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Articles by Flow Bytes
Adversarial Sample Generation Based on Model Simulation Analysis in Intrusion Detection Systems
1. Introduction With the continuous escalation of hacker attacks and the large-scale growth of network traffic data, machine learning-based malicious traffic detection methods are becoming the mainstream solution for intrusion detection systems (IDSs).
Research on Multi-Layer Defense against DDoS Attacks in Intelligent Distribution Networks
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
An efficient intrusion detection system for IoT security using CNN decision forest
The accelerated advancement of technology and its increasing integration into various aspects of our lives have led to a surge in the proliferation of smart interconnected objects. In this landscape, IoT has emerged as a propelling force behind numerous industries and large-scale enterprises (Xu, He & Li, 2014). It incorporates an array of sensors, actuators, and interconnected objects, reducing or even eliminating the need for human intervention (Delsing et al., 2016).
Eye-Net: A Low-Complexity Distributed Denial of Service Attack-Detection System Based on Multilayer Perceptron
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Unveiling Malicious Network Flows Using Benford’s Law
1. Introduction The increase in cyber-attacks poses various problems associated with security flaws in computer networks, whether physical or cloud-based, including vulnerabilities that allow attackers to exploit weaknesses in network protocols and carry out malware attacks, such as ransomware infections, compromising data integrity and confidentiality [1,2].
Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning
Abstract : The advancements and reliance on digital data necessitates dependence on information technology. The growing amount of digital data and their availability over the Internet have given rise to the problem of information security. With the increase in connectivity among devices and networks, maintaining the information security of an asset has now become essential for an organization.
A DDoS Attack Detection Method Based on Natural Selection of Features and Models
Abstract : Distributed Denial of Service (DDoS) is still one of the main threats to network security today. Attackers are able to run DDoS in simple steps and with high efficiency to slow down or block users’ access to services. In this paper, we propose a framework based on feature and model selection (FAMS), which is used for detecting DDoS attacks with the aim of identifying the features and models with a high generalization capability, high prediction accuracy, and short prediction time.
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