System Security Assessment Research Articles

Analisis dan Rekomendasi Keamanan Website Kampus X Menggunakan ISSAF

The security of educational institution websites is critical in the digital era, especially with the increasing reliance on web-based services. This study evaluates the security of the Campus X website in Malang City using ISSAF (Information Systems Security Assessment Framework). The research stages include information gathering, network mapping, vulnerability identification, and penetration testing. At the vulnerability identification stage, tools such as OWASP ZAP and Acunetix detect security holes in web applications. The results show that the server has implemented the TLS protocol with basic security configuration. Still, several vulnerabilities exist, such as unnecessary open ports and deficiencies in the security header settings. Scanning using OWASP ZAP identified 24 security alerts, 12.5% of which were categorized as high risk, including SQL Injection and a lack of Content Security Policy (CSP). Additionally, DDoS attack simulations demonstrated server resilience, but testing showed the need for security improvements in other aspects. Key recommendations include implementing DNSSEC, closing unused ports, adding CSP headers, and improving protection against web application-based attacks. This research emphasizes the importance of a holistic and ongoing approach to website security management, including regular audits and real-time monitoring. With this strategy, institutions hope to strengthen their security posture, protect digital assets, and minimize the risk of ever-growing cyber attacks.

Security Infrastructure Service and Information Security Management Capability Audit to Improve System Security in Preventing Cyber Attacks using COBIT 2019

Cyber-attacks have become a serious concern in recent years, as hackers can attack systems, databases, and even steal personal data from a particular organization's database and sell it on the dark web. The individual identity data will be used to carry out various financially profitable activities such as online fraud, selling data containing detailed account information, and asking for ransom from data owners. The failure of an organization to protect data assets from cyber-attacks stems from several internal factors such as the vulnerability of the information security system it has, the less than optimal awareness of users in the organization of the importance of maintaining safe behavior in cyberspace, and routine processes related to information security that are missed. COBIT 2019 can be used as an audit framework to assess the level of maturity of the governance. Network infrastructure services in an organization can be measured in terms of the level of capability in terms of the application of technology assets and their configuration using the DSS05 and APO13 domains. This study aims to provide guidelines in assessing system security infrastructure in IT governance by assessing areas that are still not comply with the framework used, and providing recommendations for improving these inconsistencies so that the level of infrastructure capability will be maximized then.

Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique

The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.

Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling—An Oil and Gas Sector Case Study

As security breaches are increasingly widely reported in today’s culture, cybersecurity is gaining attention on a global scale. Threat modeling methods (TMM) are a proactive security practice that is essential for pinpointing risks and limiting their impact. This paper proposes a hybrid threat modeling framework based on system-centric, attacker-centric, and risk-centric approaches to identify threats in Operational Technology (OT) applications. OT is made up of software and hardware used to manage, secure, and control industrial control systems (ICS), and its environments include factories, power plants, oil and gas refineries, and pipelines. To visualize the “big picture” of its infrastructure risk profile and improve understanding of the full attack surface, the proposed framework builds on several threat modeling methodologies: PASTA modeling, STRIDE, and attack tree components. Nevertheless, the continuity and stability of vital infrastructure will continue to depend heavily on legacy equipment. Thus, protecting the availability, security, and safety of industrial environments and vital infrastructure from cyberattacks requires operational technology (OT) cybersecurity. The feasibility of the proposed approach is illustrated with a case study from a real oil and gas production plant control system where numerous significant cyberattacks in recent years have targeted OT networks more frequently as hackers realized the possibility of disruption due to insufficient OT security, particularly for outdated systems. The proposed framework achieved better results in detecting threats and severity in the design of the case study system, helping to increase security and support cybersecurity assessment of legacy control systems.

Enhancing claustrophobia exposure therapy: A blockchain and NFT-enabled metaverse approach

Claustrophobia is a widespread mental health issue that affects a significant portion of the population. Traditional approaches, such as exposure therapies, have shown efficacy but are limited by their inability to provide immersive and controlled environments for effective therapy. These methods often struggle to replicate the intensity and authenticity of claustrophobic scenarios and face challenges in data security and patient engagement. In this paper, we propose a blockchain and Non-Fungible Token (NFT)-based metaverse to create a decentralized, transparent, immersive, and secure platform for claustrophobia exposure therapy. Our approach integrates blockchain technology for decentralized data management and NFTs for incentivizing patient participation, offering a unique enhancement to traditional therapy methods. We develop four blockchain smart contracts to manage registration, access control, NFT management, progress monitoring, and incentives distribution. Our system architecture, sequence diagrams, and algorithms are presented to demonstrate the functionality of our solution. We validate our approach through functionality testing, cost evaluation, and security analysis. Specifically, we measure the gas consumption of the smart contracts, analyze transaction costs, and assess system security against vulnerabilities. Additionally, we leverage existing literature to support the effectiveness of NFTs as incentives and the role of blockchain in enhancing data security and participation. The results show improvements in therapy engagement and data security, validating the practical implications of integrating blockchain and metaverse technologies in mental health therapy. We discuss the practical implications, challenges, and limitations of our solution, along with guidelines for generalization. The source code is publicly available on GitHub.

