MFFusion: A Multi-level Features Fusion Model for Malicious Traffic Detection based on Deep Learning

Lin, Kunda; Xu, Xiaolong; Xiao, Fu

doi:10.1016/j.comnet.2021.108658

Cited by 39 publications

(4 citation statements)

References 25 publications

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“…In the development of ML-based systems, the selection of features is a crucial step. It can aid in reducing the dimensionality of the data and enhancing the precision of the analysis [26], [27]. There are various methods that can be used for this process, such as embedded, wrapper, and filter.…”

Section: ░ 3 Feature Selectionmentioning

confidence: 99%

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Lavate¹,

Srivastava²

2023

IJEER

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The complexity and volume of network traffic has increased significantly due to the emergence of the “Internet of Things” (IoT). The classification accuracy of the network traffic is dependent on the most pertinent features. In this paper, we present a hybrid feature selection method that takes into account the optimization of Particle Swarms (PSO) and Random Forests. The data collected by the security firm, CIC-IDS2017, contains a large number of attacks and traffic instances. To improve the classification accuracy, we use the framework's RF algorithm to identify the most important features. Then, the PSO algorithm is used to refine the selection process. According to our experiments, the proposed method performed better than the other methods when it comes to the classification accuracy. It achieves a ~99.9% accuracy when using a hybrid of Random Forest and PSO. The hybrid approach also helps improve the model's performance. The suggested method can be utilized by security analysts and network administrators to identify and prevent attacks on the IoT.

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Section: ░ 3 Feature Selectionmentioning

confidence: 99%

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Lavate¹,

Srivastava²

2023

IJEER

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“…Android uygulamalarından özellik çıkarmak, kötü amaçlı yazılım tespitinin performansını artırmak için önemlidir ve bu amaçla iki ortak analiz yöntemi kullanılmaktadır: Statik Analiz [6,7,8] ve Dinamik Analiz [9,10]. Statik Analiz, uygulamanın çalıştırılmadan önce Android uygulama ve kaynak kodu dosyalarından çıkarılan özelliklere dayanarak uygulamaları sınıflandırır.…”

Section: Introductionunclassified

Kötü Amaçlı Yazılım Tespiti için Makine Öğrenmesi Algoritmalarının Kullanımı

TÜFEKCİ,

ÖNAL

2024

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Gelişen teknoloji sayesinde bilgiye kolay erişim sağlansa da, bu durum kötü amaçlı eylemlerin artışına da sebep olmuştur. Android işletim sistemlerinde sıklıkla rastlanan kötü amaçlı yazılımlar (malware), kullanıcıların cihazındaki verilere erişerek büyük bir tehdit oluşturmaktadır. Bu çalışma, kötü amaçlı yazılımları tespit etmek amacıyla yüksek doğruluklu ve güvenilir bir model geliştirmeyi hedeflemektedir. Modelleme çalışmalarında popüler bir veri seti olan DREBIN-215 Android Malware Dataset kullanılmıştır. Makine Öğrenmesi algoritmaları arasında Support Vector Machines (SVM), Gradient Boosting (GB) ve Multi Layer Perceptron (MLP) uygulanmıştır. Algoritmaların performansları, varsayılan parametreler ve GridSearch yöntemiyle elde edilen en iyi hiperparametre değerlerinin kullanılmasıyla değerlendirilmiştir. En başarılı model, SVM algoritmasıyla en iyi hiperparametrelerin uygulanması sonucu %99.07 doğruluk oranıyla elde edilmiştir.

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“…In particular, deep networks have achieved satisfactory results in semi-supervised classification of time series data [36]. However, semi-supervised classification models for time series also have two obvious shortcomings: first, it is well known that deep neural networks require a large amount of training data, but with the current small amount of time series data and the lack of labeled data, deep networks are prone to overfitting and poor robustness [21]. Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial interpolation methods, Empirical Mode Decomposition are common time series data enhancement techniques [1,2].…”

Section: Introductionmentioning

confidence: 99%

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

Liu,

Yao,

Wang

et al. 2024

ITC

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Realistic scenarios produce labeled data and unlabeled data, however, there are significant challenges in labeling time series data. It is imperative to effectively integrate the relationship between labeled and unlabeled data within semi-supervised classification model. This paper presents a novel semi-supervised classification method, namely Data Augmentation-Fast Shapelet Semi-Supervised Classification, which employs a data augmentation module to enhance the diversity of data and improve the generalization ability of the model, as well as a feature fusion module to enhance the semi-supervised network. A conditional generative adversarial network is used to synthesize excellent labeled time series samples to enhance the homogeneous data in the sample space, the fast shapelets method is used to quickly extract the important shape feature vectors in the time series, self-supervised and supervised learning are combined to fully learn the unlabeled and labeled data of the time series dataset. Thejoint loss function combines the loss functions of the two networks to optimize multiple objectives. Reinforcement learning is used to determine the weight coefficients of the joint loss function, at the same time, the reward function is modified to bias the supervisory loss, which improves the classification performance of the model under limited labeled data, and the model can also better achieve the semi-supervised classification task. The proposed method is validated on the UCR benchmark dataset, Electrocardiogram dataset, and Electroencephalogram dataset, the results show that the semi-supervised classification method can perform a more accurate semi-supervised classification of the time series, with an accuracy better than the comparison methods. Meanwhile, we use the plant electrical signal dataset obtained from actual measurements for testing, the visualizationanalysis can clearly show the model role in the semi-supervised classification task, and the experimental results fully demonstrate the effectiveness and applicability of the proposed method.

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MFFusion: A Multi-level Features Fusion Model for Malicious Traffic Detection based on Deep Learning

Cited by 39 publications

References 25 publications

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis

Kötü Amaçlı Yazılım Tespiti için Makine Öğrenmesi Algoritmalarının Kullanımı

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

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