System Security Analysis Research Articles

Formal Verification and Security Analysis of Go-based New Simple Queue System

Formal Verification and Security Analysis of Go-based New Simple Queue System

DSDOS Cloud: A Decentralized Secure Data Outsourcing System With Hybrid Encryption, Blockchain Smart Contract‐Based Access Control, and Hash Authentication Codes for Cloud Security

ABSTRACTWith the increasing trend of outsourcing data to cloud services, ensuring data security and privacy has become crucial. Typically, data are stored on cloud servers in encrypted form to mitigate risks. However, accessing the encrypted data requires an access key distributed by a third party. If this third party is untrustworthy, it poses a significant security threat to the system. To address this challenge, we propose a Decentralized Secure Data Outsourcing System (DSDOS) that uses blockchain technology to ensure data security and privacy. The DSDOS system comprises three modules: data security and privacy, access control and authorization, and data integrity and availability. The data security and privacy module uses a hybrid encryption scheme that combines Advanced Encryption Standard (AES), partially homomorphic encryption (PHE), and Diffie–Hellman (DH) to ensure secure data storage and access. The access control and authorization module uses a blockchain‐based smart contract system to manage access to the encrypted data. The data integrity and availability module uses hash‐based message authentication code (HMAC) to ensure that the data are not tampered with and is always available. We conducted a security and performance analysis of the DSDOS system and found that it outperforms previous schemes in terms of security and performance. The DSDOS system is a secure and privacy‐preserving data outsourcing system that can be used to mitigate the security risks associated with traditional cloud storage systems.

Security Analysis and Implementation of a Multilevel AI-Based Authentication System for Enhanced and Faster Security on Mobile, Web-Based, and Other Devices

Security Analysis and Implementation of a Multilevel AI-Based Authentication System for Enhanced and Faster Security on Mobile, Web-Based, and Other Devices

Assessment of climate change mitigation potential of the Kosovo energy and transport sector

The Energy and transport sector in Kosovo are the main emitter of greenhouse gases (GHG) with a share of about 85% in total annual emissions. In energetic sector, 95-97% of emissions are associated with the electricity generation due to the predominant role of lignite fuelled power plants. The electricity sector contributes about 80% of total CO2 emissions in Kosovo. Those make the energy and transport sector the main emitters of CO2 and also the main target for CO2 emissions reduction. The National Energy and Climate Plan 2021-2030 aims to ensure a reliable supply of energy for Kosovo and proposes targets for renewable, energy efficiency, and greenhouse gas reductions. This paper presents analyses of different time frame scenarios for development of the Kosovo energy system in relation to climate change mitigation potential of the Kosovo energy and transport sector. The national energy system of Kosovo is modelled using EnergyPLAN software which integrates energy for electricity, transport and heat, and includes hourly fluctuations of energy demand and energy production. Three scenarios of Kosovo Energy system are modelled; starting point scenario 2019 and two long term scenarios 2030 with 23.3% and 41.8% share of renewable. An analysis of the overall energy system costs and security of supply with a main focus in electricity sector of the three scenarios is performed.

Cyber Security Analysis of a Power Distribution System Using Vulnerability Assessment and Penetration Testing Tools

Cyber Security Analysis of a Power Distribution System Using Vulnerability Assessment and Penetration Testing Tools

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Detection and analysis of network system security based on machine learning

Computer networks (CNs) has been widely popularized and applied. They not only affect people’s daily lives, but also promote the development of the times and society. However, in the era of big data (BD), the rapid growth of data poses significant challenges to computer network security management (CNSM). The large amount, fast speed, and diverse types of data generated by modern networks make it increasingly difficult for security professionals to detect and respond to threats in real-time. Artificial intelligence (AI) has the potential to play an important role in CNSM in the era of BD. It can quickly analyze large amounts of data, automate daily tasks, and predict potential network vulnerabilities. This article conducted relevant research on the development of CNSM technology based on AI technology. The final experimental results showed that the average accuracy score of network security (NS) detection based on AI technology is 92.35 points; the average overall event response time is 9.45 hours; the average cost of network security management (NSM) is 147300 US dollars. These indicators have huge advantages compared to traditional NSM technology.

