Applying big data based deep learning system to intrusion detection

Zhong, Wei; Yu, Ning; Ai, Chunyu

doi:10.26599/bdma.2020.9020003

Cited by 115 publications

(57 citation statements)

References 39 publications

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“…This model can also combine with a time-varying dynamic network to describe more complex propagation. We also observe that the significant proliferation of machine learning techniques has resulted in the rapid development of intelligent forecasting models [3] . Recent works demonstrate their comparable performance in capturing non-trivial atypical trends and typical patterns for epidemic control, such as the Wiener-series-based machine learning model for measuring the H1N1 virus spread after an intervention [4] , and the representation learning model that generates interpretable epidemic forecasting results for seasonal influenza forecasting [5] .…”

Section: Introductionmentioning

confidence: 86%

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Pang

Huang

Xie

et al. 2021

Tsinghua Sci. Technol.

134

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The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people's trajectories, to help plan efficient and inclusive decisions.However, a city DTsystem relies on long-term and high-quality data collection to make appropriate decisions, limiting its advantages when facing urgent crises, such as the COVID-19 pandemic. Federated Learning (FL), in which all clients can learn a shared model while retaining all training data locally, emerges as a promising solution for accumulating the insights from multiple data sources efficiently. Furthermore, the enhanced privacy protection settings removing the privacy barriers lie in this collaboration. In this work, we propose a framework that fused city DT with FL to achieve a novel collaborative paradigm that allows multiple city DTs to share the local strategy and status quickly. In particular, an FL central server manages the local updates of multiple collaborators (city DTs), providing a global model that is trained in multiple iterations at different city DT systems until the model gains the correlations between various response plans and infection trends. This approach means a collaborative city DT paradigm fused with FL techniques can obtain knowledge and patterns from multiple DTs and eventually establish a "global view" of city crisis management. Meanwhile, it also helps improve each city's DT by consolidating other DT's data without violating privacy rules. In this paper, we use the COVID-19 pandemic as the use case of the proposed framework. The experimental results on a real dataset with various response plans validate our proposed solution and demonstrate its superior performance.

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Section: Introductionmentioning

confidence: 86%

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Pang

Huang

Xie

et al. 2021

Tsinghua Sci. Technol.

134

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“…With the massive amount of data generated daily and the development of deep learning, the framework and system will reduce the cost of manpower [21,22] . To assist doctors in disease diagnosis, we developed an OMGaided diagnosis system based on the above methods.…”

Section: Omg-aided Diagnostic Systemmentioning

confidence: 99%

A computer-aided system for ocular myasthenia gravis diagnosis

Liu

Wei

Xie

et al. 2021

Tsinghua Sci. Technol.

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The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem.The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient's eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance over time. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the p values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.

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“…Where True-Positive (TP) is the number of samples correctly predicted in the intrusion class; True-Negative (TN) is the number of samples correctly predicted in the normal class; False-Negative (FN) is the number of samples incorrectly predicted in the normal class; False-Positive (FP) is the number of samples incorrectly predicted in the intrusions class. • False Positive Rate (FPR): FPR is the ratio of malicious traffic being misidentified as normal traffic to the total number of malicious traffic in NIDS, calculated as shown in (7).…”

Section: B Evaluation Criteriamentioning

confidence: 99%

“…According to the calculation of (6) and (7), Fig. 8 shows the testing accuracy and FPR of each method on the respective testing set.…”

Section: ) Time Cost Of Flmentioning

confidence: 99%

“…There have been many works proposed aiming at the design of NIDS typically invoking machine learning algorithms for identifying traffic [7]- [10]. The effectiveness of NIDS is evaluated based on their performance to identify attacks which requires a comprehensive data set that contains normal and abnormal behaviors, such as NSLKDD [11] and UNSW-NB15 [12].…”

Section: Introductionmentioning

confidence: 99%

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Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

Zhou

et al. 2020

IEEE Access

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The existing satellite-terrestrial integrated networks (STINs) suffer from security and privacy concerns due to the limited resources, poor attack resistance and high privacy requirements of satellite networks. Network Intrusion Detection System (NIDS) is intended to provide a high level of protection for modern network environments, but how to implement distributed NIDS on STINs has not been widely discussed. At the same time, satellite networks have always lacked real and effective security data sets as references. To solve these problems, we propose a distributed NIDS using Federal Learning (FL) in STIN to properly allocate resources in each domain to analyze and block malicious traffic, especially distributed denial-of-service (DDoS) attacks. Specifically, we first design a typical STIN topology, on the basis of which we collect and design security data sets adapted to satellite and terrestrial networks in STIN, respectively. To address the problem of poor attack resistance of satellite networks, we propose a satellite network topology optimization algorithm to reduce the difficulty in tracing malicious packets due to frequent link switching. In order to solve the problem of limited resources and high privacy requirements of satellite networks, we propose an algorithm for FL adaptation to STIN, and build a distributed NIDS using FL in STIN. Finally, we deploy the designed distributed NIDS in a prototype system and evaluate our proposed distributed NIDS with a large number of simulations of randomly generated malicious traffic. Related results demonstrate that the performance of our approach is better than traditional deep learning and intrusion detection methods in terms of malicious traffic recognition rate, packet loss rate, and CPU utilization. INDEX TERMS Satellite-terrestrial integrated network, distributed NIDS, security data set, federated learning.

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Applying big data based deep learning system to intrusion detection

Cited by 115 publications

References 39 publications

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

Collaborative city digital twin for the COVID-19 pandemic: A federated learning solution

A computer-aided system for ocular myasthenia gravis diagnosis

Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning

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