GIDS: GAN based Intrusion Detection System for In-Vehicle Network
- Eunbi Seo, Hyun Min Song, H. Kim
- Computer ScienceConference on Privacy, Security and Trust
- 1 August 2018
A novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) is proposed using deep-learning model, Generative Adversarial Nets, which can learn to detect unknown attacks using only normal data.
OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame
- Hyunsung Lee, S. Jeong, H. Kim
- Computer ScienceConference on Privacy, Security and Trust
- 1 August 2017
This paper proposes an intrusion detection method based on the analysis of the offset ratio and time interval between request and response messages in CAN that allows quick intrusion detection with high accuracy.
In-vehicle network intrusion detection using deep convolutional neural network
- Hyun Min Song, Jiyoung Woo, H. Kim
- Computer ScienceVehicular Communications
- 2020
Know your master: Driver profiling-based anti-theft method
- Byung Il Kwak, Jiyoung Woo, H. Kim
- Computer ScienceConference on Privacy, Security and Trust
- 1 December 2016
This work proposes the driver verification method that analyzes driving patterns using measurements from the sensor in the vehicle to detect auto-theft efficiently and designs the model that uses significant features through feature selection to reduce the time cost of feature processing and improve the detection performance.
Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network
- Hyun Min Song, Ha Rang Kim, H. Kim
- Computer ScienceInternational Conference on Information…
- 13 January 2016
This paper proposes a light-weight intrusion detection algorithm for in-vehicle network based on the analysis of time intervals of CAN messages, and finds the time interval is a meaningful feature to detect attacks in the CAN traffic.
A Novel Approach to Detect Malware Based on API Call Sequence Analysis
- Youngjoon Ki, Eunjin Kim, H. Kim
- Computer ScienceInt. J. Distributed Sens. Networks
- 1 June 2015
A novel approach for dynamic analysis of malware is proposed that adopts DNA sequence alignment algorithms and extracts common API call sequence patterns of malicious function from malware in different categories and finds that certain malicious functions are commonly included in malware even inDifferent categories.
Anomaly intrusion detection method for vehicular networks based on survival analysis
- Mee Lan Han, Byung Il Kwak, H. Kim
- Computer ScienceVehicular Communications
- 1 October 2018
A behavior-based intrusion detection technique for smart grid infrastructure
- YooJin Kwon, H. Kim, Yong-hun Lim, Jong-in Lim
- Computer Science, EngineeringIEEE Eindhoven PowerTech
- 3 September 2015
This paper classifies various modern intrusion detection system (IDS) techniques for securing smart grid network and proposes a novel behavior-based IDS for IEC 61850 protocol using both statistical analysis of traditional network features and specification-based metrics.
Detecting and Classifying Android Malware Using Static Analysis along with Creator Information
- Hyunjae Kang, Jae-wook Jang, Aziz Mohaisen, H. Kim
- Computer ScienceInt. J. Distributed Sens. Networks
- 1 June 2015
A method to improve the performance of Android malware detection by incorporating the creator's information as a feature and classify malicious applications into similar groups and shows detection and classification performance with 98% and 90% accuracy, respectively.
A hybrid approach of neural network and memory-based learning to data mining
- Chung K. Shin, Ui Tak Yun, H. Kim, Sang-Chan Park
- Computer ScienceIEEE Trans. Neural Networks Learn. Syst.
- 1 May 2000
In this hybrid system of NN and MBR, the feature weight set, which is calculated from the trained neural network, plays the core role in connecting both learning strategies, and the explanation for prediction can be given by obtaining and presenting the most similar examples from the case base.
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