Benchmarking of machine learning for anomaly based intrusion detection systems in the CICIDS2017 dataset
An intrusion detection system (IDS) is an important protection instrument for detecting
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms …
Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree
Cyber-attacks pose increasing challenges in precisely detecting intrusions, risking data
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
confidentiality, integrity, and availability. This review paper presents recent IDS taxonomy, a …
Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks
PR Kanna, P Santhi - Expert Systems with Applications, 2022 - Elsevier
The recent advancements in information and communication technologies have led to an
increasing number of online systems and services. These online systems can utilize …
increasing number of online systems and services. These online systems can utilize …
Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection
Intrusion detection is a fundamental part of security tools, such as adaptive security
appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various …
appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various …
A double-layered hybrid approach for network intrusion detection system using combined naive bayes and SVM
T Wisanwanichthan, M Thammawichai - Ieee Access, 2021 - ieeexplore.ieee.org
A pattern matching method (signature-based) is widely used in basic network intrusion
detection systems (IDS). A more robust method is to use a machine learning classifier to …
detection systems (IDS). A more robust method is to use a machine learning classifier to …
A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection
Today's internet data primarily consists of streamed data from various applications like
sensor networks, banking data and telecommunication data networks. A new field of study …
sensor networks, banking data and telecommunication data networks. A new field of study …
An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset
Intrusion detection system (IDS) has been developed to protect the resources in the network
from different types of threats. Existing IDS methods can be classified as either anomaly …
from different types of threats. Existing IDS methods can be classified as either anomaly …
UIDS: a unified intrusion detection system for IoT environment
Intrusion detection system (IDS) using machine learning approach is getting popularity as it
has an advantage of getting updated by itself to defend against any new type of attack …
has an advantage of getting updated by itself to defend against any new type of attack …
[PDF][PDF] Detecting malicious dns over https traffic in domain name system using machine learning classifiers
YM Banadaki, S Robert - Journal of Computer Sciences and …, 2020 - researchgate.net
This paper presents a systematic two-layer approach for detecting DNS over HTTPS (DoH)
traffic and distinguishing Benign-DoH traffic from Malicious-DoH traffic using six machine …
traffic and distinguishing Benign-DoH traffic from Malicious-DoH traffic using six machine …
Big data-aware intrusion detection system in communication networks: a deep learning approach
One of the most important parameters that hackers have always considered is obtaining
information about the status of computer networks, such as hacking into databases and …
information about the status of computer networks, such as hacking into databases and …