Flexural behavior of functionally graded concrete beams with different patterns

With the rapid proliferation of cyber-physical systems (CPSs) in various sectors, including critical infrastructure, transportation, healthcare, and the energy industry, there is a pressing need for robust cybersecurity mechanisms to protect these systems from cyberattacks. A cyber-physical system is a combination of physical and cyber components, and a security breach in either component can lead to catastrophic consequences. Cyberattack detection and mitigation methods in CPSs involve the use of various techniques such as intrusion detection systems (IDSs), firewalls, access control mechanisms, and encryption. Overall, effective cyberattack detection and mitigation methods in CPSs require a comprehensive security strategy that considers the unique characteristics of a CPS, such as the interconnectedness of physical and cyber components, the need for real-time response, and the potential consequences of a security breach. By implementing these methods, CPSs can be better protected against cyberattacks, thus ensuring the safety and reliability of critical infrastructure and other vital systems. This paper reviews the various kinds of cyber-attacks that have been launched or implemented in CPSs. It reports on the state-of-the-art detection and mitigation methods that have been used or proposed to secure the safe operation of various CPSs. A summary of the requirements that CPSs need to satisfy their operation is highlighted, and an analysis of the benefits and drawbacks of model-based and data-driven techniques is carried out. The roles of machine learning in cyber assault are reviewed. In order to direct future study and motivate additional investigation of this increasingly important subject, some challenges that have been unaddressed, such as the prerequisites for CPSs, an in-depth analysis of CPS characteristics and requirements, and the creation of a holistic review of the different kinds of attacks on different CPSs, together with detection and mitigation algorithms, are discussed in this review.

/pdf/review-of-cyberattack-implementation-detection-and-dnzx3vwz.pdf

Review of Cyberattack Implementation, Detection, and Mitigation Methods in Cyber-Physical Systems

Mnoga izvjesca nacinjena nakon obilazaka potresom pogođenih podrucja pokazuju da su kratki stupovi
 vrlo osjetljivi elementi te da su izvor oste

/pdf/utjecaj-ispuna-od-betona-s-opekarskim-lomom-kao-agregatom-na-1czehic46h.pdf

Utjecaj ispuna od betona s opekarskim lomom kao agregatom na potresni odziv kratkih stupova

In modern power systems, predicting the time when peak loads will occur is crucial for improving efficiency and minimising the possibility of network sections becoming overloaded. However, most works in the load forecasting field are not focusing on a dedicated peak time forecast and are not dealing with load data privacy. At the same time, developing methods for forecasting peak electricity usage that protect customers' data privacy is essential since it could encourage customers to share their energy usage data, leading to more data points for the effective management and planning of power grids. Hence, the authors employ a dedicated Learning to Rank XGBoost algorithm to forecast peak times with only ranks of loads instead of absolute load magnitudes as input data, thereby offering potential privacy‐preserving properties. We show that the presented Learning to Rank XGBoost model yields comparable results to a benchmark XGBoost load forecasting model. Additionally, we describe our extensive feature engineering process and a state‐of‐the‐art Bayesian hyperparameter optimisation for selecting model parameters, which leads to a significant improvement of forecasting accuracy. Our method was used in the context of the final round of the international BigDEAL load forecasting challenge 2022, where we consistently achieved high‐ranking results in the peak time track and an overall fourth rank in the peak load forecasting track with our general XGBoost model.

Privacy‐preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering

Cybersecurity operations centers (CSOCs) protect organizations by monitoring network traffic and detecting suspicious activities in the form of alerts. The security response team within CSOCs is responsible for investigating and mitigating alerts. However, an imbalance between alert volume and available analysts creates a backlog, putting the network at risk of exploitation. Recent research has focused on improving the alert management process by triaging alerts, optimizing analyst scheduling, and reducing analyst workload through systematic discarding of alerts. However, these works overlook the delays caused in alert investigations by several factors, including: (i) False or benign alerts contributing to the backlog. (ii) Analysts experiencing cognitive burden from repeatedly reviewing unrelated alerts. (iii) Analysts being assigned to alerts that do not match well with their expertise. We propose a novel framework that considers these factors and utilizes machine learning and mathematical optimization methods to dynamically improve throughput during work shifts. The framework achieves efficiency by automating the identification and removal of a portion of benign alerts, forming clusters of similar alerts, and assigning analysts to alerts with matching attributes. Experiments conducted using real-world CSOC data demonstrate a 60.16% reduction in the alert backlog for an 8-hour work shift compared to currently employed approach.

A Machine Learning and Optimization Framework for Efficient Alert Management in a Cybersecurity Operations Center

Some devices in a network are more important than others, and potential issues with an important network device could cause significantly more damage than issues with less important devices. This paper proposes a method that can rank various device reports by using a learning-to-rank algorithm to help the end-user detect higher priority alarms easier. A pairwise learning-to-rank algorithm is used to incrementally train a model with a custom dataset while testing its accuracy on a separate testing set. Predicting the ranking for the first two, five, and all ten items in a testing dataset showed an upward trend of model accuracy. Using this method we were able to achieve a ranking accuracy surpassing 95%. The proposed method is discussed on an Intrusion Detection System case study.

Ranking Network Devices for Alarm Prioritisation: Intrusion Detection Case Study

/pdf/proracun-i-konstruiranje-kratkih-armiranobetonskih-stupova-1vabkobm68.pdf

Proračun i konstruiranje kratkih armiranobetonskih stupova

/pdf/high-strength-reinforced-concrete-elements-subjected-to-14v88jsgoa.pdf

High strength reinforced concrete elements subjected to bending

Prikazana su istraživanja i proračun duktilnih niskih armiranobetonskih zidova, naprezanih poprečnim silama, kojima je hrbat armiran kosim šipkama. Pokusima je utvrđeno da se ovako armiran hrbat duktilnije ponaša od zida armiranog mrežom od vertikalnih i horizontalnih šipki. Za pronalaženje izraza za dimenzioniranje upotrijebljen je model rešetke s tlačnim betonskim i vlačnim čeličnim štapovima. Ugradnjom kosih šipki postiže se mala ušteda u armaturi hrpta, ali treba više rada.

/pdf/duktilni-zidovi-s-dijagonalnom-armaturom-hrpta-1zdv7w72yg.pdf

Duktilni zidovi s dijagonalnom armaturom hrpta

/pdf/analysis-of-lightweight-aggregate-concrete-beams-2n677748j7.pdf

Analysis of lightweight aggregate concrete beams

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