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Error prevalence in nids datasets: A case study on cic-ids-2017 and cse-cic-ids-2018
Benchmark datasets are heavily depended upon by the research community to validate
theoretical findings and track progression in the state-of-the-art. NIDS dataset creation …
theoretical findings and track progression in the state-of-the-art. NIDS dataset creation …
Optimizing cybersecurity attack detection in computer networks: a comparative analysis of bio-inspired optimization algorithms using the CSE-CIC-IDS 2018 dataset
H Najafi Mohsenabad, MA Tut - Applied Sciences, 2024 - mdpi.com
In computer network security, the escalating use of computer networks and the
corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) …
corresponding increase in cyberattacks have propelled Intrusion Detection Systems (IDSs) …
[PDF][PDF] Towards Data-Driven Network Intrusion Detection Systems: Features Dimensionality Reduction and Machine Learning.
Cyber attacks have increased in tandem with the exponential expansion of computer
networks and network applications throughout the world. Fortunately, various machine/deep …
networks and network applications throughout the world. Fortunately, various machine/deep …
CNN-based intrusion detection software for network operating system environment
Cybersecurity represents an important challenge specific to digital technology in the modern
world, and is of vital importance for reducing or even preventing the impact of cybercrime …
world, and is of vital importance for reducing or even preventing the impact of cybercrime …
[HTML][HTML] Enhanced Hybrid Deep Learning Models-Based Anomaly Detection Method for Two-Stage Binary and Multi-Class Classification of Attacks in Intrusion …
As security threats become more complex, the need for effective intrusion detection systems
(IDSs) has grown. Traditional machine learning methods are limited by the need for …
(IDSs) has grown. Traditional machine learning methods are limited by the need for …
Securing Networks: A Deep Learning Approach with Explainable AI (XAI) and Federated Learning for Intrusion Detection
In the constantly evolving field of cybersecurity, safeguarding sensitive data from hazardous
incidents is critical. Traditional intrusion detection systems (IDS) frequently rely on …
incidents is critical. Traditional intrusion detection systems (IDS) frequently rely on …
Öznitelik seçme yöntemlerinin makine öğrenmesi tabanlı saldırı tespit sistemi performansına etkileri
Artan İnternet tabanlı teknolojilerin kullanımı insanlara ve kurumlara önemli avantajlar
sağlamanın yanı sıra bir takım dezavantajları da beraberinde getirmiştir. Bunlardan en …
sağlamanın yanı sıra bir takım dezavantajları da beraberinde getirmiştir. Bunlardan en …
A Novel Two-Stage Classification Architecture Integrating Machine Learning and Artificial Immune System for Intrusion Detection on Balanced Dataset
Effective intrusion detection in cybersecurity is crucial, and previous studies have
investigated many approaches, such as Machine Learning (ML), Deep Learning (DL), and …
investigated many approaches, such as Machine Learning (ML), Deep Learning (DL), and …
Machine Learning based Network Intrusion Detection with Hybrid Frequent Item Set Mining
Bilgisayar ağlarının her geçen gün gelişmesi ve genişlemesi ve geliştirilen yazılımların
çeşitliliği ile muhtemel saldırıların neden olabileceği zararlar tahminlerin ötesinde …
çeşitliliği ile muhtemel saldırıların neden olabileceği zararlar tahminlerin ötesinde …