Study and Analysis of Docker Internal System Security From Docker Daemon Attack and DDOS on The Open Journal System

Introduction: The growing use of data centers encourages virtualization technology to become an alternative solution in virtualization to provide a dense environment that can be adjusted according to needs. In hypervisor-based virtualization technology in managing servers in the Open Journal System data center, network administrators must allocate resources that are large enough so that when developing web or mobile systems takes a long time, and in hypervisor-based virtualization techniques, they must have access to the host kernel. Purpose: Research on the attack model and vulnerabilities of the Docker internal security system on the Open Journal System from the Docker daemon attack and DDoS attacks when building and managing the Docker internal system. Method: This research begins with a literature study, network scope, system design, and system implementation based on plans that have been made, as well as testing, analysis, and concluding the tests that have been carried out. Finding: At the testing stage, the results obtained were the success of the Docker system in handling DDoS attacks and the success of securing the Docker Daemon from Docker Daemon attacks. Conclusion: The Docker Daemon Attack can occur by misconfiguring containers. This flaw allows an unauthorised party to take control of a container that has already been created. By gaining root access, attackers can perform various malicious activities within the container. Therefore, it is important to have an understanding and implementation of proper security practices in the management and configuration of Docker containers to reduce the risk of these types of attacks. Keyword: Docker, Container, DDoS, Attack. ------------------------------------------ Studi dan Analisis Keamanan Sistem Internal Docker dari Docker Daemon Attack dan Ddos Pada Sistem Open Journal System Pendahuluan: Berkembangnya penggunaan data center ini mendorong teknologi virtualisasi menjadi salah satu alternatif solusi dalam virtualisasi untuk menyediakan lingkungan yang padat agar dapat disesuaikan sesuai kebutuhan. Pada teknologi virtualisasi berbasis hypervisor dalam pengelolaan server pada data Open Journal System, center administrator jaringan harus mengalokasikan resources yang cukup besar, sehingga ketika dilakukan development sistem web atau mobile membutuhkan waktu yang lama, serta pada teknik virtualisasi berbasis hypervisor harus memiliki akses ke kernel host. Tujuan: Meneliti model serangan dan kerentanan sistem keamanan internal Docker pada Open Journal System dari ancaman Docker Daemon Attack dan serangan DDoS pada saat membangun dan mengelola sistem internal Docker. Metode Penelitian: Penelitian ini dimulai dengan studi literatur, ruang lingkup jaringan, perancangan sistem, implementasi sistem berdasarkan rancangan yang telah dibuat, serta pengujian, analisis dan penarikan kesimpulan dari pengujian yang telah dilakukan. Hasil Penelitian: Pada tahap pengujian dilakukan hasil yang didapatkan yaitu keberhasilan sistem docker dalam menangani serangan DdoS serta keberhasilan pengamanan docker daemon dari serangan docker daemon attack. Kesimpulan: Serangan Docker Daemon dapat terjadi karena kesalahan konfigurasi container. Kelemahan ini memungkinkan pihak yang tidak berwenang untuk mengambil kendali atas container yang telah dibuat. Dengan mendapatkan akses root, penyerang dapat melakukan berbagai aktivitas berbahaya di dalam container. Oleh karena itu, penting untuk memiliki pemahaman dan penerapan praktik keamanan yang tepat dalam pengelolaan dan konfigurasi container Docker untuk mengurangi risiko jenis serangan ini. Kata Kunci: Docker, Container, DDoS, Attack.

Integrated energy cyber–physical system fusion modeling and operation state analysis under the cooperative attack

With the increasingly close coupling between the cyber system and the physical system, cyber attacks have a significant impact on the physical system by attacking the cyber system. As a cyber–physical system with multiple energy sources, integrated energy cyber–physical system (IECPS) is facing the risk of cyber attacks. However, the research on IECPS is still in the direction of operation optimization at the physical level, which cannot meet the requirements of system security and stability analysis under cyber cooperative attacks. Firstly, this paper analyzes the interaction mechanism between the energy network composed of electricity, heat and gas and the cyber network in IECPS, and put forward a layered modeling method of IECPS to analyze the influence of abnormal information flow caused by cyber attacks on energy flow. Secondly, based on the energy circuit method (ECM), a hybrid calculation method of IECPS energy-information flow is proposed to solve the model. Then, the load shedding state of IECPS and the tampering process of system control commands after the fake data injection attack are analyzed by using the proposed calculation method. Finally, the simulation analysis is carried out in an integrated energy system composed of IEEE 39-node power grid, 6-node heating network and 7-node gas network. The results show that, compared with the traditional Newton–Raphson method, the proposed IECPS hybrid calculation method can better obtain the change results of energy flow in the steady-state scenario and the cyber cooperative attack scenario, and identify the high-risk branches in the system, which is of great significance to the safe and stable operation of the energy system.

