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[HTML][HTML] Cybersecurity knowledge extraction using xai
Global networking, growing computer infrastructure complexity and the ongoing migration of
many private and business aspects to the electronic domain commonly mandate using …
many private and business aspects to the electronic domain commonly mandate using …
Unr-idd: Intrusion detection dataset using network port statistics
Multiple datasets have been proposed to create Machine Learning (ML)-based Network
Intrusion Detection Systems (NIDS). However, many of these datasets suffer from sub …
Intrusion Detection Systems (NIDS). However, many of these datasets suffer from sub …
Poisoning the well: Adversarial poisoning on ML-based software-defined network intrusion detection systems
With the usage of Machine Learning (ML) algorithms in modern-day Network Intrusion
Detection Systems (NIDS), contemporary network communications are efficiently protected …
Detection Systems (NIDS), contemporary network communications are efficiently protected …
Flood control: Tcp-syn flood detection for software-defined networks using openflow port statistics
As software-defined network (SDN) adoption increases, it becomes increasingly important to
develop effective solutions to defend them against cyber attacks. A prominent cyberattack …
develop effective solutions to defend them against cyber attacks. A prominent cyberattack …
GCAP: Cyber Attack Progression Framework for Smart Grid Infrastructures
Interdisciplinary developments like the Smart Grid (SG) provide enhanced functionality like
efficient power delivery, reliability, and safety while ensuring the smooth integration of …
efficient power delivery, reliability, and safety while ensuring the smooth integration of …
Bringing To Light: Adversarial Poisoning Detection for ML-based IDS in Software-defined Networks
Machine learning (ML)-based network intrusion detection systems (NIDS) have become a
prospective approach to efficiently protect network communications. However, ML models …
prospective approach to efficiently protect network communications. However, ML models …
Bringing To Light: Adversarial Poisoning Detection in Multi-controller Software-defined Networks
Machine learning (ML)-based network intrusion detection systems (NIDS) have become a
contemporary approach to efficiently protect network communications from cyber attacks …
contemporary approach to efficiently protect network communications from cyber attacks …
Early Detection of DDoS Attacks in SDN using Machine Learning Techniques
PS Pericherla, SK Thangavel… - 2023 14th …, 2023 - ieeexplore.ieee.org
Software-defined networking (SDN) have emerged as a popular approach to manage
network traffic in data centers. By separating the control plane from the data plane, SDN …
network traffic in data centers. By separating the control plane from the data plane, SDN …
Telling Apart: ML Framework Towards Cyber Attack and Fault Differentiation in Microgrids
Microgrids have revolutionized the field of power systems as they provide higher reliability
and power quality over traditional power systems by possessing the ability to self-supply …
and power quality over traditional power systems by possessing the ability to self-supply …
Small, but Mighty: Lightweight ML-Enabled Intrusion Detection Framework for Vehicular Ad-Hoc Networks
M Dinh, M Patel, T Das… - 2024 IEEE 3rd World …, 2024 - ieeexplore.ieee.org
Vehicular Ad-Hoc Networks (VANETs) have become an integral component of contemporary
vehicular technology. This technology provides advanced features like traffic and weather …
vehicular technology. This technology provides advanced features like traffic and weather …