INCREMENTAL ANOMALY-BASED INTRUSION DETECTION SYSTEM USING LIMITED LABELED DATA

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Alaei Parisa; Noorbehbahani Fakhroddin

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2017 | Issue:3

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Title

INCREMENTAL ANOMALY-BASED INTRUSION DETECTION SYSTEM USING LIMITED LABELED DATA

Author(s)

Alaei Parisa | Noorbehbahani Fakhroddin

Keywords

INTRUSION DETECTION

ANOMALY DETECTION

INCREMENTAL CLASSIFICATION

DATA STREAM

ACTIVE LEARNING

Abstract

WITH THE PROLIFERATION OF THE INTERNET AND INCREASED GLOBAL ACCESS TO ONLINE MEDIA, CYBERCRIME IS ALSO OCCURRING AT AN INCREASING RATE. CURRENTLY, BOTH PERSONAL USERS AND COMPANIES ARE VULNERABLE TO CYBERCRIME. A NUMBER OF TOOLS INCLUDING FIREWALLS AND INTRUSION DETECTION SYSTEMS (IDS) CAN BE USED AS DEFENSE MECHANISMS. A FIREWALL ACTS AS A CHECKPOINT WHICH ALLOWS PACKETS TO PASS THROUGH ACCORDING TO PREDETERMINED CONDITIONS. IN EXTREME CASES, IT MAY EVEN DISCONNECT ALL NETWORK TRAFFIC. AN IDS, ON THE OTHER HAND, AUTOMATES THE MONITORING PROCESS IN COMPUTER NETWORKS. THE STREAMING NATURE OF DATA IN COMPUTER NETWORKS POSES A SIGNIFICANT CHALLENGE IN BUILDING IDS. IN THIS PAPER, A METHOD IS PROPOSED TO OVERCOME THIS PROBLEM BY PERFORMING ONLINE CLASSIFICATION ON DATASETS. IN DOING SO, AN INCREMENTAL NAIVE BAYESIAN CLASSIFIER IS EMPLOYED. FURTHERMORE, ACTIVE LEARNING ENABLES SOLVING THE PROBLEM USING A SMALL SET OF LABELED DATA POINTS WHICH ARE OFTEN VERY EXPENSIVE TO ACQUIRE. THE PROPOSED METHOD INCLUDES TWO GROUPS OF ACTIONS I.E. OFFLINE AND ONLINE. THE FORMER INVOLVES DATA PREPROCESSING WHILE THE LATTER INTRODUCES THE NADAL ONLINE METHOD. THE PROPOSED METHOD IS COMPARED TO THE INCREMENTAL NAIVE BAYESIAN CLASSIFIER USING THE NSL-KDD STANDARD DATASET. THERE ARE THREE ADVANTAGES WITH THE PROPOSED METHOD: (1) OVERCOMING THE STREAMING DATA CHALLENGE; (2) REDUCING THE HIGH COST ASSOCIATED WITH INSTANCE LABELING; AND (3) IMPROVED ACCURACY AND KAPPA COMPARED TO THE INCREMENTAL NAIVE BAYESIAN APPROACH. THUS, THE METHOD IS WELL-SUITED TO IDS APPLICATIONS.

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APA: Copy

Alaei, Parisa, & Noorbehbahani, Fakhroddin. (2017). INCREMENTAL ANOMALY-BASED INTRUSION DETECTION SYSTEM USING LIMITED LABELED DATA. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/946950/en

Vancouver: Copy

Alaei Parisa, Noorbehbahani Fakhroddin. INCREMENTAL ANOMALY-BASED INTRUSION DETECTION SYSTEM USING LIMITED LABELED DATA. 2017. Available from: https://sid.ir/paper/946950/en

IEEE: Copy

Parisa Alaei, and Fakhroddin Noorbehbahani, “INCREMENTAL ANOMALY-BASED INTRUSION DETECTION SYSTEM USING LIMITED LABELED DATA,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2017, [Online]. Available: https://sid.ir/paper/946950/en

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