Attack Patterns Research Articles

From frontlines to online: examining target preferences in the Russia–Ukraine conflict

Distributed denial of service (DDoS) attacks have become a prominent threat in the digital landscape, with their frequency and impact magnifying during geopolitical conflicts. The Ukraine-Russia conflict, which commenced in February 2022, witnessed a surge in DDoS attacks, becoming the most common type of cyber assault during this period. This study examines the frequency and patterns of DDoS attacks throughout the Russo-Ukraine cyberwarfare, identifying the sectors most affected. By analyzing messages from Telegram channels containing IP addresses and URLs, we identified 4,612 unique victim domain names, with 3,090 targeted by Ukrainian hacktivist groups (pro-Ukrainian) and 1,522 by Russian hacktivist groups (pro-Russian). We observed distinct DDoS attack patterns between pro-Ukrainian and pro-Russian collectives. Ukrainian groups exhibited peak activity during May, June, and July, with a noticeable decline towards the end of 2022. In contrast, the pro-Russian group’s activities intensified in late 2022. Our investigation highlights that pro-Ukrainian collectives, particularly ‘IT Army of Ukraine 2022’, were the most active in conducting DDoS attacks and operated with higher synchronicity. Our findings also indicate that crucial portals for information and services, particularly those related to news, government, business, finance, and travel, were consistently targeted by DDoS attacks. The majority of these victim domains lacked adequate DDoS protection during the assaults, with few improving their security measures post-attack. The study also reveals that DDoS attacks predominantly occurred on Saturdays, Sundays, and Mondays. Our results underscore the necessity for enhanced cybersecurity measures in vulnerable sectors to mitigate the impact of DDoS attacks during times of conflict.

Unveiling and Evaluating Vulnerabilities in Branch Predictors via a Three-Step Modeling Methodology

With the emergence and proliferation of microarchitectural attacks targeting branch predictors, the once-established security boundary in computer systems and architectures is facing unprecedented challenges. This paper introduces an innovative branch predictor modeling methodology that abstractly characterizes 19 states and 53 operations of branch predictors, aiming to assist hardware designers in addressing overlooked security concerns during the microarchitecture design phase. Building upon this modeling discipline, we develop a symbolic execution-based framework to analyze and derive potential vulnerabilities in branch predictors. This framework finally yields 156 valid three-step attack patterns against branch predictors, including 89 novel variants not discovered in previous work. Subsequently, we extend the framework to automatically generate a benchmark suite for assessing the practical feasibility of derived attacks in real-world scenarios. Evaluation across five commercial Intel processors underscores the substantial threat posed by branch predictor attacks, with 130 of the 156 derived attacks proving viable on at least one processor. Finally, we theoretically model and evaluate 12 secure designs related to branch predictors. The evaluation results demonstrate that existing secure branch predictors can offer better security guarantees than secure speculation schemes, indicating that secure branch predictor designs are promising solutions to maintain the confidentiality and integrity of computer systems.

북한의 사이버전 역량변화와 위협 전망: 군사적 관점을 중심으로

This article analyzed North Korea's rapidly changing cyber capabilities and presented countermeasures against future threats from a military perspective. Previous studies had limitations in staying in past data with North Korea experts, so this study differentiated them based on specific latest cases. North Korea is focusing on selecting cyber warfare talents and increasing manpower, strengthening education and training systems, and upgrading cyber attack patterns and technologies, focusing on excluding background components in accordance with domestic and foreign environmental changes such as the signing of the New Treaty between North Korea and Russia, promoting local development 20×10 policies, and developing artificial intelligence (AI) technology. In particular, North Korea laid the foundation for the remarkable development of cyber warfare capabilities based on the New Treaty. In addition, cyber attacks are expanding into the social infrastructure sector to support the economic policies directly managed by Kim Jong-un. It is expected that AI technology will be applied more to North Korea's destructive cyber attacks in the future. In conclusion, Republic of Korea (ROK) should strengthen ROK-U.S. cybersecurity cooperation and develop its capabilities to fully respond to all-round cyber threats, including North Korea's cyber threats, by applying cutting-edge AI technology to cyber warfare in earnest from the perspective of total power.

