated Vehicles: Preliminary Simulation and Field Test Results

For connected automated vehicles (CAVs), safety and security are two interrelated critical issues since many in-vehicle components are both safety critical and security critical. To achieve both safety and security in the presence of functional failures or cyberattacks, this article proposes a dynamic heterogeneous redundancy (DHR) scheme for CAVs. The basic idea is that each safety- and security-critical in-vehicle component should employ a DHR architecture, which is constructed by multiple heterogeneous executors with the same function. With redundancy, the functional safety can be achieved when one executor fails. Meanwhile, based on the principle that the probability is extremely low that two or more heterogeneous executors with the same function will fail for the same vulnerability, security can be ensured by using simple consensus mechanisms to detect abnormal executors caused by any cyberattacks. A DHR prototype has been designed and installed on an automated bus. Test results show that the proposed DHR is effective in enhancing both safety and security for CAVs.

A Cyber-Physical Power System Risk Assessment Model Against Cyberattacks

With the integration of cyber devices into power systems, such as information communication technology devices and intelligent electronic devices, there are currently a large number of software vulnerabilities (SVs) in cyber devices, which are probably used to obtain the authority of cyber devices for malicious attackers and be carried out subsequently severe attacks on power systems. Thus, it is inappropriate to only consider the power risks as the risk assessment for the system with the strong coupling between cyber systems and power systems. In this article, we investigate a dynamic risk assessment model for the cyber-physical power system (CPPS) against cyberattacks, combining the network security vulnerability of the supervisory control and data acquisition (SCADA) system in a substation and physical consequences in power systems caused by it under malicious control. By construction, a dynamic network security vulnerability assessment method for the SCADA system is well studied by considering SVs and deployed network security defense measures, which is also corrected by the propagation characteristics. Moreover, we estimate physical consequences by minimum shedding loads in N-1 contingencies caused by the SCADA system in a substation under malicious control. Finally, simulations on modified IEEE 14-bus and IEEE 118-bus systems verify the proposed model's effectiveness, highlighting the importance of network security in CPPS.

Exploring the Nexus between Agricultural Seasonal Dynamics and Rural Household Food Security in Kilombero Wetland, Morogoro Region, Tanzania

It is recognized that wetlands have great potential for enhancing agricultural activities due to their prolonged periods of water availability and good fertile soils making great expansion and intensification of agricultural production enhancing household food security in rural areas hence, communities have been relying on those wetlands for their livelihoods in different ways. It is from this interdependence that this study was proposed to examine the seasonal patterns of agriculture in the wetland and their implications on food security in three villages, Njage, Mngeta and Mkangawalo in the Kilombero wetland. The study employed mixed method approach whereby household questionnaires,-key informant interviews, focus group discussions, field observations, wealth ranking and documentary review were used to collect primary and secondary data. Purposive and simple random sampling techniques were used to obtain 150 respondents. Content analysis was used to analyze qualitative data while descriptive statistics were used to analyze quantitative data. Chi-square test was used to test the statistical significances of the differences between wetland and upland yields as well as size of wetland plots cultivated among socio-economic groups. The findings revealed that wetland users were the average wealthy (42.7%) and poor farmers (40.7%) who depended on the wetland for their survival. Crop production in Kilombero wetland was low as farmers practiced mono-cropping of rice and maize in both dry and wet seasons depending on rain-fed agriculture. Very few wealthy (8.7%) practiced dry season farming. The small size of the wetland plots cultivated, inadequate use of agro-inputs and over dependence on subsistence led to low crop production. Consequently, farmers experienced seasonal food shortages and food insecurity, particularly among the poor and very poor households. The study concludes that, despite its agricultural potential, Kilombero wetland had not contributed much to improve the food security status of the poor and very poor households, only the well-off and average wealthy were benefiting from the wetland. Therefore, Kilombero district authorities should ensure that village governments allocate adequate land for both the poor and very poor farmers to enable them increase crop production. The agricultural extension officials of Kilombero district should build capacity to farmers on the importance of intensive agriculture, use of improved seeds and use of a variety of cropping methods in order to improve their produce.

