The field of cybersecurity is rapidly evolving, with a growing focus on advanced threat intelligence and innovative defense strategies. Recent research has highlighted the importance of public awareness, cybersecurity education, and robust regulatory frameworks in combating cyber threats. The increasing digitization of various sectors, including agriculture and healthcare, has introduced new cybersecurity challenges, and researchers are exploring the use of machine learning and artificial intelligence to strengthen cybersecurity resilience. Notable papers in this area include the introduction of the Cybersecurity Improvement Initiative for Agriculture (CIIA), which aims to strengthen cybersecurity awareness and resilience among farmers and food producers. Another significant contribution is the proposal of a conceptual model for threat intelligence event extraction, which leverages artificial intelligence and machine learning to enhance the efficiency of cyber threat intelligence data collection. Additionally, the development of neurosymbolic artificial intelligence for robust network intrusion detection has shown promising results, outperforming traditional neural models in classification accuracy and false omission rate.
Cybersecurity Advancements and Threat Intelligence
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A comprehensive survey of cybercrimes in India over the last decade
Strengthening Cybersecurity Resilience in Agriculture Through Educational Interventions: A Case Study of the Ponca Tribe of Nebraska
Predicting Blood Type: Assessing Model Performance with ROC Analysis
A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges
A Threat Intelligence Event Extraction Conceptual Model for Cyber Threat Intelligence Feeds
Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learning
Classifying Dental Care Providers Through Machine Learning with Features Ranking
Comparative performance of ensemble models in predicting dental provider types: insights from fee-for-service data
On Automating Security Policies with Contemporary LLMs