The role of data-driven methods in power system security assessment from aggregated grid data

Data-driven techniques have been considered as an enabling technology for reducing the computational burden of both static and dynamic power system security analysis. Anyway, the studies reported in the literature mainly focused on inferring from historical data the mapping between the bus variables before and after a certain contingencies set, while, to the best of the Author's knowledge, limited contributions have been devoted to try and classify the power system security state by processing aggregated grid data. This is a relevant issue to address for a Transmission System Operator since it could allow a sensible decrease in the computational burden and, considering that aggregated grid data can be reliably predicted from several hours to one day ahead, it may enable the evolution of security assessment to security forecasting. In trying and filling this research gap, this paper explores the role of machine learning and feature selection algorithms. A realistic case study involving 2 years of synthetic grid data simulated on the Italian power system model against future potential operational scenarios characterized by a high share of renewables is presented and discussed to identify the most promising computing paradigms, analyzing the criticality of tuning the feature selection and classifier algorithms.

Power Load Forecast Based on CS-LSTM Neural Network

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizing. Therefore, the use of the LSTM algorithm for load forecast is more effective. The CS algorithm can perform global search better and does not easily fall into local optima. The CS-LSTM forecasting model, where CS algorithm is used to optimize the hyper-parameters of the LSTM model, has a better forecasting effect and is more feasible. Simulation results show that the CS-LSTM model has higher forecasting accuracy than the standard LSTM model, the PSO-LSTM model, and the GA-LSTM model.

A Blockchain-Powered E-Voting System with SHA-256 Encryption

E-voting systems hold promise for revolutionizing the electoral process by offering efficiency, accessibility, and transparency. However, concerns about security and integrity have hindered their widespread adoption. In this research paper, we propose a blockchain-based e-voting system leveraging the SHA-256 algorithm to address these challenges. Our system utilizes blockchain technology to create a tamper-proof and immutable ledger of votes, ensuring transparency and accountability throughout the voting process. The SHA-256 algorithm is employed to secure data integrity, providing robust protection against unauthorized modifications. This paper presents the system architecture, implementation details, and security analysis, demonstrating its resistance to tampering and vulnerabilities. Furthermore, we evaluate the system's performance and usability, highlighting its potential to enhance the electoral process. By leveraging blockchain technology and the SHA-256 algorithm, our e-voting system offers a secure, transparent, and efficient platform for conducting elections, fostering trust and confidence among voters. Keywords: Blockchain-based e-voting system, SHA-256 Algorithm

Security Analysis of Learning Management System Using Penetration Testing with ISSAF Framework

Security Analysis of Learning Management System Using Penetration Testing with ISSAF Framework

Towards robust log parsing using self-supervised learning for system security analysis

Logs play an important role in anomaly detection, fault diagnosis, and trace checking of software and network systems. Log parsing, which converts each raw log line to a constant template and a variable parameter list, is a prerequisite for system security analysis. Traditional parsing methods utilizing specific rules can only parse logs of specific formats, and most parsing methods based on deep learning require labels. However, the existing parsing methods are not applicable to logs of inconsistent formats and insufficient labels. To address these issues, we propose a robust Log parsing method based on Self-supervised Learning (LogSL), which can extract templates from logs of different formats. The essential idea of LogSL is modeling log parsing as a multi-token prediction task, which makes the multi-token prediction model learn the distribution of tokens belonging to the template in raw log lines by self-supervision mode. Furthermore, to accurately predict the tokens of the template without labeled data, we construct a Multi-token Prediction Model (MPM) combining the pre-trained XLNet module, the n-layer stacked Long Short-Term Memory Net module, and the Self-attention module. We validate LogSL on 12 benchmark log datasets, resulting in the average parsing accuracy of our parser being 3.9% higher than that of the best baseline method. Experimental results show that LogSL has superiority in terms of robustness and accuracy. In addition, a case study of anomaly detection is conducted to demonstrate the support of the proposed MPM to system security tasks based on logs.

Evaluation and Improvement of Power System Security with the Application of Machine Learning

The electricity grid has added many renewable and non-renewable energy sources to meet expanding demand. Sudden load variations exacerbate generator, transmission line, and distribution network issues. Load modelling choices are crucial for system prediction. This study indicates that ZIP load models with contingency criteria can accurately forecast load behaviour over time. The NR method predicts the contingency ranking with the High Bride Line Stability Ranking Index (HLSRI) under single line outage conditions, and an artificial neural network (ANN) is trained to predict the severity of the line outage and the system's behaviour. A mathematical model was utilised to analyse stability and cost with and without the UPFC and IPFC. Machine learning (ML) is used to rapidly predict the most affected transmission line during a contingency by clustering data using the J48 algorithm for the location of compensating devices. The PSO algorithm is used to develop an objective function to minimise fuel costs by maximising generating capacity. A transmission line failure and load variation might damage the electrical system. This study prioritises transmission line breakdowns and load changes. Power system security analysis provides power system status.

