Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Thapa, K. Naresh Kumar; Duraipandian, N.

doi:10.1007/s11277-021-08359-6

Cited by 20 publications

(8 citation statements)

References 24 publications

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“…Some well-known classifier evaluation measures include accuracy (also known as recognition rate), sensitivity (or recall), specificity, precision, and F1 Score [7], [19], [20].…”

Section: Discussionmentioning

confidence: 99%

Comparative Study for News Categorization by Multinomial Naïve Bayes and Support Vector Machine

Farawn Ogaib,

Mahdi Hashim

2023

JEPS

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Online news is published in very large numbers, searches take a long time, and because of this hugenumber of news articles that include different genres (such as politics, sports, business, entertainment,technology, health, economics, real estate, art, etc) it will be difficult for users to access the importantnews that It suits their inclinations and desires. In this paper, the news group of BBC News is categorizedinto five categories, including (sports, business, politics, entertainment and technology). The objective ofis to classify news into its own category to help users quickly and easily access relevant news withoutwasting any time through classification methods that use machine learning algorithms. The classificationalgorithms Multinomial Naive Bayes and Support Vector Machine were applied to the news data set afterextracting the features from it using count vectorizer method and TF-IDF vectorizer. SVM algorithm hasproven superiority over MNB in count vectorizer with an accuracy 99.1% and TF-IDF vectorizer withan accuracy 98.2%.

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“…Some well-known classifier evaluation measures include accuracy (also known as recognition rate), sensitivity (or recall), specificity, precision, and F1 Score [7], [19], [20].…”

Section: Discussionmentioning

confidence: 99%

Comparative Study for News Categorization by Multinomial Naïve Bayes and Support Vector Machine

Farawn Ogaib,

Mahdi Hashim

2023

JEPS

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“…The architecture of speed network typically consists of the following components: The Network In Card (NIC), which is a hardware component that connects the computer to the n server. The classification of its speed as bits or bytes is discussed above in Table [64] Sensors 2023, 23, x FOR PEER REVIEW 4 of 25 are 40 Gigabit Ethernet, 100 Gigabit Ethernet, and InfiniBand [22]. A Cisco Report predicted that the internet protocol may cross 4.3 zettabytes in 2023, which is 1879 exabytes higher than 2018 [3].…”

Section: Architecture Of High-speed Networkmentioning

confidence: 99%

High-Speed Network DDoS Attack Detection: A Survey

Haseeb-ur-rehman,

Aman,

Hasan

et al. 2023

Sensors

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Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

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“…Here, "none" is the dynamic size number of instances, "48" is the number of features, and "1" is the third dimension value. The output shape of this layer is (none, 48,24) for combined DDoS and (none, 10, 20) for the car-hacking dataset, which is the input to the next layer. LSTM has a series of gates for the flow control of information, for example, how data come in, saves it, and leaves.…”

Section: Lstmmentioning

confidence: 99%

“…For IDS, some commonly used DL algorithms are convolutional neural network (CNN), recurrent neural network (RNN), LSTM, and GRU. The CCN is more complex than other DL algorithms, because it requires data-like images in matrix form; the data must be normalized and converted into the form of an image matrix [ 24 , 25 , 26 , 27 , 28 , 29 ]. The LSTM and GRU algorithms are effective at detecting malicious assaults over other ML and DL algorithms.…”

Section: Introductionmentioning

confidence: 99%

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

Ullah

Khan

Ahmad

et al. 2022

Sensors

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Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that connects smart vehicles to the internet, and vehicles with each other. With the emergence of IoV technology, customers have placed great attention on smart vehicles. However, the rapid growth of IoV has also caused many security and privacy challenges that can lead to fatal accidents. To reduce smart vehicle accidents and detect malicious attacks in vehicular networks, several researchers have presented machine learning (ML)-based models for intrusion detection in IoT networks. However, a proficient and real-time faster algorithm is needed to detect malicious attacks in IoV. This article proposes a hybrid deep learning (DL) model for cyber attack detection in IoV. The proposed model is based on long short-term memory (LSTM) and gated recurrent unit (GRU). The performance of the proposed model is analyzed by using two datasets—a combined DDoS dataset that contains CIC DoS, CI-CIDS 2017, and CSE-CIC-IDS 2018, and a car-hacking dataset. The experimental results demonstrate that the proposed algorithm achieves higher attack detection accuracy of 99.5% and 99.9% for DDoS and car hacks, respectively. The other performance scores, precision, recall, and F1-score, also verify the superior performance of the proposed framework.

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Malicious Traffic classification Using Long Short-Term Memory (LSTM) Model

Cited by 20 publications

References 24 publications

Comparative Study for News Categorization by Multinomial Naïve Bayes and Support Vector Machine

Comparative Study for News Categorization by Multinomial Naïve Bayes and Support Vector Machine

High-Speed Network DDoS Attack Detection: A Survey

HDL-IDS: A Hybrid Deep Learning Architecture for Intrusion Detection in the Internet of Vehicles

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