Cyber Security Monitoring Device

IAEME PUBLICATION, 2024

Artificial Intelligence (AI) has emerged as a transformative force in enhancing financial data security, offering advanced capabilities to detect, mitigate, and prevent cyber threats in the digital age. This paper explores the integration of AI technologies within the financial sector, focusing on its applications in predictive security analytics, automated threat detection, and regulatory compliance. Key topics include the fundamentals of AI in cybersecurity, challenges in securing financial data, and ethical considerations in AI deployment. Future trends such as quantum-resistant cryptography, autonomous security operations, and the evolution of ethical AI governance are also discussed. By examining current practices, emerging trends, and regulatory frameworks, this research highlights AI's pivotal role in fortifying financial data defenses and shaping the future of cybersecurity strategies. Embracing AI-driven innovations responsibly promises to strengthen resilience against evolving threats while maintaining trust, compliance, and operational efficiency in financial ecosystems.

Computers & Security, 2020

Given the global increase in crippling cyberattacks, organizations are increasingly turning to cyberthreat intelligence (CTI). CTI represents actionable threat information that is relevant to a specific organization and that thus demands its close attention. CTI efforts aim to help organizations “know their enemies better” for proactive, preventive, and timely threat detection and remediation—complementing conventional risk-management paradigms designed to improve ‘general readiness’ against known or unknown threats. Organizational security (OrgSec) and behavioral security research has lagged behind CTI’s growing potential to address current cybersecurity challenges. Instead, CTI has largely been the purview of computer science from an algorithmic perspective. However, OrgSec and behavioral researchers can contribute a further combined knowledge of design for the organization, human factors, and organizational governance to foster CTI. In this theory-building and review manuscript, we propose the CTI capability model (CTI-CM) to prescribe the key capabilities necessary for a CTI practitioner to engage effectively in CTI activities. The CTI-CM defines a practitioner’s CTI capability in terms of three highly interrelated but conceptually distinctive dimensions: analytical component capability, contextual response capability, and experiential practice capability. We further explain how these capabilities can be fostered, and the key implications for leading security practice in organizations.

IAEME PUBLICATION, 2024

The integration of predictive analytics with Security Information and Event Management (SIEM) systems represents a significant advancement in the field of cybersecurity, enhancing the ability to detect, respond to, and mitigate threats proactively. This research paper explores the foundational concepts of SIEM and predictive analytics, detailing their core functionalities and the benefits of their convergence. It examines various methods of integration, including direct integration, external analytics engines, and hybrid approaches, and provides practical strategies for implementation. Key benefits of integrating predictive analytics with SIEM systems include enhanced threat detection, improved incident response, optimized resource allocation, and better compliance reporting. However, the paper also addresses the challenges associated with this integration, such as ensuring data quality, maintaining model accuracy, managing integration complexity, and addressing performance and scalability concerns. By leveraging advanced machine learning and data processing techniques, organizations can achieve a more robust and resilient cybersecurity posture. The paper concludes with a discussion on future directions, emphasizing the potential for continued innovation in integrating predictive analytics with SIEM systems to address the ever-evolving landscape of cyber threats.

Sensors, 2021

Security Information and Event Management (SIEM) systems have been widely deployed as a powerful tool to prevent, detect, and react against cyber-attacks. SIEM solutions have evolved to become comprehensive systems that provide a wide visibility to identify areas of high risks and proactively focus on mitigation strategies aiming at reducing costs and time for incident response. Currently, SIEM systems and related solutions are slowly converging with big data analytics tools. We survey the most widely used SIEMs regarding their critical functionality and provide an analysis of external factors affecting the SIEM landscape in mid and long-term. A list of potential enhancements for the next generation of SIEMs is provided as part of the review of existing solutions as well as an analysis on their benefits and usage in critical infrastructures.

International Journal of Science and Research (IJSR), 2023

With the evolving sophisticated attack techniques and cyber-attacks, businesses must adapt their threat detection and response mechanisms. It is paramount to explorecontemporary tools, from real-time monitoring and network forensics to XDR,SIEM, SOAR, and NDR, giving insights into the ever-changing detection and response systems space. The migration of business data and applications to the cloud has dramatically improved security and threat detection. Conventional security approaches must be revised to guard against advanced threats within the fragile network infrastructures of cloud environments. By understanding this challenge, artificial intelligence (AI) comes in to help enhance the accuracy and speed of threat response and identification. This paper depicts the impact of AI on cloud security and threat detection. As cyber threats increasingly target service providers and cloud infrastructures, the demand for robust, easily deployable security measures remains essential. To address this issue, this paper will address the collaboration between cloud security and AI operations, stressing the resultant acceleration in incident response timesfurther depicting how this relationship strengthens an organization's defenses and curbs the impact of security incidents. For organizations looking to keep up with the dynamic threat landscape, leveraging and understanding the relationship between cloud security and AI is essential in maintaining an adaptive and resilient security posture.

arXiv (Cornell University), 2023

As digital technologies become more pervasive in society and the economy, cybersecurity incidents become more frequent and impactful. According to the NIS and NIS2 Directives, EU Member States and their Operators of Essential Services must establish a minimum baseline set of cybersecurity capabilities and engage in cross-border coordination and cooperation. However, this is only a small step towards European cyber resilience. In this landscape, preparedness, shared situational awareness, and coordinated incident response are essential for effective cyber crisis management and resilience. Motivated by the above, this paper presents PHOENI2X, an EU-funded project aiming to design, develop, and deliver a Cyber Resilience Framework providing Artificial-Intelligence-assisted orchestration, automation and response capabilities for business continuity and recovery, incident response, and information exchange, tailored to the needs of Operators of Essential Services and the EU Member State authorities entrusted with cybersecurity.

