IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Improving intrusion detection performance using keyword selection and neural networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Practical automated detection of stealthy portscans
Journal of Computer Security
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Ascending Frequency Ordered Prefix-tree: Efficient Mining of Frequent Patterns
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamically maintaining frequent items over a data stream
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
On deriving unknown vulnerabilities from zero-day polymorphic and metamorphic worm exploits
Proceedings of the 12th ACM conference on Computer and communications security
An Algorithm for In-Core Frequent Itemset Mining on Streaming Data
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evading network anomaly detection systems: formal reasoning and practical techniques
Proceedings of the 13th ACM conference on Computer and communications security
A study in using neural networks for anomaly and misuse detection
SSYM'99 Proceedings of the 8th conference on USENIX Security Symposium - Volume 8
Challenging the anomaly detection paradigm: a provocative discussion
NSPW '06 Proceedings of the 2006 workshop on New security paradigms
On the infeasibility of modeling polymorphic shellcode
Proceedings of the 14th ACM conference on Computer and communications security
Casting out Demons: Sanitizing Training Data for Anomaly Sensors
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Mining approximate frequent closed flows over packet streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Fast mining erasable itemsets using NC_sets
Expert Systems with Applications: An International Journal
Function and service pattern analysis for facilitating the reconfiguration of collaboration systems
Computers and Industrial Engineering
Anomaly extraction in backbone networks using association rules
IEEE/ACM Transactions on Networking (TON)
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Fast mining Top-Rank-k frequent patterns by using Node-lists
Expert Systems with Applications: An International Journal
| Hi-index | 12.06 |
Because of the varying and dynamic characteristics of network traffic, such as fast transfer, huge volume, shot-lived, inestimable and infinite, it is a serious challenge for network administrators to monitor network traffic in real time and judge whether the whole network works well. Currently, most of the existing techniques in this area are based on signature training, learning or matching, which may be too complicated to satisfy timely requirements. Other statistical methods including sampling, hashing or counting are all approximate methods and compute an incomplete set of results. Since the main objective of network monitoring is to discover and understand the active events that happen frequently and may influence or even ruin the total network. So in the paper we aim to use the technique of frequent pattern mining to find out these events. We first design a sliding window model to make sure the mining result novel and integrated; then, under the consideration of the distribution and fluidity of network flows, we develop a powerful class of algorithms that contains vertical re-mining algorithm, multi-pattern re-mining algorithm, fast multi-pattern capturing algorithm and fast multi-pattern capturing supplement algorithm to deal with a series of problems when applying frequent pattern mining algorithm in network traffic analysis. Finally, we develop a monitoring system to evaluate our algorithms on real traces collected from the campus network of Peking University. The results show that some given algorithms are effective enough and our system can definitely identify a lot of potentially very valuable information in time which greatly help network administrators to understand regular applications and detect network anomalies. So the research in this paper not only provides a new application area for frequent pattern mining, but also provides a new technique for network monitoring.