Analysis of Support Vector Machine-based Intrusion Detection Techniques

Bhati, Bhoopesh Singh; S., C.

doi:10.1007/s13369-019-03970-z

Cited by 66 publications

(24 citation statements)

References 20 publications

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“…The dataset is preprocessed to maintain the time variable at a single column from the starting date of COVID-19 to the current data used in the study. For merging several attributes, we employed inner joins and other concatenation approaches used to pre-process the datasets [ 9 , 10 ]. After that, the data is passed to the deep learning models i.e RNN, LSTM, ANN, and GRU for training purposes.…”

Section: Resultsmentioning

confidence: 99%

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Rauf

Lali

Khan

et al. 2021

Pers Ubiquit Comput

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Section: Resultsmentioning

confidence: 99%

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Rauf

Lali

Khan

et al. 2021

Pers Ubiquit Comput

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“…Xu et al [19] applied K-nearest neighbors (K-NN) for anomaly ID, and evaluated the efficacy of the proposed ID system using the KDDCup ID dataset. Bhati et al [20] applied variants of support vector machine (SVM), such as quadratic, linear, fine, and medium Gaussian, to analyze the performance of SVM techniques using the NSL-KDD dataset. An integrated ID system was developed by Sumaiya et al [21] using correlation-based feature selection and an artificial neural network (ANN).…”

Section: Related Workmentioning

confidence: 99%

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Khan

2021

Processes

165

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Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

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“…It can be used for regression, but mostly it is used in classification algorithms. In SVM, we sketch data items by the point in an ndimensional area where n represents the considered features [25]. It creates a hyper plane and separates the data into classes [26].…”

Section: ) Support Vector Machinementioning

confidence: 99%

Attack Detection in IoT using Machine Learning

Anwer

Khan

Farooq

et al. 2021

Eng. Technol. Appl. Sci. Res.

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Many researchers have examined the risks imposed by the Internet of Things (IoT) devices on big companies and smart towns. Due to the high adoption of IoT, their character, inherent mobility, and standardization limitations, smart mechanisms, capable of automatically detecting suspicious movement on IoT devices connected to the local networks are needed. With the increase of IoT devices connected through internet, the capacity of web traffic increased. Due to this change, attack detection through common methods and old data processing techniques is now obsolete. Detection of attacks in IoT and detecting malicious traffic in the early stages is a very challenging problem due to the increase in the size of network traffic. In this paper, a framework is recommended for the detection of malicious network traffic. The framework uses three popular classification-based malicious network traffic detection methods, namely Support Vector Machine (SVM), Gradient Boosted Decision Trees (GBDT), and Random Forest (RF), with RF supervised machine learning algorithm achieving far better accuracy (85.34%). The dataset NSL KDD was used in the recommended framework and the performances in terms of training, predicting time, specificity, and accuracy were compared.

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Analysis of Support Vector Machine-based Intrusion Detection Techniques

Cited by 66 publications

References 20 publications

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks

HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Attack Detection in IoT using Machine Learning

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