Influential Features Analysis And AI-Driven Accuracy Enhancement: A Study Case For DDoS Detection

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to be discovered. Because of that, there have been a number of approaches, as well as solutions that have been devised in order to detect it as accurately and efficiently as possible. Impressively, data mining methods have been employed to identify patterns of DDoS attacks from the computer network traffic. Nevertheless, the recent works’ results have not yet mentioned which factors of the computer network traffic play the most vital role in indicating the potential for true positive attacks. Additionally, with the Machine Learning approach, there are still ample opportunities to enhance the attack prediction accuracy of the detection model. As such, in this paper, we attempt to explore factors that would influence the classification result, and leverage a variety of Machine Learning algorithms, i.e. Random Forest, Naive Bayes, Logistic Regression, and Multilayer Perceptron, for the purpose of improving the accuracy of data classification. The experiments were deployed using CICIDS2017 dataset and compared with the other related works on the same dataset. The experimental outcomes of our methodologies and analyses demonstrate some potential and effectiveness enhancement compared to previous works. Moreover, we analysized and concluded the insight of how side factors affect the attack identification result. The collected information from our analysis identifies dominant factors, and opens a new view for their hidden correlationship directly affecting the attack labeling.

Neuromorphic computing for real-time adaptive penetration testing: analysis of human intuition in AI-dominated work space

This research investigates the revolutionary integration of neuromorphic computing with human intuition in the domain of real-time adaptive penetration testing, addressing the critical challenges facing modern cybersecurity in AI-dominated workspaces. The study presents a novel approach that combines the parallel processing capabilities of neuromorphic architectures with the nuanced decision-making abilities of human security experts, resulting in a hybrid system that significantly enhances threat detection and response capabilities. Our research implements a sophisticated neuromorphic computing model featuring 1024 input neurons, 2048 hidden layer neurons, and 512 output neurons, utilizing modified leaky integrate-and-fire algorithms for spike processing. The system incorporates real-time adaptive mechanisms that enable dynamic threat modeling and immediate response generation, while simultaneously learning from human expert intuition through a carefully designed collaborative interface. This architecture demonstrates remarkable improvements in both processing efficiency and threat detection accuracy, achieving a 94.7% true positive rate while maintaining an exceptionally low 2.3% false positive rate. The experimental methodology encompassed extensive testing across a diverse range of attack scenarios, including advanced persistent threats, zero-day vulnerabilities, and sophisticated social engineering attacks. The test environment comprised 500 virtual nodes distributed across multiple security zones, providing a realistic platform for comprehensive system evaluation. Performance metrics revealed significant improvements over traditional approaches, including an 89% success rate in adapting to previously unseen attack patterns and a 76% reduction in decision-making time for complex threats. Quantitative analysis demonstrates the system's superior capabilities in real-time threat detection and response, with average detection latencies of 1.2 milliseconds and consistent performance maintaining up to 100,000 concurrent connections. Qualitative assessment of human-AI synergy, conducted with 50 experienced penetration testers, revealed that 92% reported enhanced decision-making capabilities, while 95% experienced reduced cognitive load during complex scenarios. The research contributes significantly to the field by establishing a new paradigm for adaptive penetration testing that effectively combines neuromorphic computing efficiency with human intuitive expertise. The findings demonstrate that this integration not only enhances detection and response capabilities but also provides a more sustainable approach to cybersecurity in increasingly complex technological environments. Furthermore, the study opens new avenues for research in human-AI collaboration within cybersecurity, suggesting promising directions for future development in adaptive security systems. The implications of this research extend beyond immediate cybersecurity applications, offering insights into the broader field of human-AI collaboration in critical decision-making scenarios. The study also addresses important ethical considerations regarding AI autonomy in security operations, providing guidelines for responsible implementation of AI-driven security solutions while maintaining essential human oversight.