Secure Cluster-based Routing using TCSA and Hybrid Security Algorithm for WSN

Wireless Sensor Network (WSN) is operated as a medium to connect the physical and information network of Internet-of-Things (IoT). Energy and trust are two key factors that assist reliable communication over the WSN-IoT. Secure data transmission is considered a challenging task during multipath routing over the WSN-IoT. To address the aforementioned issue, secure routing is developed over the WSN-IoT. In this paper, the Trust-based Crow Search Algorithm (TCSA) is developed to identify the Secure Cluster Heads (SCHs) and secure paths over the network. Further, data security while broadcasting the data packets is enhanced by developing the Hybrid Security Algorithm (HSA). This HSA is a combination of the Advanced Encryption Standard (AES) and Hill Cipher Algorithm (HCA). Therefore, the developed TCSA-HSA avoids malicious nodes during communication which helps to improve data delivery and energy consumption. The performance of the TCSA-HSA method is analyzed using Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, End to End Delay (EED), and throughput. The existing methods namely Optimal Privacy-Multihop Dynamic Clustering Routing Protocol (OP-MDCRP) and Secure and Energy-aware Heuristic-based Routing (SEHR) are used to evaluate the TCSA-HSA performances. The PDR of TCSA-HSA for 100 nodes is 99.7449%, which is high when compared to the OP-MDCRP and SEHR.

ПСИХОЛОГО-ПЕДАГОГІЧНИЙ АСПЕКТ БЕЗПЕЧНОЇ ДІЯЛЬНОСТІ ПЕРСОНАЛУ ДКВС УКРАЇНИ

The article deals with the psychological and pedagogical aspect of the safe activity of the personnel of the State Criminal and executive Service of Ukraine within penitentiary institutions and pre-trial detention centers. The safe activity of personnel in penitentiary institutions cannot but be an irrelevant topic, as it may affect the preservation of mental and physical health and life of personnel, convicts, prisoners and persons who visit institutions for one reason or another. The object of our study is the psychological and pedagogical angle of this issue and the ways it can influence the improvement of security in penitentiary institutions. The purpose of the article is to reveal the psychological and pedagogical aspect of safe activity, to study the main factors of their influence on the safe activity of the personnel of the State Criminal and Executive Service of Ukraine. The author examines the concept of safe work in the State Criminal and Execution Service of Ukraine and clarifies the specifics of personnel activities from a psychological and pedagogical point of view and the difficulties that arise due to this specificity in ensuring safe work in penitentiary institutions and pre-trial detention centers. The article investigates and reveals the following: psychological and pedagogical aspects of influence on the level of safe activity of personnel and factors contributing to this process. According to the results of the study, we found that the psychological and pedagogical aspect plays a significant role in the formation and maintenance of the level of safe activity of the staff of the State Criminal and Executive Service of Ukraine, given the specifics of the penitentiary system. The author characterizes the impact of professional training and education on the level of safe activity of personnel. This aspect can be considered and specified in further research for application in practice in order to increase the level of dynamic and personal security within the institutions of the State Criminal and Executive Service of Ukraine. Key words: security, safe activity, psychological and pedagogical aspect, professional training, personnel of the State Criminal and Executive Service of Ukraine.

Vulnerability Detection Using Two-Stage Deep Learning Models

Application security is an essential part of developing modern software, as lots of attacks depend on vulnerabilities in software. The number of attacks is increasing globally due to technological advancements. Companies must include security in every stage of developing, testing, and deploying their software in order to prevent data breaches. There are several methods to detect software vulnerability Non-AI-based such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). However, these approaches have substantial false-positive and false-negative rates. On the other side, researchers have been interested in developing an AI-based vulnerability detection system employing deep learning models like BERT, BLSTM, etc. In this paper, we proposed a two-stage solution, two deep learning models were proposed for vulnerability detection in C/C++ source codes, the first stage is CNN which detects if the source code contains any vulnerability (binary classification model) and the second stage is CNN-LTSM that classifies this vulnerability into a class of 50 different types of vulnerabilities (multiclass classification model). Experiments were done on SySeVR dataset. Results show an accuracy of 99% for the first and 98% for the second stage.

