| Temporal sequence learning and data reduction for anomaly detection T Lane, CE Brodley ACM Transactions on Information and System Security (TISSEC) 2 (3), 295-331, 1999 | 803 | 1999 |
| Graph-based malware detection using dynamic analysis B Anderson, D Quist, J Neil, C Storlie, T Lane Journal in computer Virology 7, 247-258, 2011 | 353 | 2011 |
| An application of machine learning to anomaly detection T Lane, CE Brodley Proceedings of the 20th national information systems security conference 377 …, 1997 | 348 | 1997 |
| A computational study of off-target effects of RNA interference S Qiu, CM Adema, T Lane Nucleic acids research 33 (6), 1834-1847, 2005 | 345 | 2005 |
| Approximation algorithms for orienteering and discounted-reward TSP A Blum, S Chawla, DR Karger, T Lane, A Meyerson, M Minkoff SIAM Journal on Computing 37 (2), 653-670, 2007 | 336 | 2007 |
| TDCS guided using fMRI significantly accelerates learning to identify concealed objects VP Clark, BA Coffman, AR Mayer, MP Weisend, TDR Lane, VD Calhoun, ... Neuroimage 59 (1), 117-128, 2012 | 318 | 2012 |
| Sequence matching and learning in anomaly detection for computer security T Lane, CE Brodley AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, 43-49, 1997 | 301 | 1997 |
| Machine learning techniques for the computer security domain of anomaly detection TD Lane Purdue University, 2000 | 223 | 2000 |
| Approaches to online learning and concept drift for user identification in computer security. T Lane, CE Brodley KDD, 259-263, 1998 | 186 | 1998 |
| Improving malware classification: bridging the static/dynamic gap B Anderson, C Storlie, T Lane Proceedings of the 5th ACM workshop on Security and artificial intelligence …, 2012 | 183 | 2012 |
| Integrating multiple data sources for malware classification BH Anderson, CB Storlie, T Lane US Patent 9,021,589, 2015 | 148 | 2015 |
| Hidden markov models for human/computer interface modeling T Lane Proceedings of the IJCAI-99 Workshop on Learning about Users, 35-44, 1999 | 141 | 1999 |
| A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction S Qiu, T Lane IEEE/ACM Transactions on Computational Biology and Bioinformatics 6 (2), 190-199, 2008 | 137 | 2008 |
| Modeling transfer relationships between learning tasks for improved inductive transfer E Eaton, M Desjardins, T Lane Machine Learning and Knowledge Discovery in Databases: European Conference …, 2008 | 113 | 2008 |
| An empirical study of two approaches to sequence learning for anomaly detection T Lane, CE Brodley Machine learning 51, 73-107, 2003 | 113 | 2003 |
| Exploiting amino acid composition for predicting protein-protein interactions S Roy, D Martinez, H Platero, T Lane, M Werner-Washburne PloS one 4 (11), e7813, 2009 | 94 | 2009 |
| Discrete dynamic Bayesian network analysis of fMRI data J Burge, T Lane, H Link, S Qiu, VP Clark Human brain mapping 30 (1), 122-137, 2009 | 73 | 2009 |
| Knowledge discovery and data mining: Computers taught to discern patterns, detect anomalies and apply decision algorithms can help secure computer systems and find volcanoes on … CE Brodley, T Lane, TM Stough American Scientist 87 (1), 54-61, 1999 | 68 | 1999 |
| Reduced fMRI activity predicts relapse in patients recovering from stimulant dependence VP Clark, GK Beatty, RE Anderson, P Kodituwakku, JP Phillips, TDR Lane, ... Human brain mapping 35 (2), 414-428, 2014 | 64 | 2014 |
| Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia D Kim, J Burge, T Lane, GD Pearlson, KA Kiehl, VD Calhoun Neuroimage 42 (4), 1560-1568, 2008 | 63 | 2008 |
Carla BrodleyNortheastern UniversityVerified email at neu.edu