MITIGATING CYBER THREATS WITH ROBUST IDENTITY AND ACCESS MANAGEMENT TECHNIQUES

Abstract—Identity and Access Management (IAM) is a fundamental security framework that ensures legitimate users can access critical resources within a computer system while preventing unauthorized access. This is especially vital in financial organizations, where safeguarding sensitive information such as user identities, financial data, and medical records is paramount. These organizations face a heightened risk of cyberattacks due to the valuable and often high- stakes nature of the data they manage. Data breaches in such institutions can result in identity theft, billing fraud, insurance fraud, and other serious consequences, making robust IAM solutions essential. Traditional IAM systems rely on user credentials, roles, and policies to manage access, but as threats become more sophisticated, so must the defense mechanisms. Recent advances in machine learning offer promising methods for enhancing IAM systems to detect and prevent potential security breaches in real- time. One such approach is the use of Identity Markup- based IAM systems, which integrate user behavior and context-based attributes to assess the legitimacy of access requests. By embedding advanced algorithms, such as ensemble models, within these systems, organizations can better predict, identify, and mitigate threats before they escalate. This paper proposes an Identity Markup-based IAM solution enhanced by an Ensemble ExoBoost Tree for detecting and preventing security attacks within financial organizations. The ExoBoost tree, a variant of gradient boosting, combines multiple decision trees to provide a powerful, accurate method for detecting anomalies and potential attack patterns based on historical data and user behavior analysis. By incorporating this machine learning-driven approach, the IAM system not only controls access more effectively but also enhances its ability to identify subtle attack vectors, even those previously unknown. The proposed solution aims to address the growing complexity of cybersecurity challenges in financial organizations by providing a robust, adaptive IAM system capable of preemptively identifying and mitigating future data attacks. By leveraging advanced machine learning techniques, this approach offers a significant advancement over traditional IAM systems, ensuring the ongoing protection of sensitive financial data against emerging threats. Index Terms—Identity and Access Management,Identity Markup-based IAM,Ensemble ExoBoost Tree

PreyTouch: a touchscreen-based closed-loop system for studying predator-prey interactions

The ability to catch prey is crucial for survival and reproduction and is subject to strong natural selection across predators. Prey capture demands the orchestrated activation of multiple brain regions and the interplay between sensory processing, decision-making, and motor execution. These factors, together with the ubiquity of prey capture across species makes it appealing for comparative studies across neuroscience and ecology. However, despite recent technological advances, experimental approaches for studying natural behaviors such as prey catch are lagging behind. To bridge this gap, we created PreyTouch—a novel approach for performing prey capture experiments that incorporate flexible prey control, accurate monitoring of predator touchscreen strikes and automated rewarding. Further, its real-time processing enables coupling predator movement and prey dynamics for studying predator-prey interactions. Finally, PreyTouch is optimized for automated long-term experiments featuring a web UI for remote control and monitoring. We successfully validated PreyTouch by conducting long-term prey capture experiments on the lizard Pogona vitticeps. This revealed the existence of prey preferences, complex prey attack patterns, and fast learning of prey dynamics. PreyTouch’s unique features and the importance of studying prey capture behavior make it a valuable platform for connecting natural behavior with cognitive studies across various species and disciplines.

Application of GIS Criminalistics Techniques in the Analysis of Urban Terrorism

A common myth about terrorist attacks is that they are random incidents that can occur anywhere in the world. While it is true that such attacks can happen globally, they usually exhibit discernible spatiotemporal patterns that could be analysed using criminalistics techniques, methods, and analytic tools. Numerous studies have shown that analysing the spatial and temporal patterns of terrorist attacks can uncover the modus operandi of terrorist organisations and shed light on their strategic objectives. By identifying the operational methods of specific terrorist groups, it becomes theoretically possible to create models that predict the spatiotemporal dispersion of their movements and actions. Such models can potentially indicate sites of future attacks, including bombings, arsons, assassinations, hijackings, abductions, sabotage, kidnappings, and more.The paper aims to examine and provide a concise explanation of why the application of criminalistic techniques is possible in the prediction of distinct aspects of urban terrorism.

Distributed Denial of Services: Detection and Prevention

Distributed Denial of Service is one of the most prominent and dangerous types of attacks dis- rupting critical online services, suffering finan- cial losses, and affecting organizational oper- ations. The exploitation of vulnerabilities is enough to overwhelm the targeted websites with malicious traffic, making them inaccessible to legitimate users. Such sophistication in attack patterns coupled with explosive growth in con- nected devices has challenged the traditional approaches of detection, being bare minimum signature-based or basic anomaly detection and likely to pose difficulties in coping with emerging threats, thus prone to high false positives. This paper presents a systematic review of ten works from very recent literature on ML and DL applications in DDoS detection and mit- igation. The techniques thus are supervised, unsupervised, and hybrids applied to datasets such as NSL-KDD, CICIDS2017. ML and DL techniques do appear promising for increas- ing accuracy in detection and adaptability, but then challenges persist-in scalability, efficiency or speed of computation, and quality of datasets. It would challenge the future of research: more light on real- time models, more diversified and all-inclusive datasets, and edge and federated computing towards better accelerated detection. These would significantly contribute to the ad- vancement of scalable effective solutions that counter complex DDoS threats in today’s net- work environments. Keywords Distributed Denial of Service (DDoS) Attacks, Cybersecurity, Network Traffic Anomaly De- tection, Real-Time Threat Detection, Machine Learning (ML), Deep Learning (DL), Hy- brid Detection Models, Anomaly Detection, Signature- Based Detection, Adaptive Security Models.

