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Intrusion Classification and Detection System Using Machine Learning Models on NSL-KDD Dataset
Intrusion detection systems are crucial to cyberattack protection. This paper presents an intrusion detection system (IDS) architecture that uses...
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Enhancing Accuracy with Recursive Feature Selection Using Multiple Machine Learning and Deep Learning Techniques on NSL-KDD Dataset
The world has moved toward digital revolution and more and more services are now being available online. This has presented significant challenges in...
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Comparative Analysis of CatBoost Against Machine Learning Algorithms for Classification of Altered NSL-KDD
The importance of information security, especially in network environments, has increased as a result of the expanding volume of data stored in...
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Intrusion Detection Systems Using Support Vector Machines on the KDDCUP’99 and NSL-KDD Datasets: A Comprehensive Survey
With the growing rates of cyber-attacks and cyber espionage, the need for better and more powerful intrusion detection systems (IDS) is even more...
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Comparison of Principle Component Analysis and Stacked Autoencoder on NSL-KDD Dataset
In the traditional era, there was no concern of time and the memory space, the processing was the main issues to solve any problem. But in the modern...
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Structural Analysis of the NSL-KDD Data Sets for Solving the Problem of Attacks Detection Using ML/DL Methods
The study explores the potential for network intrusion detection using the popular NSL-KDD dataset. Before applying ML/DL methods, various options...
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A Deep Learning Model for Intrusion Detection with Imbalanced Dataset
The frequency of cyberattacks increases as network technology evolves and Internet services become more widely utilized. As more individuals and...
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Feature Extraction and Anomaly Detection Using Different Autoencoders for Modeling Intrusion Detection Systems
Maintaining network security by preventing attacks is essential for a network intrusion detection system. Machine learning techniques heavily depend...
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An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer
In recent years, developed Intrusion Detection Systems (IDSs) perform a vital function in improving security and anomaly detection. The effectiveness...
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A Novel Intelligent Intrusion Prevention Framework for Network Applications
Nowadays, the intrusion prevention model in network applications is essential in protecting data from malicious users. The intrusion prevention model...
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IoT intrusion detection model based on gated recurrent unit and residual network
The sample data of the existing intrusion detection models of the Internet of Things has defects such as class imbalance and insufficient feature...
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A distributed platform for intrusion detection system using data stream mining in a big data environment
With the growth of computer networks worldwide, there has been a greater need to protect local networks from malicious data that travel over the...
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Analysis of Machine Learning Classification Techniques for Anomaly Detection with NSL-KDD Data Set
Along with the high-speed growth of the Internet, cyber-attacks are becoming even more frequent, so detecting network intrusions is essential for...
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Enhancing intrusion detection recursive feature elimination with resampling in WSN
With the proliferation of technologies such as the Internet of Things, Cloud computing, and Social Networking, large quantities of network traffic...
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CNN-GRU-FF: a double-layer feature fusion-based network intrusion detection system using convolutional neural network and gated recurrent units
Identifying and preventing malicious network behavior is a challenge for establishing a secure network communication environment or system. Malicious...
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Toward support-vector machine-based ant colony optimization algorithms for intrusion detection
One of the major challenges of network traffic analysis is intrusion detection. Intrusion detection systems (IDSs) are designed to detect malicious...
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Cloud intrusion detection framework using variational auto encoder Wasserstein generative adversarial network optimized with archerfish hunting optimization algorithm
The cloud computing environment has been severely harmed by security issues, which has a negative impact on the healthy and sustainable development...
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A Novel Cyber Security Model Using Deep Transfer Learning
Preventing attackers from interrupting or totally stop** critical services in cloud systems is a vital and challenging task. Today, machine...
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HIDM: A Hybrid Intrusion Detection Model for Cloud Based Systems
The cloud computing model is very popular among the users in different sectors like banking, healthcare, education etc due to its customized low-cost...
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Adapting deep learning-LSTM method using optimized dataset in SDN controller for secure IoT
The Internet of Things (IoT) has grown into various enterprises. While the IoT ecosystem's extensive and open environment has many advantages, it can...
