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Denial-of-Service (DoS) Threat Detection Using Supervised Machine Learning Algorithms on CICIDS2018 Dataset
Identification and protection against ever-growing cyber threats is a constantly evolving challenge for traditional Intrusion Detection Systems....
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Designing Intelligent Intrusion Detection System for Industry 4.0 Using Feature Learning Techniques
Increase in connectivity and cost pressure has pushed Industry 4.0 to rely on the systems built over Internet of Things (IoT). These IoT devices are...
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Deep Convolutional Neural Network for Active Intrusion Detection and Protect data from Passive Intrusion by Pascal Triangle
Active and passive intrusion are the two types of intrusion. The active intrusion attempts to modify the data and the passive intrusion observes the...
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Boosting Algorithms-Based Intrusion Detection System: A Performance Comparison Perspective
An intrusion detection system (IDS) monitors the system’s behavior and network for suspicious activities. IDS was first proposed in 1980, and it has...
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Hybrid architecture for mitigating DDoS and other intrusions in SDN-IoT using MHDBN-W deep learning model
The Internet of Things (IoT) connects billions of devices. However, because of its heterogeneous system and broad connectivity, it is vulnerable to...
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Probability Boosted Regression for Intrusion Detection in Cyberactive Space
Protecting cyber-physical infrastructure from random network attacks is critical in this age of widespread Internet connectivity. This will prevent...
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Performance and Complexity Tradeoffs of Feature Selection on Intrusion Detection System-Based Neural Network Classification with High-Dimensional Dataset
In the realm of cyber security, particularly in the area of network security, IDS has recently attracted researchers’ attention since it has been...
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CoWrap: An Approach of Feature Selection for Network Anomaly Detection
Feature Selection (FS) is a crucial technique that picks out the most significant features from an augmented and ambiguous feature set to increase...
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BotDefender: A Collaborative Defense Framework Against Botnet Attacks using Network Traffic Analysis and Machine Learning
Botnets, an army of remotely controlled compromised devices called bots, routinely cause severe damage to infrastructures and organizations. Since...
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Ensemble Learning Based Big Data Classification for Intrusion Detection
The growth of technology has made life much easier with its speed, but we cannot deny that it suers from multiple security problems. Therefore, the...
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A Comprehensive Analysis of Novel Intrusion Detection Systems for Internet of Things Networks
In recent years, the Internet of Things (IoT) paradigm has shown massive embracing by various industries notably the medical sector, vehicle...
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Study of Class Incremental Learning Strategies for Intrusion Detection System
In today’s digital world, the Internet connects many apps and gadgets. This advancement is beneficial in many aspects, but on the other hand, it...
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Meta learning-based few-shot intrusion detection for 5G-enabled industrial internet
With the formation and popularization of the 5G-enabled industrial internet, cybersecurity risks are increasing, and the limited number of attack...
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Interpreting Intrusions - The Role of Explainability in AI-Based Intrusion Detection Systems
Machine learning has become a key component of the effective detection of network intrusions. Yet, it comes with the lack of transparency - an issue...
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A Systematic Literature Review of Network Intrusion Detection System Models
Rapid growth in the communication and Internet domains requires large network data and network size. Thereby, in such a network, several new attacks...
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Accuracy Improvement of Network Intrusion Detection System Using Bidirectional Long-Short Term Memory (Bi-LSTM)
An intrusion detection system (IDS), sometimes known as an infiltration prevention system, is an active defensive mechanism deployed by the Internet...
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Multi-layer Intrusion Detection on the USB-IDS-1 Dataset
Intrusion detection plays a key role in a modern cyber security system. In recent years, several research studies have utilized state-of-the-art...
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Intrusion Detection in IoT Devices Using ML and DL Models with Fisher Score Feature Selection
IoT devices are physical things that have sensors, network connectivity, and software built into them. This allows them to gather data and share it...
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Classification of Bruteforce Attacks Using Convolution Neural Network
The computer technology is being advanced day by day such that it results in being subjected to various data breaches and network attacks that might...
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Intrusion Detection System Using Big Data Based Hybrid Hierarchical Model (BDHHM) of Deep Learning
The world interconnectivity for the Internet infrastructure has been increasing incredibly because of the enormous increase of data day by day. As...