TAILIEUCHUNG - Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

The paper present: method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), malware, and phishing. Our method is based on learning a (null hypothesis) probability model based on a large set of domain names that have been white listed by some reliable authority. Since these names are mostly assigned by humans, they are pronounceable, and tend to have a distribution of characters, words, word lengths, and number of words that are typical of some language (mostly English), and often consist of words drawn from a known lexicon. On the other hand, in the present day scenario, algorithmically generated domain names typically have distributions that are quite different from that of human-created domain names. We propose a fully generative model for the probability distribution of benign (white listed) domain names which can be used in an anomaly detection setting for identifying putative algorithmically generated domain names. Unlike other methods, our approach can make detections without considering any additional (latency producing) information sources, often used to detect fast flux activity. Experiments on a publicly available, large data set of domain names associated with fast flux service networks show encouraging results, relative to several baseline methods, with higher detection rates and low false positive rates. | Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modeling

• Vật lý
• Anomaly detection
• Algorithmically generated domain names
• Malicious domain names
• Domain name modeling
• Fast flux
• Journal of Computer Science and Cybernetics
• An evaluation method for unsupervised anomaly detection algorithms
• An evaluation method
• Unsupervised anomaly detection algorithms
• Intrusion detection in network security
• Double shrink autoencoder
• Network anomaly detection
• Deep learning
• Latent representation
• Anomaly detection system
• Web log
• Rule generation
• User behavior
• Anomaly detection system of web access
• Intrusion detection
• Negative selection algorithm
• Multi start methods
• Genetic algorithms
• A Distributed Monitoring
• Reconfiguration Approach for Adaptive Network Computing
• Network Computing
• Distributed Service Monitoring
• The anomaly detection efficiency of kernel density estimation functions
• Kernel density estimation functions on UAV images
• Kernel density estimation functions
• Nonparametric density estimation
• Search and rescue
• One class classification
• Principal component analysis
• Contamination scenarios
• International Journal of computer science & communication networks
• Anomaly detection using genetic
• SVM algorithm in data mining
• Algorithm in data mining
• K mean algorithm
• Two algorithm genetic and SVM
• Convolutional LSTM
• Convolutional autoencoder
• Prediction error
• Reconstruction error
• BMC Bioinformatics
• Robust principal component analysis
• Outlier detection
• RNA seq
• High dimensional data
• Lecture Data mining
• Bài giảng Khai thác dữ liệu
• Data mining
• Outlier discovery/anomaly detection
• Credit card fraud detection
• Telecommunication fraud detection
• natural sciences
• science and technology
• fuzzy inference
• scientific reports
• research projects
• scientific research
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• báo cáo khoa học
• Alternative Techniques
• training data
• Simple models
• Logistic regression
• Decision trees
• Europe’s ageing fleet
• Nuclear reactors operating
• CORTEX Horizon 2020 EU project
• Deep Learning framework
• 3D Convolutional Neural Network
• Digital pathology
• Convolutional neural networks
• Artificial intelligence
• Machine learning
• Information sciences
• Probabilistic generalization of isolation forest
• Spatio temporal datasets
• Probabilistic generalization of isolation