MFVT: an anomaly traffic detection method merging feature fusion network and vision transformer architecture

Abstract

Network intrusion detection, which takes the extraction and analysis of network traffic features as the main method, plays a vital role in network security protection. The current network traffic feature extraction and analysis for network intrusion detection mostly uses deep learning algorithms. Currently, deep learning requires a lot of training resources and has weak processing capabilities for imbalanced datasets. In this paper, a deep learning model (MFVT) based on feature fusion network and vision transformer architecture is proposed, which improves the processing ability of imbalanced datasets and reduces the sample data resources needed for training. Besides, to improve the traditional raw traffic features extraction methods, a new raw traffic features extraction method (CRP) is proposed, and the CPR uses PCA algorithm to reduce all the processed digital traffic features to the specified dimension. On the IDS 2017 dataset and the IDS 2012 dataset, the ablation experiments show that the performance of the proposed MFVT model is significantly better than other network intrusion detection models, and the detection accuracy can reach the state-of-the-art level. And, when MFVT model is combined with CRP algorithm, the detection accuracy is further improved to 99.99%.

RESNETCNN: An abnormal network traffic flows detection model

“…In this paper, an intrusion detection model (RESNETCCN) is proposed that fuses traffic detection requirements. In our future work, we will introduce more new ideas such as blockchain cryptography [8], [18], [9], [19], [16], alliance chain [36], [7], [20], visual Q&A [5], [28], transformer [21], panoramic image [17], reinforcement learning [3], internet of things [23], [24], shared data [6] in our model.We will continue to explore network intrusion detection methods in more areas such as unsupervised and semi-supervised [2] areas for network anomalous traffic data detection. In addition, we also try to introduce new evaluation metrics and establish systematic evaluation methods of intrusion detection.…”

A Comprehensive Review on Artificial Intelligence/Machine Learning Algorithms for Empowering the Future IoT Toward 6G Era

“…Research topic Research analysis/findings Deep Learning [148] Network access and routing algorithm Survey on DL, supervised, reinforcement and imitation learning [149], [150] Indoor localization Localization error analysis [151] CSI estimation technique CSI overhead, channel measurement and sum rate analysis [152] DoA estimation Estimation accuracy analysis with the proposed, RVNN, SVR and MUSIC approaches [154] Power allocation strategy Analysis of secrecy rate, computation time and interference leakage [155] QoE forecasting mechanism Performance analysis of the proposed scheme against SVR, MLP, LSTM-based schemes [156] Anti-jamming scheme Throughput analysis Transformer algorithm [157] Medical image classification Classification accuracy analysis [158] Traffic sign recognition Classification accuracy analysis [159] Wildfire recognition and region detection Classification and detection accuracy analysis [160] Modulation recognition Classification and detection accuracy analysis [161] Intrusion detection Detection accuracy analysis Graph neural network [162] Topology control Network lifetime enhancement [163] IoT device tracking Tracking optimization in terms of execution time and distance covered by the tracking devices [164] Sentiment classification Interpretation accuracy of the aspect of text(s) [165] Vehicular traffic data prediction Prediction accuracy of the missing data from the available dataset recognition mechanism is designed in [158] with the help of DNN consisting of CNN and transformer-based algorithm.…”

“…In [159], a novel deep ensemble learning-based methodology is combined with two-transformer based algorithm and a DNN model for classification of wildfire regions and precise region detection. In case of cellular network system, modulation recognition and network intrusion detection is also being conducted with transformer-based algorithms to analyze their classification and detection accuracy [160], [161] .…”

A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots

“…People use the Internet to access a large amount of information that is relevant to their lives and work, and language is one of the most direct types of information, so it is particularly important to get the right and important information back to us from the many linguistic messages available. Artificial intelligence (AI), often thought of as computer systems with human-like thinking and capabilities [1] [2], is used in a wide range of applications such as voice chat, autonomous driving, social media, gaming, industry, and even replacing humans in tedious, repetitive tasks [3][4][5][6][7].…”