A model-free voltage stability security assessment method using artificial intelligence

Bastos, M. R.; Martini, José Sidnei Colombo

doi:10.1109/isgt-la.2015.7381238

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Cybersecurity defenses are put in place to counteract three categories of attacks: conventional attacks that make use of well-known weaknesses, sophisticated attacks that exploit complex flaws, and emerging attacks that exploit newly discovered weaknesses. The information system has infiltrated several aspects of the company, such as the production, operation, and management departments (Bastos, & Martini, 2015). These advancements entail the need for reliable information security policies and solutions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Naseer

2024

ABBDM

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Contemporary institutions are consistently confronted with fraudulent activities that exploit weaknesses in interconnected systems. Securing critical data against unauthorized access by hackers and other cybercriminals requires the application of robust cybersecurity protocols. As the number and complexity of cyber threats continue to grow, innovative prevention strategies are required. The objective of this study is to investigate the correlation between machine learning (ML) and cyber threat intelligence (CTI) to improve cybersecurity strategies. For the detection of anomalies, the analysis of malware, and the prediction of threats, ML techniques are indispensable in industries including retail, finance, healthcare, and cybersecurity. By employing critical threat information (CTI), security teams can gain a comprehensive understanding of adversary strategies and bolster defensive measures; thus, they play a pivotal role in proactive defense. Integration of ML and CTI facilitates exhaustive analysis by automating the acquisition, processing, and categorization of data. However, obstacles arise when confronted with issues such as risk assessment, the requirement for precise data, and the initial stages of machine learning implementation in business intelligence. In this paper, we present an extensive examination of the current literature concerning the visualization of Cyber Threat Intelligence (CTI) and the utilization of Machine Learning (ML. Therefore, the report concludes with an analysis of emergent threats, potential future applications of AI and ML in the field of cyber threat intelligence, and the critical contribution of machine learning to the improvement of cybersecurity.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Naseer

2024

ABBDM

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“…Cyber Security defenses are carried in concern for common attacks (manipulating known vulnerabilities), advanced attacks (abusing complex vulnerabilities) and emerging attacks (new attacks vulnerabilities). The information system has entered into various aspects of the organization such as production, operation, and management departments [3]. These advancements require the need for reliable information security techniques and systems.…”

Section: Introductionmentioning

confidence: 99%

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

Gupta*¹,

Sabitha²,

Punhani³

2019

IJRTE

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Threat intelligence is the procurement of evidence-based knowledge about current or potential threats. The interest of threat intelligence comprises of advancement in efficiency and boosting effectiveness in terms of analytical and prevention capabilities. Cybersecurity represents serious interest for numerous organizations because maximum of them are using Internet-connected data devices which are opening doors for cyber attackers. Outstanding threat intelligence within the cyber sphere requests for the knowledge base of threat information and a thoughtful way to represent this knowledge. This study proposes a clear rationale of significant artificial intelligence (AI) techniques used for recognizing a cyber-attack. Data analysis can be formulated to guide industries and Internet-connected systems such as smartphones or robotic factories on what to do in the appearance of an incident. AI techniques will analyze past incidents and summarize knowledge from experts and will continue to adapt or reform new branches as it reviews from the new incidents. In addition, various data mining approaches used in boosting threat truthfulness in cybersecurity data are also studied. To conclude, we discussed that; AI will robotize the collation of machine-readable external threats and will improve the efficiency and accuracy of the data for each smart organization’s specific framework.

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A model-free voltage stability security assessment method using artificial intelligence

Cited by 2 publications

References 21 publications

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Machine Learning Applications in Cyber Threat Intelligence: A Comprehensive Review

Cyber Security Threat Intelligence using Data Mining Techniques and Artificial Intelligence

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