Development of algorithms for early detection of cyberattacks on networks using machine learning

Critical infrastructure protection and national security are enhanced by the security and reliability of networks. Various types of information circulate on these networks, ranging in classification from open to closed. The consequences of cyberattacks on these networks can be severe, including reputational damage, financial loss, operational disruption and data leakage. Traditional security methods, such as firewalls and anti-virus software, are becoming less effective against modern and ever-changing cyber threats. As a result, powerful network intrusion detection systems (IDS) have become indispensable for proactive detection and mitigation of cyber attacks. Machine learning has become a viable method for creating adaptive intrusion detection tools that can detect new and complex attack patterns. By learning from huge labelled network traffic datasets, ML models can understand the subtle patterns and differentiating features of normal and abnormal or malicious traffic flows. This allows them to detect possible cyber threats and intrusions that traditional signature-based IDSs cannot detect. Extracting discriminative features and training appropriate classification models from such data is a challenging task. In the presented study, we analyse the effectiveness of ML algorithms for detecting cyberattacks, in particular distributed denial of service (DDoS) attacks, in network traffic data. In the presented study, a network attack detection system is developed using ML and deep learning (DL) models and experimented on the CICIDS2017 dataset. The main objectives of the study are to develop a strategy for extracting valuable information from raw network streams; to study the impact of data preparation on the false positive rate; and to conduct a comparative analysis of ML models for cyberattack detection. The main goal of the study is to provide an understanding of the development of a reliable adaptive network intrusion detection system using ML approaches that increase cybersecurity capabilities and protect against future cyberattacks.

Renyi entropy-driven network traffic anomaly detection with dynamic threshold

Network traffic anomaly detection is a critical issue in network security. Existing Abnormal traffic detection methods rely on statistical-based or anomaly-based approaches, and these detection methods all require a full understanding of traffic characteristics and attack patterns. Information entropy has been widely studied in abnormal traffic detection because it can describe the distribution characteristics of network traffic. However, this method makes it difficult to cope with the timing and variability of network traffic. To address these challenges, this paper proposes a network traffic anomaly detection method based on Renyi entropy. Simultaneously, we introduce a fixed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection. Experimental results show that the method proposed in this paper is superior to popular abnormal traffic detection methods in terms of effectiveness and efficiency, it is better adapted to the dynamic changes of network traffic and provides a more reliable solution for anomaly detection.

CAPEC ontology generator

CAPEC is an effort coordinated by MITRE Corporation. Its aim is attack pattern database structured in taxonomies. CAPEC is available as XML document from its project site. CAPEC structure and content are under permanent change and development. It is still not mature database but may be never will.CAPEC, CWE, and CVE are databases devoted to attacks, weaknesses, and vulnerabilities. They refer each other forming a knowledge ecosystem in cybersecurity area.Traditional approach for knowledge presentation as information does not work well with conceptualizations under dynamics of this ecosystem and particularly of CAPEC. In this paper, an alternative approach to CAPEC knowledge presentation is proposed, as ontology. First, CAPEC structure and content are discussed and then ontology structure is introduced. CAPEC as ontology opens doors to ``open world'' concept that is more adequate to ecosystem dynamics.CAPEC ontology is programmatically generated from CAPEC database.CAPEC ontology generator in implemented in Python.

Analysis of Internet of Things to Enhance Security Using Artificial Intelligence based Algorithm

Exploring creative methods to secure IoT networks is vital due to the enormous security concerns created by the rapid proliferation of the Internet of Things (IoT). To increase the security of the IoT, this study examines the use of artificial intelligence (AI), specifically deep learning (DL) as well as machine learning (ML) techniques. Three state-of-the-art DL algorithms—Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN)—along with three ML methods—CatBoost, LightGBM, and XGBoost—are examined. These algorithms are renowned for their capability to handle big, as well as unbalanced datasets. This work test how well each algorithm can identify anomalies, categorize attacks, and forecast vulnerabilities using an IoT security dataset, such as CICIDS 2017 as well as IoT-23. The research evaluates algorithms by comparing their accuracy and training time. Classification tasks are where CatBoost and LightGBM really good, but when it comes to sequential data and complicated attack patterns, DL algorithms like CNN and LSTM are good. To provide the groundwork for creating AI-driven security solutions optimised for IoT systems, this research sheds light on the benefits and drawbacks of each method.

