Network Penetration Identification Method Based on Interactive Behavior Analysis
| Title | Network Penetration Identification Method Based on Interactive Behavior Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Xuan, Shichang, Wang, Huanhong, Gao, Duo, Chung, Ilyong, Wang, Wei, Yang, Wu |
| Conference Name | 2019 Seventh International Conference on Advanced Cloud and Big Data (CBD) |
| Date Published | sep |
| ISBN Number | 978-1-7281-5141-0 |
| Keywords | attack identification, attack recognition method, attacker information collection, attacker keystroke record, Computer hacking, computer network security, Data models, feature extraction, gradient disappearance problem, gradient explosion, Hidden Markov models, honeynet, Human Behavior, human factors, interaction behavior, interactive behavior analysis, Internet, keystroke analysis, keystroke sequences, Logic gates, Long short-term memory, long-term memory shortage, LSTM, malicious attacks, Mathematical model, Metrics, national economy, Network penetration identification method, network penetration recognition technology, ordinary recurrent neural network, penetration attack, penetration attacks, pubcrawl, recurrent neural nets, recurrent neural network, Recurrent neural networks, Sebek, Sebek technology, short-short time memory network, time series, time series modeling |
| Abstract | The Internet has gradually penetrated into the national economy, politics, culture, military, education and other fields. Due to its openness, interconnectivity and other characteristics, the Internet is vulnerable to all kinds of malicious attacks. The research uses a honeynet to collect attacker information, and proposes a network penetration recognition technology based on interactive behavior analysis. Using Sebek technology to capture the attacker's keystroke record, time series modeling of the keystroke sequences of the interaction behavior is proposed, using a Recurrent Neural Network. The attack recognition method is constructed by using Long Short-Term Memory that solves the problem of gradient disappearance, gradient explosion and long-term memory shortage in ordinary Recurrent Neural Network. Finally, the experiment verifies that the short-short time memory network has a high accuracy rate for the recognition of penetration attacks. |
| URL | https://ieeexplore.ieee.org/document/8916365 |
| DOI | 10.1109/CBD.2019.00046 |
| Citation Key | xuan_network_2019 |
- penetration attacks
- long-term memory shortage
- LSTM
- malicious attacks
- Mathematical model
- Metrics
- national economy
- Network penetration identification method
- network penetration recognition technology
- ordinary recurrent neural network
- penetration attack
- Long short-term memory
- pubcrawl
- recurrent neural nets
- recurrent neural network
- Recurrent neural networks
- Sebek
- Sebek technology
- short-short time memory network
- time series
- time series modeling
- Hidden Markov models
- attack recognition method
- attacker information collection
- attacker keystroke record
- Computer hacking
- computer network security
- Data models
- feature extraction
- gradient disappearance problem
- gradient explosion
- attack identification
- honeynet
- Human behavior
- Human Factors
- interaction behavior
- interactive behavior analysis
- internet
- keystroke analysis
- keystroke sequences
- Logic gates