Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

Wang, Yanan; Wang, Jian; Fan, Xiaoshi; Song, Yafei

doi:10.1109/access.2020.2983986

Cited by 7 publications

(3 citation statements)

References 14 publications

(20 reference statements)

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“…Error correction is carried out in the python statement of the candidate network application layer. According to the data classification standard, on the basis of capturing sensitive data in the whole DHCP protocol, the correlation between this statement and captured information is evaluated [11].  is assumed to be a random variable in DHCP protocol, and the process of judging this variable can be expressed as formula 123 (1).…”

Section: Python-based Network Traffic Anomaly Detection Methodsmentioning

confidence: 99%

Research on Network Traffic Anomaly Detection Method Based on Python

Cai¹

2022

AJST

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Abnormal traffic is the traffic that differs from the normal range of network services. Objective social and natural phenomena, network equipment failures on hardware, and man-made malicious attacks can all lead to abnormal network traffic. Python is a computer programming language that can realize cross-platform interaction, and it is also an object-oriented explanatory and interactive scripting language. Based on this, this paper studies the network traffic anomaly detection method based on Python. By sampling the data sets divided by each layer with different strategies, multiple balanced sub-data sets are obtained, and the feature selection fusion method proposed in the previous section is applied to each sub-data set to obtain the corresponding optimal feature subset, which is used to train multiple base classifiers to perform anomaly detection in this layer. The results show that Python-based network traffic anomaly detection method is superior to the traditional algorithm in accuracy and F1-Score.

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Section: Python-based Network Traffic Anomaly Detection Methodsmentioning

confidence: 99%

Research on Network Traffic Anomaly Detection Method Based on Python

Cai¹

2022

AJST

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“…Also, an interval type-2 fuzzy logic system (IT2FLS) based on a fuzzy time series was proposed by [38]. A fuzzy time series model has been developed by combining the model with some techniques such as network traffic anomaly detection algorithm [39], complex network analysis [40] and C-Means clustering algorithm [41].…”

Section: B Review and Framework Of The Fuzzy Time Seriesmentioning

confidence: 99%

A Novel Stochastic Fuzzy Time Series Forecasting Model Based on a New Partition Method

2021

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Fuzzy Time Series (FTS) models are commonly used in time series forecasting, where they do not require any statistical assumptions on time series data. FTS models can handle data sets with a small number of observations or with uncertainty. This is a general advantage of FTS as compared with other techniques. However, FTS models still have some criticisms, such as the optimal lengths of intervals and the proper weights, which always influence the model accuracy and still have been of many concerns in literature. The work in this paper proposes a novel FTS forecasting model based on a new tree partitioning method (TPM) and Markov chain (MC), called FTSMC-TPM, for determining the optimal partitions of intervals and the proper weights vectors respectively, and this will greatly improve the model accuracy. The efficiency of the FTSMC-TPM model is tested using two types of time series consisting of the air pollution index (API) data, which is collected from Kuala Lumpur, Malaysia and the benchmark data of the University of Alabama enrollment. Three statistical criteria have been used for investigating the accuracy of the proposed model. The results indicate that the FTSMC-TPM model outperforms the existing classic and advanced time series models in terms of forecasting accuracy. In addition, the FTSMC-TPM model shows the ability to successfully deal with forecasting problems to obtain higher model accuracy, which is examined in comparison with the existing models to validate its superiority. Hence, this study demonstrates that the proposed model is more suitable for the accurate prediction of air pollution events as well as for forecasting any type of random time series.

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“…A subgraph of 𝐺 isomorphic to 𝑝 is called an embedding. GPM enables various important applications such as functional modules discovery [53,63], biochemical structures mining [12,43,47] and anomaly detection [6,28,29,48,54,68] and many others [17,30,57,64,67,71,76]. The key challenge of GPM is the need to enumerate a large number of subgraphs, e.g., with WikiVote, a small graph with merely 7k vertices, the number of vertex-induced 5-chain embeddings can reach 71 billion.…”

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confidence: 99%

SparseCore: stream ISA and processor specialization for sparse computation

Rao

Chen

Yik

et al. 2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Computation on sparse data is becoming increasingly important for many applications. Recent sparse computation accelerators are designed for specific algorithm/application, making them inflexible with software optimizations. This paper proposes SparseCore, the first general-purpose processor extension for sparse computation that can flexibly accelerate complex code patterns and fast-evolving algorithms. We extend the instruction set architecture (ISA) to make stream or sparse vector first-class citizens, and develop efficient architectural components to support the stream ISA. The novel ISA extension intrinsically operates on streams, realizing both efficient data movement and computation. The simulation results show that SparseCore achieves significant speedups for sparse tensor computation and graph pattern computation. CCS CONCEPTS• Computer systems organization → Architectures.

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Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

Cited by 7 publications

References 14 publications

Research on Network Traffic Anomaly Detection Method Based on Python

Research on Network Traffic Anomaly Detection Method Based on Python

A Novel Stochastic Fuzzy Time Series Forecasting Model Based on a New Partition Method

SparseCore: stream ISA and processor specialization for sparse computation

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