Robust dynamic network traffic partitioning against malicious attacks

Xu, Bing; Yang, Kun; Zhao, Jiachang; Li, Keqin

doi:10.1016/j.jnca.2016.04.013

Cited by 54 publications

(36 citation statements)

References 27 publications

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“…This model generates the score for every input feature taking into account both current input timesteps and previous timesteps. The score formula is given in Equation (11).…”

Section: F I G U R E 7 Block Chart Of the Bilstmmentioning

confidence: 99%

“…In traditional machine learning models like autoregressive (AR) models or exponential smoothing-feature engineering is rendered dynamically and a part of parameters are fine-tuned. [11][12][13][14] Moreover taking into account the demesne facts, deep learning models learn highlights and evolution straightforwardly from the data. [15][16][17] They accelerate the mode of data preparation and can study more intricate data patterns in a replete manner.…”

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confidence: 99%

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Traffic prediction using MSSBiLS with self‐attention model

Suvitha

Vijayalakshmi

2022

Concurrency and Computation

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Due to gathering and examining a tremendous amount of traffic stream data, traffic blockage is the most important issue in the intelligent transportation framework. The main aim of this research work is "to find the appropriate deep learning time series models, to predict the future traffic of Denmark Aarhus city." In this article, multivariate sequential stacked bidirectional LSTM with self-attention model (MSSBiLS-SA) is proposed to prevent the long term dependencies and also to reduce the error rate.Based on the traffic state, the high, medium, and low range of traffic is predicted to give the best route to the users, and visualization is performed with the help of the Android application. The proposed MSSBiLS-SA attains higher accuracy 94.56% and 95.36%, sensitivity 88.74% and 92.5%, specificity 93.5% and 94.5%, precision 94.23% and 95.5%, F-score 94.5% and 94.5%, and lower time complexity 10.19% and 12.43% shows better performance when compared with the existing methods, such as prediction and classification of traffic location analysis using optimized graph convolutional recurrent neural network and prediction and classification of traffic location analysis based multilayer perceptron network using long short term memory. Finally, the proposed method predicts and classifies the traffic location very accurately.

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“…This model generates the score for every input feature taking into account both current input timesteps and previous timesteps. The score formula is given in Equation (11).…”

Section: F I G U R E 7 Block Chart Of the Bilstmmentioning

confidence: 99%

mentioning

confidence: 99%

Traffic prediction using MSSBiLS with self‐attention model

Suvitha

Vijayalakshmi

2022

Concurrency and Computation

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“…Authors in [5], [19], [20] explored whether graph mining techniques can help to uncover such macroscopic coordinated events in darknet traffic. The darknet traffic is getting complex daily, and malicious activities such as physical threats, sales data theft, fraudulent activity, phishing attacks, and scams, DDoS attacks, illicit links, Illicit Drugs, and terrorism vary day by day [21], [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

et al. 2021

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Darknet is commonly known as the epicenter of illegal online activities. An analysis of darknet traffic is essential to monitor real-time applications and activities running over the Darknet. Recognizing network traffic bound to unused Internet addresses has become undeniably significant for identifying and examining malicious activities on the internet. Since there are no authentic hosts or devices in an unused address block, any observed network traffic must be the aftereffect of misconfiguration from spoofed source addressed and other frameworks that monitor unused address space. However, the recent advancements in artificial intelligence allow digital systems to detect and identify darknet traffic autonomously. In this paper, we propose a generalized approach for darknet traffic detection and categorization using Deep Learning. We examine the state-of-the-art complex dataset, which provides excessive information about the darknet traffic and perform data preprocessing. Next, we analyze diverse feature selection techniques to select optimal features for darknet traffic detection and categorization. We apply fine-tuned machine learning (ML) algorithms which include Decision Tree (DT), Gradient Boosting (GB), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGB) on selected features and compare the performance. Next, we apply modified Convolution-Long Short-Term Memory (CNN-LSTM) and Convolution-Gradient Recurrent Unit (CNN-GRU) deep learning techniques to recognize the network traffic more accurately. The results demonstrate that the proposed approach outperforms the existing approaches by yielding the maximum accuracy of 96% of darknet traffic detection and 89% of darknet traffic categorization through XGB as a feature selection approach and CNN-LSTM a recognition model.

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“…[36][37][38][39][40][41][42][43][44][45] EPCM particles consist of a solid shell that does not change phase and a PCM core that can be melted and solidified. [46][47][48][49][50][51][52][53][54][55][56] The shell has a great effect on the morphological, mechanical, and thermal properties of EPCM. Moreover, PCMs can be classified as organic, inorganic, and organic-inorganic hybrid materials.…”

Section: Introductionmentioning

confidence: 99%