Toward Online Power System Model Identification: A Deep Reinforcement Learning Approach

The rapidly increasing penetration of power electronic equipment has complicated the dynamic characteristics of modern power systems, which has promoted the development of simulation analysis methods for online dynamic security analysis (DSA) systems. Accurate models lay the foundation for online DSA, while the high complexity and low observability of power electronic equipment models hinder the online implementation of conventional model identification approaches. In this paper, we propose an online parameter identification (PI) framework based on deep reinforcement learning (DRL), suitable for power electronic equipment models. The DRL-based online PI formulation is proposed by integrating a model-based online PI and an improved DRL-based PI approach. On this basis, the framework for DRL-based online PI is constructed through tailored design of interfaces and training mechanisms. The DRL agent is able to learn the PI policy offline by constructing a training scenario database and the trained policy can be deployed to realize accurate online identification. Case studies showcase the application of the proposed method on a high-voltage direct current (HVDC) system. Simulation results demonstrate that the proposed method exhibits superior computational advantage, while achieving a similar level of accuracy compared to conventional methods.

Three-phase fault analysis of power system transient stability based on TCSC controller using FPA for its location

Preventing the power system from becoming overloaded and under stress to the point where it is on the verge of reaching the limits of its transient stability due to high power consumption. This paper proposed the dynamic security assessment for transient stability using Thyristor controlled series capacitor (TCSC) devices controller. The stability criteria are based on the severity indices for analyzing the fault in the power system. The index uses the time domain analysis simulation, making it easier and simpler to implement for the system operators to better prepare for potential disturbances by anticipating how power systems will behave. The optimal location and parameters setting of the TCSC was determined by Flower Pollination Algorithm (FPA) due to its higher convergence rate. The proposed index is tested on the IEEE 14-bus system and further discusses the effect of a three-phase short circuit fault, different fault locations, and fault clearing method. An analysis is done to determine how the short circuit faults on lines affect the transient voltage stability of the load bus and generator bus in the system. The findings show that by employing TCSC, the voltage drop was acceptable improved and stability margins were greatly increased.

Massively Parallel Modeling of Battery Energy Storage Systems for AC/DC Grid High-Performance Transient Simulation

Extensive integration of power electronics apparatuses complicates the modern power grid and consequently necessitates time-domain transients study for its planning and operation. In this work, a heterogeneous computing architecture utilizing the CPU and graphics processing unit (GPU) is proposed for the efficient study of interactions between a power grid network and massive utility-scale battery energy storage systems (BESSs). The device-level electromagnetic transient (EMT) simulation aiming at enhanced fidelity of the BESS is conducted simultaneously with electro-mechanical transient stability (TS) simulation which suffices system-level dynamic security assessment. Since the reservation of a large amount of energy storage units is computationally intensive for the CPU, the concurrent multi-streaming, multi-threading capability of GPU is exploited to achieve asynchronous sequential-parallel processing, so that the proposed EMT-TS co-simulation can flexibly harness all available computing resources. The multi-rate scheme is adopted for further computational burden alleviation in addition to achieving timely information exchange. It shows that the heterogeneous computation of an IEEE 118-bus system integrated with a substantial number of distributed batteries becomes feasible following the achievement of a remarkable speedup of over 200, and the device- as well as system-level accuracy are validated by MATLAB/Simulink^® and DSATools^TM/TSAT simulation, respectively.

Zero trust architecture in IAM with AI integration

ZTA is a new model of enterprise cybersecurity that stands for continuous authentication, strict access control and dynamic verification for data, assets and identities of enterprises in multilayered infrastructures. ZTA ensures that each and every incoming request to access people, devices or applications is constantly compared to the organisation’s policy and approved, no matter if it is coming inside or outside a network, unlike a traditional perimeter-based security strategy. In an effort to enhance the compliance standards and lower the attack surface, this article expands on the basic tenets of ZTA, such as adding IAM to regulate access based on the role and permission systems. ZTA is further enhanced by AI that enables the real-time identification of threats, dynamic security access control, and risk estimation. By adopting the AI approach, ZTA can make intelligent decisions, and this makes companies able to counteract the ever-incoming cyber threats. Among the areas where ZTA and AI applications are being adopted are cloud environments, remote workforces, IoT devices, and microservices. Further research prospects are also enumerated in this document, such as developing AI-based behavioural analytics, safeguarding Edge and IoT applications, incorporating improved threat intelligence, and incorporating machine learning in ZTA processes. These developments will keep ZTA as a strong and adaptive cybersecurity framework for any corporate infrastructures and protect them from threats in related modern digital landscapes.

