A novel system for medical equipment supply chain traceability based on alliance chain and attribute and role access control

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios.

https://doi.org/10.1080/09540091.2022.2067124

TCN enhanced novel malicious traffic detection for IoT devices

With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios. 

Abstract Fog computing, as a new distributed computing framework, extends the tasks originally done in the cloud data center to the edge of the network and brings more serious security challenges while providing convenience. Abnormal network traffic detection is an effective means to defense malicious behavior, can detect a variety of known attacks. Although the application of deep learning method in the field of network abnormal traffic detection is easier than traditional machine learning methods, there are still problems of poor recognition accuracy and false alarm rate. In this paper, we use the semi-supervised network anomaly detection model (NADLA) that combines the long-short-term memory neural network method and the self-encoder method to solve this problem. NADLA analyzes network traffic through the time characteristics and behavior characteristics of traffic, and optimizes the accuracy and false alarm rate of network traffic classification. In addition, we improved the preprocessing method to improve the sensitivity of the trained model to network abnormal traffic. The NADLA model is tested on NSL-KDD dataset, and the results show that the proposed model can improve the accuracy and F 1-value of network anomaly traffic detection. 

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Network abnormal traffic detection method based on fusion of chord similarity and multiple loss encoder

The widespread popularity of the Internet of human life has been accompanied by a significant increase in the cost of protecting private data from malicious attacks. Researchers have proposed many deep learning-based intrusion detection methods. However, traditional methods rely on a large number of unpolluted data to learn benign data distributions, while nonidentical distributions will affect the performance in distinguishing normal and abnormal data. To address this problem, this article proposes an intrusion detection method feedback semi-supervised learning with meta-gradient for intrusion detection (FSMG) based on feedback deep semi-supervised learning. FSMG constructs a lightweight evaluation network with slight data augmentation and nonprocessing on the same input, respectively, and uses a small amount of labeled data to infer nonidentically distributed data flows hidden in the training dataset. Then, FSMG converts malicious flows into useful information, continuously track and update the model, reducing data labeling errors. Furthermore, for the updating of gradients in the model, a bilevel nested optimization is used to ensure the model converges within <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O({\mathrm{C}}/{\sqrt{T}})$</tex-math></inline-formula> . Unlike other semisupervised algorithms, FSMG uses labeled data and nonidentically distributed unlabeled data proportionally to construct the training dataset, achieving higher classification accuracy, and better robustness even with an 80% polluted rate. 

A Feedback Semi-Supervised Learning With Meta-Gradient for Intrusion Detection

With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model.

https://doi.org/10.1080/09540091.2021.2024509

A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning

An reinforcement learning-based speech censorship chatbot system

The widespread popularity of the Internet of human life has been accompanied by a significant increase in the cost of protecting private data from malicious attacks. Researchers have proposed many deep learning-based intrusion detection methods. However, traditional methods rely on a large number of unpolluted data to learn benign data distributions, while nonidentical distributions will affect the performance in distinguishing normal and abnormal data. To address this problem, this article proposes an intrusion detection method feedback semi-supervised learning with meta-gradient for intrusion detection (FSMG) based on feedback deep semi-supervised learning. FSMG constructs a lightweight evaluation network with slight data augmentation and nonprocessing on the same input, respectively, and uses a small amount of labeled data to infer nonidentically distributed data flows hidden in the training dataset. Then, FSMG converts malicious flows into useful information, continuously track and update the model, reducing data labeling errors. Furthermore, for the updating of gradients in the model, a bilevel nested optimization is used to ensure the model converges within $O({\mathrm{C}}/{\sqrt{T}})$. Unlike other semisupervised algorithms, FSMG uses labeled data and nonidentically distributed unlabeled data proportionally to construct the training dataset, achieving higher classification accuracy, and better robustness even with an 80% polluted rate.

Does Short-and-Distort Scheme Really Exist? A Bitcoin Futures Audit Scheme through BIRCH & BPNN Approach

The rapid development of artiﬁcial intelligence (AI) technology has enabled large-scale AI applications to land in the market and prac-tice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur frequently. Therefore, this paper proposes a semantics censorship chatbot system based on reinforcement learning, which is mainly composed of two parts: the Oﬀensive semantics censorship model and the semantics puriﬁcation model. Oﬀensive semantics review can combine the context of user input sentences to detect the rapid evolution of Oﬀensive semantics and respond to Oﬀensive semantics responses. The semantics puriﬁcation model For the case of chatting robot models, it has been contaminated by large numbers of oﬀensive semantics, by strengthening the oﬀensive reply learned by the learning algorithm, rather than rolling back to the early versions. In addition, by integrating a once-through learning approach, the speed of semantics puriﬁcation is accelerated while reducing the impact on the quality of replies. The experimental results show that our proposed approach reduces the probability of the chat model generating oﬀensive replies and that the integration of the few-shot learning algorithm improves the training speed rapidly while eﬀectively slowing down the decline in BLEU values.

Shaokang Cai

Papers

A Hybrid parallel deep learning model for efficient intrusion detection based on metric learning

An reinforcement learning-based speech censorship chatbot system

A Feedback Semi-Supervised Learning With Meta-Gradient for Intrusion Detection

Does Short-and-Distort Scheme Really Exist? A Bitcoin Futures Audit Scheme through BIRCH & BPNN Approach

A Reinforcement Learning-based Offensive semantics Censorship System for Chatbots