IEEE Access

Cyber Supply Chain (CSC) system is complex which involves different subsystems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, and Procedure (TT and P), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement. INDEX TERMS Cyber threat intelligence, machine learning, cyber supply chain, predictive analytic, cyber security, tactic techniques procedures.

2020

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PRAESTANTIA NEXUS DIGEST, 2024

The importance of an operating system expert in today's digital environment cannot be overstated. Cyber dangers are becoming more advanced and frequent at an unprecedented rate, affecting organizations, governments, and individuals. Operating system experts have the responsibility of both maintaining the functioning and performance of systems and strengthening them against persistent cyber threats, making them the first line of defense. This dual obligation requires a distinct combination of technical expertise, forward-thinking planning, and a steadfast dedication to ensuring security and resilience. "Steadfast Security: Mastering Resilience as an Operating System Expert Against Cyber Threats" is a crucial manual for individuals committed to effectively navigating the intricate and ever-changing landscape of cybersecurity. The purpose of this document is to provide operating system specialists with the essential knowledge, tools, and best practices to construct and sustain strong, resilient systems that can resist and recover from cyber disasters. To become proficient in resistance against cyber threats, one must first acquire a comprehensive comprehension of the existing danger environment and the necessity of maintaining constant awareness and knowledge updates. The importance of remaining vigilant and always updating one's knowledge cannot be emphasized enough, especially when it comes to zero-day vulnerabilities and advanced persistent threats.

International Journal of Scientific Research and Management , 2022

Predictive behavioral analytics and automated response (PBAAR) concepts have the potential to revolutionize cybersecurity. The main idea of predictive behavioral analytics is to analyze, extract, and automatically apply behavioral patterns to assess whether a particular activity is malicious. To achieve this goal, simple predictive models built by domain experts need to be developed, understood, and digitally implemented in the form of a sequential approximation of the expert's descriptive models. The expert's logic will be embedded into the constructed predictive model by writing a Python function or defining a decision table. Then, after exposure to examples of the relevant behavior, the resulting model becomes an integral part of real-time predictive analytics characterized by a built-in predictive behavioral task and built-in adaptive machine learning.Predictive behavioral analytics imply that predictive analytics can remove the necessity of the labeled training dataset, and then extraction of the feature subset and training of the classification models. The goal of the developed predictive model within PBAAR should be to automate the detection and resolution of cyber incidents. The key question in automated response is understanding how to define trigger conditions to fire an appropriate set of response activities (responder services, interaction, and decisionmaking), and how to construct decision tables or Python functions.

SSRN, 2024

The unprecedented pace of technology has been significantly influenced by the integration of Artificial Intelligence (AI). The ubiquity of AI spans various domains, garnering both criticism and acclaim. Its growing application presents both advantages and drawbacks in cybersecurity, as it becomes a standard component in the development and operational phases of contemporary technologies. This paper provides a comprehensive overview of AI utilization in cybersecurity, exploring its benefits, challenges, and potential negative impacts. In addition to that, it explores AI-based models that enhance or compromise security across various infrastructures and cyber networks. The paper critically examines the role of AI in developing cybersecurity applications, proposes strategies for leveraging emerging technologies to counteract AI-generated threats and vulnerabilities, and addresses the socioeconomic repercussions of the involvement of AI in cybersecurity.

GRADIVA REVIEW JOURNAL, 2024

As organizations increasingly migrate their operations to the cloud, the need for robust security measures becomes paramount. This research-oriented descriptive article explores the landscape of cloud security, focusing on ThreatDefender 360-a comprehensive security solution that leverages advanced threat intelligence. Drawing insights from case reports, case series, cross-sectional studies, ecological studies, surveys, observational studies, and case studies, this article aims to provide an in-depth analysis of the current state of cloud security and the transformative impact of ThreatDefender 360 in fortifying digital assets. Keywords, references, and future considerations are incorporated to guide further research in this crucial domain.

Journal of information security, 2024

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker's journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses' need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. "Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises" is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

JOURNAL OF BASIC SCIENCE AND ENGINEERING, 2019

In the realm of cloud cyber security, the evolution of AI-based threat intelligence has become pivotal in safeguarding digital assets against increasingly sophisticated threats. This paper proposes a comprehensive framework for leveraging artificial intelligence (AI) to enhance threat intelligence capabilities within cloud environments. The framework integrates various stages of threat intelligence, including data collection, preprocessing, and feature engineering, with advanced AI and machine learning techniques such as supervised and unsupervised learning, and deep learning models. Key components include the threat intelligence lifecycle of detection, analysis, and mitigation, supported by diverse data sources like network traffic, user activity logs, and external threat feeds. Integration with existing security systems, evaluation metrics, and realworld case studies highlight practical implementations and successes. Challenges such as data privacy, scalability, and model interpretability are discussed, with future directions focusing on AI advancements and collaborative efforts to address evolving threats.

2021

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.