Security assessment and preventive control of combined electricity and heat systems with distributed electric thermal storage

Achieving carbon peak and neutrality targets can be accelerated by reducing wind power curtailment (WPC). However, WPC is constrained due to the electric thermal coupling characteristics of combined heat and power. To tackle this challenge, we propose an optimal scheduling framework for combined electricity and heat systems (CEHS) with distributed electric thermal storage (DETS) and perform security assessment and preventive control. Firstly, a general model is proposed to schedule DETS for different types of customers. The customer flexibility regulation variable (CFRV) for each customer is developed to fill this knowledge gap, and the excess thermal storage stored in DETS is used for auxiliary thermal network regulation (ATNR). Secondly, a two-stage security assessment and preventive control method for CEHS with DETS is proposed to correct overloaded branch flow. Thirdly, an evaluation index is developed to evaluate the severity of overloaded branches. The results show that the method of considering CFRV can reduce WPC and operation costs compared to the traditional method. Adopting ATNR enhances the flexibility and economics of grid scheduling compared to non-ATNR. Moreover, assessments of overloaded branches in various cases show that the preventive control method mitigates overload risks.

A new comprehensive framework for cascading outage simulation in power systems

Simulating accurately and fast cascading outages play a critical role in power system security and resilience assessment. This paper proposes a new comprehensive framework for cascading outage simulation, articulated around an extended AC power flow. To address the key modelling features of cascading outage simulation, the proposed framework develops new algorithms for: a) detecting possible islanding, b) frequency control in each island: primary frequency control (PFC), under-frequency load shedding (UFLS) and over-frequency generator tripping (OFGT) and c) improved under-voltage load shedding (UVLS) and load models to avoid the divergence of AC power flow and allow handling voltage instability during the cascade. Tests on a realistic 60-bus system show that the proposed cascading outage simulator can successfully handle islanding, PFC, UFLS, OFGT, and UVLS; it simulates the cascade until there is no violation of system frequency, voltage, and power flow limits in islands that do not experience blackout.

A Hybrid Technique Based on RF-PCA and ANN for Detecting DDoS Attacks IoT

The increasing reliance on smart products has increased vulnerabilities in Internet of Things (IoT) traffic, which poses significant security risks. These vulnerabilities allowed some hackers to exploit them, which led to system performance degradation. Attacks can lead to these vulnerabilities to various undesirable outcomes, including data leakage, economic losses, data breaches, operational disruptions, and damage to the company's reputation. To address these security challenges, network intrusion detection alarms play a crucial role in assessing system security. In recent years, the proliferation of intelligent and soft computing-based algorithmic and structural frameworks has been evident. However, previous studies have faced challenges related to comprehensiveness, zero-day attacks, realism, and data interpretation. In light of these concerns, this study proposes to design a neural network for proactive detection of attacks. Moreover, we propose to use a hybrid system called RF-PCA to facilitate dimensionality reduction and help classifiers. Notably, this is the first application of a BOT-IoT data set in such an approach. The study also includes a discussion of relevant IoT terms in the context of our work. The proposed method uses high-level data features to represent and draw conclusive conclusions. To evaluate its effectiveness, an experiment was conducted using Python as the programming environment, achieving a remarkable detection rate of 99.73%.

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.

A Literature Survey on System Security and Network Vulnerability Assessment

This abstract outlines a detailed study on improving network security through vulnerability assessment and penetration testing, using tools like Nessus, Nmap, Metasploit, on the Kali Linux OS. The methodology involves identifying vulnerabilities, conducting simulated attacks, and developing a bash script to address false positives from Nessus scans. The research aims to enhance network security, provide practical recommendations, and contribute to cybersecurity practices, with a focus on both internal and external network security. Keywords: Vulnerability Assessment, Pentest, Nessus, Metasploit, Kali Linux

Web Application Penetration Testing on Udayana University's OASE E-learning Platform Using Information System Security Assessment Framework (ISSAF) and Open Source Security Testing Methodology Manual (OSSTMM)