SDN-Driven Security Framework for DDOS Attack Detection and Mitigation

The rising complexity and persistence of Distributed Denial of Service (DDoS) attacks, particularly low-rate variants, present significant challenges in detection and mitigation within Software-Defined Networking (SDN) environments. Existing detection systems often flood networks with alerts, burdening security personnel and delaying timely mitigation. Furthermore, many solutions are designed and tested in simulated conditions, limiting their real-world applicability. To address these challenges, we propose an SDN-based security framework enhanced with automated monitoring, detection, and mitigation capabilities, optimized for slow-rate DDoS attacks. Our framework was rigorously evaluated on a physical testbed, achieving mitigation efficiencies between 91.66% and 100% under varied attack conditions, thus proving its robustness in practical settings. Additionally, we introduce the SDN-SlowRate-DDoS dataset, designed to assist researchers and industry professionals in developing and testing intrusion detection solutions in more realistic scenarios. To further improve DDoS defense in SDN, we incorporate an ensemble online machine learning model that dynamically adapts to evolving attack patterns, enhancing accuracy across attack types and outperforming traditional models with a detection rate of 99.2% on benchmark datasets. This dual approach leverages both real-world testing and adaptive machine learning, advancing proactive DDoS threat management in SDN environments

Advanced deep learning framework for detecting SQL injection attacks based on GRU Model

SQL injection attacks are a major danger to data security in application systems, leveraging weaknesses to illicitly access and change sensitive data. Traditional detection methods, such rule-based systems and supervised machine learning, struggle to adapt to new SQL injection attack strategies. This study presents an Enhanced Deep Learning Framework for SQL Injection Detection utilizing the Gated Recurrent Unit (GRU) model to overcome constraints. To discover new attack patterns, the proposed framework uses a dynamic learning process instead of static detection methods. By examining SQL query sequences, the model can distinguish between legal and malicious interactions without predefined rules or reinforcement learning. The framework's performance is assessed using a broad dataset of valid and malicious SQL queries. Experiments show a considerable increase in detection accuracy, reaching 96.65% with little false positives. The proposed system is resilient and adaptable to address the complexity of modern SQL injection threats. The results demonstrate the effectiveness of deep learning, particularly the GRU model, in detecting SQL injection attacks. This research enhances database security and lays the groundwork for future cyber-security methods in web-based applications.

Improving Intrusion Detection System using the Combination of Neural Network and Genetic Algorithm

One of the essential issues in network-based systems is a fault attack which is caused by intrusion. It is the responsibility of intrusion detection to provide capabilities such as adaptation, fault tolerance, high computational speed, and error resilience in the face of noisy information. Thus, the construction of an efficient intrusion detection model is highly appreciated to increase the detection rates as well as to decrease false detection. Currently, researchers are more focusing on abnormal behaviour of network as this system can easily recognize new attacks without updating the daily recognized databases. However, the capability of current developing machine learning algorithms suffers from inefficient use of intrusion detection particularly once it involved some huge datasets of irrelevant and redundant features. The main objective of this thesis is to achieve the higher detection rate with lower false detection for attack recognition in order to support efficient application of intrusion detection. To achieve this goal, a new machine learning model was designed and developed to provide intelligent recognition with new attack patterns. New proposed and improved algorithm GA-ANN is constructed to support the proof of concept. For evaluation, five datasets namely KDD CUP 99 from the online data repositories are used in the experiment. In the above scenarios, GA-ANN provides the highest detection rate for pattern recognition which was 98.98% based on 18 selected features. This means that the proposed IDS model is significant and increases the network security.

Activation of PAA at the Fe-Nx Sites by Boron Nitride Quantum Dots Enhanced Charge Transfer Generates High-Valent Metal-Oxo Species for Antibiotics Degradation.

