An Improved Convolutional Neural Network Model for Intrusion Detection in Networks

Khan, Riaz Ullah; Zhang, Xiaosong; Alazab, Mamoun; Kumar, Rajesh

doi:10.1109/ccc.2019.000-6

Cited by 108 publications

(39 citation statements)

References 12 publications

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“…Based on the performance of the DNN model, the authors have proposed a scalable hybrid IDS-AlertNet system that would efficiently monitor network traffic and proactively send alert on cyber attacks. In coordination with the use of DNN, convolutional neural network algorithms have also been identified as an advanced and superior technology for extraction of features in an intrusion dataset for the classification purposes [12].…”

Section: Related Workmentioning

confidence: 99%

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

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Section: Related Workmentioning

confidence: 99%

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

et al. 2020

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“…CNN is widely being used in image recognition [242], drug discovery [243], and anomaly detection [244,245], to name a few. Riaz et al proposed an improved version of CNN for intrusion detection with an accuracy of 99.23% using the KDD99 dataset [246]. CNN has also been used widely for the classification of malicious traffic [247][248][249].…”

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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“…We are also using a "magic function" -%matplotlib inline. A magic function has the advantage that it is not necessary to invoke the function explicitly (Khan et al, 2019). Python does the work for it.…”

Section: Proposed Framework With Implementation Workmentioning

confidence: 99%

Classification model for accuracy and intrusion detection using machine learning approach

Agarwal

Sharma

Alshehri

et al. 2021

PeerJ Computer Science

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In today’s cyber world, the demand for the internet is increasing day by day, increasing the concern of network security. The aim of an Intrusion Detection System (IDS) is to provide approaches against many fast-growing network attacks (e.g., DDoS attack, Ransomware attack, Botnet attack, etc.), as it blocks the harmful activities occurring in the network system. In this work, three different classification machine learning algorithms—Naïve Bayes (NB), Support Vector Machine (SVM), and K-nearest neighbor (KNN)—were used to detect the accuracy and reducing the processing time of an algorithm on the UNSW-NB15 dataset and to find the best-suited algorithm which can efficiently learn the pattern of the suspicious network activities. The data gathered from the feature set comparison was then applied as input to IDS as data feeds to train the system for future intrusion behavior prediction and analysis using the best-fit algorithm chosen from the above three algorithms based on the performance metrics found. Also, the classification reports (Precision, Recall, and F1-score) and confusion matrix were generated and compared to finalize the support-validation status found throughout the testing phase of the model used in this approach.

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An Improved Convolutional Neural Network Model for Intrusion Detection in Networks

Cited by 108 publications

References 12 publications

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Classification model for accuracy and intrusion detection using machine learning approach

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