Education is a field that utilizes information technology to support academic and operational activities. One of the technologies widely used in the education sector is web-based applications. Web-based technologies are vulnerable to exploitation by attackers, which highlights the importance of ensuring strong security measures in web-based systems. As an educational organization, Udayana University utilizes a web-based application called OASE. OASE, being a web-based system, requires thorough security verification. Penetration testing is conducted to assess the security of OASE. This testing can be performed using the ISSAF and OSSTMM frameworks. The penetration testing based on the ISSAF framework consists of 9 steps, while the OSSTMM framework consists of 7 steps for assessment. The results of the OASE penetration testing revealed several system vulnerabilities. Throughout the ISSAF phases, only 4 vulnerabilities and 3 information-level vulnerabilities were identified in the final testing results of OASE. Recommendations for addressing these vulnerabilities are provided as follows. Implement a Web Application Firewall (WAF) to reduce the risk of common web attacks in the OASE web application. input and output validation to prevent the injection of malicious scripts addressing the stored XSS vulnerability. Update the server software regularly and directory permission checks to eliminate unnecessary information files and prevent unauthorized access. Configure a content security policy on the web server to ensure mitigation and prevent potential exploitation by attackers.

A systematic approach for threat and vulnerability analysis of unmanned aerial vehicles

The proliferation of Unmanned Aerial Vehicles (UAVs) is expected to experience a substantial rise in the next years, driven by their ever-increasing application across various domains. However, ensuring secure communication between UAVs and their ground stations is crucial to prevent the unauthorized disclosure of sensitive information that could jeopardize the mission’s integrity when exploited by malicious actors. Despite this importance, several UAV systems currently operate based on open-source command and control technologies, which have overlooked several security considerations while focusing on availability and safety. To address this concern, this study conducts a comprehensive security assessment of UAV-based systems starting with a systematic literature review whose main purpose is building a comprehensive catalog of threats associated with this technology. Particular attention has been paid to the MAVlink protocol, an open-source protocol commonly utilized for telemetry and command and control for multiple UAVs. Therefore, drawing upon the built catalog, a threat modeling and penetration testing technique has been employed to examine the MAVlink implementation on a real UAV. A threat model developed for a specific case study is also presented, leading to the discovery of four new vulnerabilities, some of which were successfully exploited through attacks. By shedding light on these vulnerabilities, this work seeks to encourage further investigation and research to develop robust security mechanisms for UAV communication systems. It is imperative to address these vulnerabilities proactively to enhance the overall security posture and safeguard against potential threats in the UAV ecosystem.

Secure and Efficient Wireless Sensor Network and Machine Learning -based Monitoring System for Student Physical and Mental Health

In this research we have reviewed the performance of machine learning algorithms and WSNs for the monitoring student physical and mental health. We have found that logistic regression is a stronger alternative after an exhaustive evaluation of machine learning algorithms that consistently produce superior performance metrics than Gaussian mixture models (GMM). This has demonstrated average accuracy of 87.1% across 10 experiments, besting the average accuracy of 83.8% of GMM. It also illustrates greater efficacy for assessing system security, producing average accuracy of 86.6%, with 91.9% for GMM. The findings demonstrate the significance of select a machine learning algorithms that meets the specific needs of monitoring student health systems. They are also an illustration of logistic regressions dependability, and adeptness at identifying subtle changes in student health indicators that allow for proactive initiation of interventions and support mechanisms tailored to individual students’ needs. Going forward, monitoring systems should next be further refined and optimised so that they can more effectively support both student well-being and academic success in the complex and diverse educational settings of Indian universities.

A Dynamic Equivalent Method for PMSG-WTG Based Wind Farms Considering Wind Speeds and Fault Severities

The dynamic security assessment of power systems needs to scan contingencies in a preselected set through time-domain simulations. With more and more wind power generation integrated into power systems, the complexity of simulation will increase greatly if wind farms are modeled in detail. Thus, it is critical to develop dynamic equivalent methods for wind farms. The dynamic response characteristics of permanent magnet synchronous generator-wind turbine generators (PMSG-WTGs) are not only influenced by their control strategies, but also by the operating wind speeds and the fault severities. Thus, this paper proposes a dynamic equivalent method for a PMSG-WTG based wind farm considering the wind speeds and the fault severities. Firstly, we propose a clustering method based on the operating wind speed and the terminal voltage of each PMSG-WTG at the end of the fault. Then, a single-machine equivalent method is introduced for each cluster of PMSG-WTGs. For the cluster of PMSG-WTGs with active power ramp recovery process, an equivalent model with segmented ramp rate limitation for active current is designed. In order to obtain the clustering indicators, a simulation-based iterative method is put forward. Eventually, the efficiency and accuracy of the proposed method are verified by the simulation results.