ОКРЕМІ ПИТАННЯ КЛАСИФІКАЦІЇ ЗАСУДЖЕНИХ ДО ПОКАРАННЯ У ВИДІ ПОЗБАВЛЕННЯ ВОЛІ НА ПЕВНИЙ СТРОК

Classification of convicts as one of the main elements affecting the application of the main means of convicts’ correction and resocialization is considered in the article. Criminal’s identity, their personality, social and psychological structure are examined in the article. The criteria of penitentiary influence that ensure the solution of the goals and tasks of the Criminal and Executive System, are also given. The authors note that the classification of convicts can be defined as based on the law, the requirements of Psychology and Pedagogics, as well as the practice of dividing persons deprived of liberty according to a court verdict into more or less homogeneous groups based on the level of social and moral neglect of their personality, as well as the level of social danger and motives of the crime committed by them for the purpose of their separate detention both in various penal institutions and within them in separate local and structural precincts. Special objectives of the classification of persons sentenced to imprisonment for a certain period of time are given, such as: preventing possible negative influence of different groups of convicts on each other, as well as preventing negative influence of convicts with a clearly expressed criminal antisocial position on other convicts; reducing the influence of the criminal subculture on convict’s personality; ensuring safe conditions of stay in penal institutions; construction of a rational, modern system of penal institutions; organization of convicts’ protection and supervision taking into account the requirements of the theory of physical and dynamic security; etc. The authors considered the European Prison Rules that specify the requirements for the separate detention of different categories of convicts, taking into account their safety and protection, as well as the corresponding regime. Key words: convict, legal status, punishment, classifications, penal institution, criminal offense, correction and resocialization.

A Dynamic Sharding Model Aware Security and Scalability in Blockchain

A Dynamic Sharding Model Aware Security and Scalability in Blockchain

Dynamic stochastic game-based security of edge computing based on blockchain

Dynamic stochastic game-based security of edge computing based on blockchain

New Energy Power System Dynamic Security and Stability Region Calculation Based on AVURPSO-RLS Hybrid Algorithm

Using a high proportion of new energy is becoming the development trend of the modern power industry, with broad application prospects and potential threats to power system operation safety. This paper proposes a hybrid adaptive velocity update relaxation particle swarm optimization algorithm (AVURPSO) and recursive least square (RLS) method to quickly estimate the DSSR boundary using hyper-plane expression. Firstly, the operating point data in the high-dimension nodal injection space are analyzed using the AVURPSO algorithm to identify the key generators, equivalent search space, and critical points, which have relatively great effects on transient angle stability. The hyper-plane expression of the DSSR boundary, which matches the critical points best, is finally fitted by the RLS approach. Hence, the adopted algorithm is applied to rapidly approximate the DSSR boundary by hyper-plane expression in power injection spaces. Finally, the proposed algorithm is validated using a simulation case study on three wind farm regions of the actual Hami Power Grid of China using the DIgSILENT/Power Factory software. Consequently, the mentioned method effectively captures the security stability boundary of the new energy power system and realizes the three-dimensional visualization space of DSSR. By leveraging the DSSR, the state analysis can be conducted rapidly on several parameters, including security and stability assessments in relation to various energy supply capabilities. Meanwhile, these indices are calculated offline and applied online. The findings of this investigation confirm the efficacy and accuracy of the suggested modeling used in the analyzed system, offering technical assistance ensuring the stability of the new energy power system. The DSSR allows the rapid analysis of several parameters, including security and stability assessments with various energy supply capabilities.

Investigating the energy security performance, productivity, and economic growth for the EU

AbstractEnergy is one of the key elements of economies. The maintenance of daily economic activities would not be possible without assurance of energy security, which is also a critical contributor to sustainable energy. The European Union (EU) strives to protect the energy security of its members in order to prevent supply problems or crises. The main purpose of our analysis is to evaluate the energy security of EU countries by analyzing their performance, productivity, and relationship with economic growth. Using hybrid and comparative MCDM and Malmquist productivity approaches, we assess the static and dynamic energy security performance of the EU countries in the period from 2014 to 2018. We use IDOCRIW‐weighted SAW, MARCOS, and CODAS techniques for static performance measurement and the Malmquist productivity approach for dynamic performance evaluation. Considering both static and dynamic performance among developed EU countries, Denmark is found to be superior. Further, the Malmquist productivity index results indicate a slight improvement for developed countries and deterioration for developing countries. According to the panel data analysis, energy security is a factor that leads to economic growth. Consequently, energy efficiency initiatives such as efficiency action plans, hybrid and electric vehicles, and energy storage advancements play crucial roles in enhancing the EU's energy security.