Performance evaluation of a combined anomaly detection platform
Hybrid Anomaly Detection Model (HADM) is a platform that filters network traffic and
identifies malicious activities on the network. The platform applies data mining techniques to …
identifies malicious activities on the network. The platform applies data mining techniques to …
Anomaly detection using ensemble techniques for boosting the security of intrusion detection system
IoT-based applications have witnessed a rapid surge in deployment in various domains. IoT
infrastructure is the nervous system responsible for the effective functioning of Smart Cities …
infrastructure is the nervous system responsible for the effective functioning of Smart Cities …
Machine learning techniques for anomaly-based detection system on CSE-CIC-IDS2018 dataset
Anomaly-based detection is a novel form of an intrusion detection system, which has
become the focus of many researchers for cybersecurity systems. Data manages most …
become the focus of many researchers for cybersecurity systems. Data manages most …
Anomaly detection for cyber-security based on convolution neural network: A survey
The expanding growth of computer and communication technologies results in a vast
amount of security concerns. Various types of cyber-security enabled mechanisms have …
amount of security concerns. Various types of cyber-security enabled mechanisms have …
[HTML][HTML] Anomaly detection optimization using big data and deep learning to reduce false-positive
Abstract Anomaly-based Intrusion Detection System (IDS) has been a hot research topic
because of its ability to detect new threats rather than only memorized signatures threats of …
because of its ability to detect new threats rather than only memorized signatures threats of …
Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic
N Moustafa - 2017 - unsworks.unsw.edu.au
Abstract Despite a Network Anomaly Detection System (NADS) being capable of detecting
existing and zero-day attacks, it is still not universally implemented in industry and real …
existing and zero-day attacks, it is still not universally implemented in industry and real …
[PDF][PDF] Anomalies Classification Approach for Network-based Intrusion Detection System.
Q Qassim, AM Zin, MJ Ab Aziz - Int. J. Netw. Secur., 2016 - academia.edu
Anomaly based intrusion detection system (A-IDS) is considered to be a better option than
signature based system since it does not require prior knowledge of attack signature before …
signature based system since it does not require prior knowledge of attack signature before …
ERCR TV: Ensemble of random committee and random tree for efficient anomaly classification using voting
A Niranjan, DH Nutan, A Nitish… - … for convergence in …, 2018 - ieeexplore.ieee.org
Anomaly Detection is widely used in applications related but not limited to intrusion
detection, fault detection, fraud detection, health monitoring systems and many other places …
detection, fault detection, fraud detection, health monitoring systems and many other places …
Evaluation of anomaly-based intrusion detection with combined imbalance correction and feature selection
AH Engly, AR Larsen, W Meng - … , VIC, Australia, November 25–27, 2020 …, 2020 - Springer
Intrusion detection systems (IDSs) are an important security mechanism to protect computing
resources under various environments. To detect malicious unknown events, machine …
resources under various environments. To detect malicious unknown events, machine …
[PDF][PDF] Hybrid Multi-Objective Deep Learning Model for Anomaly Detection in Cloud Computing Environment
RR Palle - International Journal of Scientific Research in Science …, 2015 - academia.edu
Cloud computing environments play a pivotal role in the IT landscape, seamlessly integrated
into the fabric of organizations and individuals' daily activities. Despite the myriad …
into the fabric of organizations and individuals' daily activities. Despite the myriad …