QFDSA: A Quantum-Secured Federated Learning System for Smart Grid Dynamic Security Assessment

QFDSA: A Quantum-Secured Federated Learning System for Smart Grid Dynamic Security Assessment

Distributed Feature Selection for Power System Dynamic Security Region Based on Grid-Partition and Fuzzy-Rough Sets

In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs.

Safeguarding Industry 4.0: A Machine Learning Approach for Cyber-Physical Systems Security and Sustainability

The proliferation of connected objects, with over 8 billion IoT devices currently and a projected increase to 41 billion by 2027, signifies the widespread integration of technology in sectors like Smart City, Industry 4.0, e-commerce, and e-health. This study focuses on the security assessment of Cyber-Physical Systems (CPS) in manufacturing processes, utilizing six supervised algorithms on a dataset with 61 features. The results not only offer valuable insights into security but also contribute to the optimization of machine learning models. This research implicitly addresses the sustainability aspect by acknowledging the broader impact of CPS technologies. Cyber-Physical Systems (CPS) optimization of machine learning models not only fits in with the industry 4.0 framework’s overarching goal of promoting environmentally friendly practices, but it also creates a vital connection between sustainability and the security paradigm that these complex systems are built upon. This mutually beneficial relationship highlights how improving machine learning algorithms with the goal of reducing environmental impact also helps to strengthen the security infrastructure of CPS. Industry 4.0 prioritizes environmental responsibility by emphasizing the development and application of eco-conscious practices. It also acknowledges the interdependence of sustainability and security within the framework of dynamic cyber-physical ecosystems.

A Security Assessment Strategy for Corporate Financial Systems Based on Data Mining Techniques

Abstract The security assessment of corporate financial systems has become a popular area of research, although few results have been published so far. This research focuses on integrating the association rule algorithm with Bayesian networks to evaluate the security of corporate financial systems. It primarily employs Bayesian networks to calculate the conditional probability, prior joint probability, and posterior probability of each attribute node in the IDRI-Tree to assess the security of the company’s financial system. Additionally, abnormal data from Company A’s financial system were collected to evaluate the method’s effectiveness. The results indicate that Company A’s financial system has data discrepancy compliance rates of 98.92% and 96.50%, respectively. The security assessment scores for monitoring, evaluation, and assessment in financial system security are the lowest, averaging 1.41, while the scores for payment, service, and support are the highest, averaging 3.32. The financial system security assessment method proposed in this paper demonstrates high practical value and offers a reference for the future establishment and improvement of financial system security assessment methods.

Online Monitoring of Oscillation Modes for Small-Signal Security Assessment

This paper presents the main concepts of small-signal security assessment of power systems, focusing on the online monitoring of small-signal stability. In this work, a computational tool for online monitoring of oscillation modes (OLMO) in power systems is proposed. The objective of the methodology consists of determining small-signal stability margins, security levels, and corrective measures that can improve the damping factor of oscillation modes present in such systems. These corrective measures can be associated with possible redispatches of power plants. In this context, the Closest Security Boundary for Generation Redispatch using Eigenvalue Sensitivities, known as CSBGRES Method, could be applied to determine an optimized redispatch for the power plants of the system to ensure a desired minimum damping factor, previously chosen, for oscillation modes. The proposed tool can be applied for controlling power plant dispatches on Smart Grids, focusing on the damping factor of oscillation modes. A small test system is used to evaluate the proposed computational feature and the results were presented to demonstrate its importance to power system security. Results of the utilization of OLMO on the Brazilian Interconnected Power System (BIPS) are presented to prove the viability of using this method in large-scale power systems.

The Total Transfer Capability Assessment of Transmission Interfaces Combining Causal Inference and Multi-Task Learning

Power system security assessment has many tasks for different evaluation topics or objects, such as the Total Transfer Capability (TTC) assessment of different transmission interfaces. Some different tasks are related with each other, which is an important characteristic to improve the assessment performance if used properly but easily to be neglected. In this study, a novel architecture of the Causality-based Multi-Task Neural Network (CMTNN) is proposed, which can take the causal relevance between tasks into account while avoiding the introduction of too much extraneous information. First, the mask layer is applied after the input layer to filter irrelevant information task-specifically. Then, the causal analysis based on the Peter-Clark algorithm is performed to find those causally-related tasks. Finally, the causality-based information-sharing mechanism between related tasks is designed in the neural network to share deep information, so as to improve the model assessment performance on those tasks. In this paper, the proposed method is applied on the total transfer capability assessment problem of multiple transmission interfaces, and the case study on a real-world regional system with more than 8,000 nodes in China validates its effectiveness.