Analysis of information security analysis of a distributed system based on message flow

SIEM systems are considered and a comparative analysis of existing correlation mechanisms is carried out. A new mechanism for unique fields is proposed. The necessity of using the unique fields mechanism to improve the performance of the system as a whole is substantiated. Keywords information security, security events, correlation, unique fields

BPA: A decentralized payment system that balances privacy and auditability

<abstract><p>The rapid development of blockchain transactions highlights the importance of privacy protection (including anonymity and confidentiality) and underscores the necessity for auditability. Some schemes, such as PGC and Miniledger, support privacy protection and auditability. However, they only offer incomplete privacy protection (i.e., supporting anonymity or confidentiality exclusively). In response to these issues, we propose a scheme that achieves partial anonymity, confidentiality, auditability, and traceability. By integrating a variant of Pedersen commitments and randomizable signatures, we achieve partial anonymity for users and the auditability of transactions, thereby protecting user privacy under audit conditions. Based on the twisted ElGamal encryption algorithm and specially constructed zero-knowledge proofs, we achieve confidentiality of transaction amounts under legal and regulatory conditions. System test results indicate that this scheme effectively meets the above requirements. The feasibility of this scheme is confirmed through system testing, comparative analysis, and security analysis.</p></abstract>

Identification of Critical Nodes in Power Grid Based on Improved PageRank Algorithm and Power Flow Transfer Entropy

Identifying critical nodes in the power grid is a crucial aspect of power system security and stability analysis. However, the current methods for identification fall short in fully accounting for the power transfer characteristics between nodes and the consequences of node removal on the security and stability of power grid operation. To enhance the effective and accurate identification of critical nodes in the power grid, a method is proposed. This method is based on improved PageRank algorithm and node-weighted power flow transfer entropy, referred to as IPRA-PFTE. Firstly, based on the power flow and equivalent impedance between nodes, and the introduction of virtual nodes, an improved PageRank algorithm is obtained. Then the node-weighted power flow transfer entropy is derived by considering the uniformity of the transfer power flow distribution in the system following the removal of a node. Finally, the importance of nodes is obtained by combining the improved PageRank algorithm with the node-weighted power flow transfer entropy. The method’s effectiveness and accuracy are validated through simulation using the IEEE 39-bus example and subsequent comparison with existing methods.

Enhancing Color Image Security: Encryption with Dynamic Chaotic Three-Dimensional System and Robust Security Analysis

The rapid tech growth and widespread internet usage caused a surge in sharing multimedia (text, images, videos, audio) across public networks. Protecting this data is vital, demanding encryption to prevent unauthorized access. Image encryption distorts images for security. This paper highlights encryption's vital role in safeguarding multimedia, especially amid rising internet use and media exchange. It introduces a novel solution: a chaotic three-dimensional system for color image encryption. The study scrutinizes system traits using math software. It employs a new chaotic system to generate a crucial key sequence for pixel scrambling. Utilizing stream cipher encryption enhances security. Extensive security analysis tests its resilience against attacks like histogram and correlation techniques. Results are promising: a fairly uniform histogram, minimal correlation among pixels nearing zero, and entropy close to the ideal. Metrics like NPCR and UACI almost match ideal values, ensuring high security. Experiments confirm its effectiveness in encrypting diverse color images. The approach guarantees a uniform histogram, minimal pixel correlation nearing zero, entropy near the ideal value (8), and NPCR/UACI values close to ideals (99.61191% and 33.41068% respectively).

Security and dependability analysis of blockchain systems in partially synchronous networks with Byzantine faults

Blockchains enhance trust in decentralized systems through components like distributed consensus, peer-to-peer communication, and trustless computing. Deployed in real networks, they must ensure security and dependability at each level. This paper introduces security and dependability concepts in three key layers: consensus protocols, network infrastructures, and smart contract applications, derived from distributed systems theory and adapted to blockchains. It evaluates these attributes across eight famous blockchains, assuming realistic network deployments and Byzantine fault scenarios to reveal system strengths and weaknesses. The aim is to establish a comprehensive framework for blockchain assessments, providing insight into design choices and potential security issues.