Modeling and analyzing attacker behavior in IoT botnet using temporal convolution network (TCN)

Sadique, Farhan; Sengupta, Shamik

doi:10.1016/j.cose.2022.102714

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…It proves that TCN can not only focus on local features but also extract temporal relationships within the context. Sadique et al [23] utilized TCN for sequential and predictive analysis of heterogeneous threat data, aiming to detect and thwart botnets effectively. Cai et al [24] proposed a model for detecting malicious network traffic, leveraging bidirectional TCN (BiTCN) and a multi-head self-attention mechanism.…”

Section: Research On Intrusion Detection Based On Tcnmentioning

confidence: 99%

DTT: A Dual-domain Transformer model for Network Intrusion Detection

Xu,

Sun,

2024

ICST Transactions on Scalable Information Systems

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With the rapid evolution of network technologies, network attacks have become increasingly intricate and threatening. The escalating frequency of network intrusions has exerted a profound influence on both industrial settings and everyday activities. This underscores the urgent necessity for robust methods to detect malicious network traffic. While intrusion detection techniques employing Temporal Convolutional Networks (TCN) and Transformer architectures have exhibited commendable classification efficacy, most are confined to the temporal domain. These methods frequently fall short of encompassing the entirety of the frequency spectrum inherent in network data, thereby resulting in information loss. To mitigate this constraint, we present DTT, a novel dual-domain intrusion detection model that amalgamates TCN and Transformer architectures. DTT adeptly captures both high-frequency and low-frequency information, thereby facilitating the simultaneous extraction of local and global features. Specifically, we introduce a dual-domain feature extraction (DFE) block within the model. This block effectively extracts global frequency information and local temporal features through distinct branches, ensuring a comprehensive representation of the data. Moreover, we introduce an input encoding mechanism to transform the input into a format suitable for model training. Experiments conducted on two distinct datasets address concerns regarding data duplication and diverse attack types, respectively. Comparative experiments with recent intrusion detection models unequivocally demonstrate the superior performance of the proposed DTT model.

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Section: Research On Intrusion Detection Based On Tcnmentioning

confidence: 99%

DTT: A Dual-domain Transformer model for Network Intrusion Detection

Xu,

Sun,

2024

ICST Transactions on Scalable Information Systems

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“…GRU converges faster than LSTM, thus reaching the best performance with a less complex model. This is because GRU typically has fewer trainable parameters [27]. All in all, TCN shows the best prediction accuracy regardless of the network depth.…”

Section: B Complexity Analysismentioning

confidence: 99%

Performance Analysis of ML-Based MTC Traffic Pattern Predictors

Ruiz-Guirola

López

Montejo‐Sánchez³

et al. 2023

IEEE Wireless Commun. Lett.

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Prolonging the lifetime of massive machine-type communication (MTC) networks is key to realizing a sustainable digitized society. Great energy savings can be achieved by accurately predicting MTC traffic followed by properly designed resource allocation mechanisms. However, selecting the proper MTC traffic predictor is not straightforward and depends on accuracy/complexity trade-offs and the specific MTC applications and network characteristics. Remarkably, the related state-ofthe-art literature still lacks such debates. Herein, we assess the performance of several machine learning (ML) methods to predict Poisson and quasi-periodic MTC traffic in terms of accuracy and computational cost. Results show that the temporal convolutional network (TCN) outperforms the long-short term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural network (RNN), in that order. For Poisson traffic, the accuracy gap between the predictors is larger than under quasi-periodic traffic. Finally, we show that running a TCN predictor is around three times more costly than other methods, while the training/inference time is the greatest/least.

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“…For sales forecasting, the input sequence of TCN is only sales time series S = (s0, st). The extended convolution operation H(t) of TCN can be defined as follows [40]:…”

Section: Deep Temporal Features Extractionmentioning

confidence: 99%

A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development

Jian

Chen

2022

Sustainability

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In recent years, with the rise of the Internet, e-commerce has become an important field of commodity sales. However, e-commerce is affected by many factors, and the wrong judgment of supply and marketing relationships will bring huge losses to operators. Therefore, it is of great significance to establish a model that can effectively achieve high precision sales prediction for ensuring the sustainable development of e-commerce enterprises. In this paper, we propose an e-commerce sales forecasting model that considers the features of many aspects of correlation. In the first layer of the model, the temporal convolutional network (TCN) is used to extract the deep temporal characteristics of univariate sales historical data, which ensures the integrity of temporal information of sales characteristics. In the second layer, the feature selection method based on reinforcement learning is used to filter the effective correlation feature set and combine it with the temporal feature after processing, which not only improves the amount of effective information input by the model, but also avoids the high feature dimension. The third layer of the reformer model learns all the features and pays different attention to the features with different degrees of importance, ensuring the stability of the sales forecast. In the experimental part, we compare the proposed model with the current advanced sales forecasting model, and we can find that the proposed model has higher stability and accuracy.

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Modeling and analyzing attacker behavior in IoT botnet using temporal convolution network (TCN)

Cited by 11 publications

References 8 publications

DTT: A Dual-domain Transformer model for Network Intrusion Detection

DTT: A Dual-domain Transformer model for Network Intrusion Detection

Performance Analysis of ML-Based MTC Traffic Pattern Predictors

A New Feature Based Deep Attention Sales Forecasting Model for Enterprise Sustainable Development

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