The evolution of cybersecurity has witnessed a transformative shift from reactive defense measures to proactive threat-hunting and risk-mitigation strategies. In response to the rapidly evolving threat landscape, the integration of Artificial Intelligence (AI) into Security Information and Event Management (SIEM) tools has emerged as a crucial solution. Historically, SIEMs primarily aggregated security data but struggled to analyze the vast, complex datasets effectively. The integration of AI, especially Machine Learning (ML) and Deep Learning (DL), revolutionized these systems. AI algorithms enable SIEMs to extract meaningful insights from massive datasets, allowing for the identification of subtle anomalies and hidden threats that may not be detected by traditional detection methods. This transition marks a fundamental shift from simple data aggregation to intelligent analysis, empowering SIEMs to move beyond detection towardproactive threat hunting. This paper highlights the role of AI in predicting threats, leveraging historical data to forecast potential risks, and continuously learning to adapt to evolving threat landscapes. It also explores the real-world use cases of AI-powered SIEMs in proactive threat hunting and risk mitigation.
The paper considers a boundary value problem for a third order with no smooth coefficients and pure derivatives. Odds. This is due to the fact to introduce the concept of the conjugate Green's function. It is very difficult to write the form of the conjugate differential operator corresponding to equation in the Lagrange sense. Therefore, in this work, without using strict conditions smoothness under the conditions and boundedness, an explicit form is found conjugate operator since the initial-boundary value problem for integral-differential equations has been studied based on the introduction special conjugate systems in the form of an integral-algebraic equations’ system. In this article, it can be said that Green's function is considered based on Lagrange's formula for the third-order differential equation with boundary conditions and its conjugate.
A brief description on polymer flat plat solar collector manufacturing, design, and applications are given in this work. The main obstacles that face these collectors type, and how can be processed are also discussed. It is found that polymer low thermal conductivity, and degradation are the most essential difficulties in this industry, and increase heat transfer area and additives are the best common solutions. While stabilizers can be added to increase polymer life time.
The ever-growing cost of cybercrime has created the need for proactive solutions for organizations seeking to protect their digital assets. While traditional security systems struggle to detect anomalies buried within vast datasets, new solutions like User and Entity Behavior Analytics (UEBA) emerge as a game-changer. By leveraging the power of machine learning, UEBA analyzes diverse data sources like user logins, file accesses, event logs, business context, externalthreat intelligence, and network activity, to unveil hidden threats most traditional methods could miss. The ability to analyze multiple data sources enables UEBA solutions to effectively detect malicious insiders, compromised users, Advanced Persistent Threats (APTs), and zero-day attacks. By using various analytics techniques like supervised learning, unsupervised learning, and statistical modeling, UEBA solutions can detect subtle anomalies that deviate fromestablished behavior baselines. Despite the many benefits, UEBA solutions still have limitations like data quality concerns, high implementation costs, and the need for model maintenance. Integration with System Information and Event Management (SIEM) systems helps mitigate some of these challenges to further enhance UEBA's capabilities and provide a unified platform for threat identification and response. This paper provides a detailed insight into the capabilities ofUEBA, its three pillars, significance, and limitations.
The paper aims to investigate the influencing of marketing mix (MM) elements (product, price, place or distribution, and promotion) on increasing the effectiveness of product promotion and their role to reduce the problems within the organization. The main importance aspects of this paper are to discuss the theoretical part of MM, to provide some perspectives for the researchers, and to give some instructions for the marketing department in Al-Saaeda Company for medical equipment technologies. The researchers used the main related academic resources from university library, and internet, and they designed and distributed questionnaires on a random sample of Al-Saaeda Company for Medical Equipment Technologies customers and the company employees to measure the impact of promotion on the marketing of its product (Glucocard 01-mini plus). The main findings of this paper can be concluded as following: 1. The promotion has a very high level of impact to increase the sales of products. 2. The good distribution of product can effect positively on customer satisfaction. 3. The company's policy for promoting has a very good reflection on increasing the sales of products. The researchers recommended that the company must strengthen the level of promotions in its activities and departments, and the increasing of sales points is very important, so the company must enhance its policies of distribution.
