The invention belongs to the technical field of network security. The invention discloses a network intrusion abnormity detection method based on machine learning. The method comprises the following steps: (1) classifying data feature attributes, (2) mapping target attributes in a data set according to traditional network intrusion categories, (3) reducing dimensions of sample features in the data, (4) training a multi-classification model for network requests by adopting a processed data set, and (5) evaluating the trained classification model by using test set data. The invention discloses anetwork intrusion abnormity detection method based on machine learning. Data are preprocessed in cooperation with feature processing and normalization technologies, an SVM model is constructed to achieve efficient detection of network abnormal attacks, and the problems that a traditional rule-based detection method is high in maintenance cost, and filtering rules need to be updated in real time are solved.

Network intrusion abnormity detection method based on machine learning

The present invention provides a data processing method. The method comprises: acquiring target operation data, wherein the target operation data comprises real operation data generated by operation of an real object on an application program or fake operation data generated by operation of a pseudo object on the application program; obtaining reference data for identifying authenticity of an object; and recognizing the authenticity of the object that generates target operation data through operation on the application program by using the reference data to identify the target operation data.The present invention further provides a data processing system and a non-volatile storage medium.

Data processing method and system

The invention discloses a feature screening system and method for network traffic attack detection based on a learning automaton. The system comprises a data preprocessing module, a setting module, alearning automaton module, a random environment module, a feature screening module and an attack detection module. According to the invention, by interacting with the classifer and learning the evolution of the automaton is learned, redundant features are removed one by one, finally, the optimal features are screened out to form an optimal feature subset, the problems that the network traffic datasize is large, and the dimension is high are effectively solved, the network traffic attack detection efficiency can be effectively improved through the screened features, and the method can be applied to environments of large-scale networks such as a power grid industrial control network and the like.

Feature screening system and method for network traffic attack detection based on learning automaton

The invention discloses an information security risk assessment method and system based on situation awareness learning. The method comprises the following steps: firstly, collecting network securityasset defense information and department personnel organization information; then, preprocessing the collected information; performing format unification, useless data filtering, repeated data mergingand the like on the data; then, in combination with an existing information security risk assessment standard and an existing information security risk assessment model, systematically establishing entry information of the risk assessment model, and finally, grading network security according to the collected information by utilizing a Bayesian network machine learning algorithm. The system comprises an asset defense information collection module, a statistical personnel organization information module, a data set preprocessing module, a risk assessment model establishment module and a network security comprehensive assessment module. According to the invention, a set of more complete risk assessment system is constructed in multiple aspects and multiple ways, and security assessment grading is carried out, so that risk measures are selected in a targeted manner to reduce and control risks.

Information security risk assessment method and system based on situation awareness learning

The invention discloses an intrusion detection method oriented to complex stream data event analysis. The method comprises the implementation steps of carrying out sample collection in advance; carrying out intrusion detection result labeling on a collected sample to obtain a training sample data set; carrying out data pre-processing on the training sample data set and then completing training for a classifier, wherein the data pre-processing includes characteristic dimension reduction and sample reduction, and an execution sequence is to perform characteristic dimension reduction first and then perform sample reduction or to perform sample reduction first and then perform characteristic dimension reduction; and carrying out the data pre-processing on a to-be-detected current data set, then inputting the to-be-detected current data set into the trained classifier for classification detection, and outputting an intrusion detection result. According to the intrusion detection method, the correlation between characteristics can be effectively removed; the calculation can be greatly simplified; the problems of the redundancy of sample quantity and the sample feature correlation are comprehensively considered; the data redundancy is removed from the two aspects of characteristic dimension reduction and sample reduction, respectively; and the training time and the detection time are greatly shortened on the premise of guaranteeing that the detection accuracy rate before and after sample compression is substantially unchanged.

Intrusion detection method oriented to complex stream data event analysis

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Tan Yi

Author Tools

Chat about Author

Papers

Intrusion detection method oriented to complex stream data event analysis