Distributed anomaly detection using concept drift detection based hybrid ensemble techniques in streamed network data
M Jain, G Kaur - Cluster Computing, 2021 - Springer
Ever since the internet became part of the everyday lives of humans providing network
security has been considered of utmost importance. Over the years lot of time and energy …
security has been considered of utmost importance. Over the years lot of time and energy …
An incremental learning method based on dynamic ensemble RVM for intrusion detection
Z Wu, P Gao, L Cui, J Chen - IEEE Transactions on Network …, 2021 - ieeexplore.ieee.org
Due to the dynamic changes of network data over time, static intrusion detection systems
cannot adapt well to the behavioral characteristics of the input network data, resulting in …
cannot adapt well to the behavioral characteristics of the input network data, resulting in …
Adversarial RL-based IDS for evolving data environment in 6LoWPAN
Low-power and Lossy Networks (LLNs) comprise nodes characterised by constrained
computational power, memory, and energy resources. The LLN nodes empower ubiquitous …
computational power, memory, and energy resources. The LLN nodes empower ubiquitous …
Building an intrusion detection system to detect atypical cyberattack flows
Artificial Intelligence (AI) techniques provide effective solutions for the detection of many
aberrant network traffic patterns and attack flows. However, the validation of these …
aberrant network traffic patterns and attack flows. However, the validation of these …
A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks
TJ Lucas, IS De Figueiredo, CAC Tojeiro… - IEEE …, 2023 - ieeexplore.ieee.org
Machine learning algorithms present a robust alternative for building Intrusion Detection
Systems due to their ability to recognize attacks in computer network traffic by recognizing …
Systems due to their ability to recognize attacks in computer network traffic by recognizing …
[HTML][HTML] Incremental hybrid intrusion detection for 6LoWPAN
Abstract IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) has grown
in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks …
in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks …
[HTML][HTML] A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
Incremental learning enables the learner to accommodate new knowledge without retraining
the existing model. It is a challenging task that requires learning from new data and …
the existing model. It is a challenging task that requires learning from new data and …
Autoencoders: a low cost anomaly detection method for computer network data streams
Computer networks are vulnerable to cyber attacks that can affect the confidentiality, integrity
and availability of mission critical data. Intrusion detection methods can be employed to …
and availability of mission critical data. Intrusion detection methods can be employed to …
Practical application of machine learning based online intrusion detection to internet of things networks
Internet of Things (IoT) devices participate in an open and distributed perception layer, with
vulnerability to cyber attacks becoming a key concern for data privacy and service …
vulnerability to cyber attacks becoming a key concern for data privacy and service …
Training A Dynamic Neural Network to Detect False Data Injection Attacks Under Multiple Unforeseen Operating Conditions
As a cyber-physical attack targeting power systems, False Data Injection Attack (FDIA) has
raised widespread concern in recent years. Many FDIA detection approaches in the …
raised widespread concern in recent years. Many FDIA detection approaches in the …