Advanced oxidation processes (AOPs) based on peracetic acid (PAA) offer a promising strategy to address antibiotic wastewater pollution. In this study, Fe-doped graphitic carbon nitride (g-C3N4) nanomaterials were used to construct Fe-Nx sites, and the electronic structure was tuned by boron nitride quantum dots (BNQDs), thereby optimizing PAA activation for the degradation of antibiotics. The BNQDs-modified Fe-doped g-C3N4 catalyst (BNQDs-FCN) achieved an excellent reaction rate constant of 0.0843 min-1, marking a 21.6-fold improvement over the carbon nitride (CN)-based PAA system. DFT calculations further corroborate the superior adsorption capacity of the Fe-Nx sites for PAA, facilitating its activation. Charge transfer mechanisms, with PAA serving as an electron acceptor, were identified as the source of high-valent iron-oxo species. Moreover, the BNQDs-FCN system preferentially targets oxygen-containing functional groups in antibiotic structures, elucidating the selective attack patterns of these highly electrophilic species. This research not only elucidates the pivotal role of high-valent iron-oxo species in pollutant degradation within the PAA-AOPs framework but also pioneers a wastewater treatment system characterized by excellent degradation efficiency coupled with low ecological risk, thereby laying the groundwork for applications in wastewater management and beyond.

An optimized ensemble model with advanced feature selection for network intrusion detection.

In today's digital era, advancements in technology have led to unparalleled levels of connectivity, but have also brought forth a new wave of cyber threats. Network Intrusion Detection Systems (NIDS) are crucial for ensuring the security and integrity of networked systems by identifying and mitigating unauthorized access and malicious activities. Traditional machine learning techniques have been extensively employed for this purpose due to their high accuracy and low false alarm rates. However, these methods often fall short in detecting sophisticated and evolving threats, particularly those involving subtle variations or mutations of known attack patterns. To address this challenge, our study presents the "Optimized Random Forest (Opt-Forest)," an innovative ensemble model that combines decision forest approaches with genetic algorithms (GAs) for enhanced intrusion detection. The genetic algorithms based decision forest construction offers notable benefits by traversing a wider exploration space and mitigating the risk of becoming stuck in local optima, resulting in the discovery of more accurate and compact decision trees. Leveraging advanced feature selection techniques, including Best-First Search, Particle Swarm Optimization (PSO), Evolutionary Search, and Genetic Search (GS), along with contemporary dataset, this research aims to enhance the adaptability and resilience of NIDS against modern cyber threats. We conducted a comprehensive evaluation of the proposed approach against several well-known machine learning models, including AdaBoostM1 (AbM1), K-nearest neighbor (KNN), J48-Decision Tree (J48), multilayer perceptron (MLP), stochastic gradient descent (SGD), naïve Bayes (NB), and logistic model tree (LMT). The comparative analysis demonstrates the effectiveness and superiority of our method across various performance metrics, highlighting its potential to significantly enhance the capabilities of network intrusion detection systems.

Assessing of software security reliability: Dimensional security assurance techniques

Security plays a crucial role in ensuring the reliability of software systems, making a secure and dependable security framework vital for safeguarding software integrity. However, evaluating the dynamic and multifaceted aspects of security presents significant challenges, as various security metrics and factors complicate the assessment of reliability. This study advances the core concepts of software security through the application of security assurance techniques, including vulnerability scanning, code review, penetration testing, threat assessment, control evaluation, mitigation, risk assessment, and configuration review. In the context of the Software Security Reliability Model (SSRM), a framework was developed to enhance software security assurance across different stages. A comprehensive systematic literature review was conducted to identify security challenges, and the STRIDE and DREAD methodologies were applied to model security threats effectively. Additionally, a mathematical CVSS scoring method was utilized for risk assessment. The synthesis of diverse security methods, tools, attack patterns, and systems was analyzed, identifying 15 critical software security terms: authentication, authorization, encryption, access control, network security, application security, data security, incident response, compliance, threat intelligence, privacy protection, third-party risk, cloud security, endpoint security, and identity management. The findings highlight these terms as key contributors to improving software security reliability.

Corrosion attack in existing reinforced concrete structures: in-field investigation and analysis of naturally corroded bars

Corrosion of steel bars is a major issue for the management of existing reinforced concrete structures, since affecting many ageing structures and infrastructures and introducing risk for safety and reliability, as well as high maintenance costs. Intervention strategies should be planned and carried out according to the effective state of deterioration and to the expected evolution of performances over time. The critical parameters needed to evaluate these performances are the intensity and pattern of the corrosion attack, referring to both extension and depth of the attack. However, this information is rarely available and hard to predict when dealing with real structures. This paper presents a field investigation on existing corroded structures characterized by different environmental conditions. Corroded bars were removed from structural elements, the corrosion patterns were studied, and then the bars were tested to determine their mechanical properties. The objective of this study is to increase the knowledge of the effects of corrosion in natural environments and to propose a method to relate easy-measurable environmental conditions with the characteristics of the expected corrosion attack, which need to be further validated.