| Paper Abstract and Keywords |
| Presentation |
2004-07-20 11:35
Intrusion Detection System to Detect Variant Attacks Using Learning Algorithms with Automatic Generation of Training Data
Akira Yamada, Yutaka Miyake, Keisuke Takemori, Toshiaki Tanaka (KDDI Labs.) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
Although many anomaly detection systems based on learning algorithms
have been proposed to detect unknown attacks or variants of known
attacks, most systems require sophisticated training data for
supervised learning. Because it is not easy to prepare the training
data, the anomaly detection systems are not widely used in the
practical environment.
On the other hand, misuse detection systems that
use signatures to detect attacks are deployed widely. However, they are
not able to detect unknown attacks and variants of known attacks.
In this paper, we proposed a new anomaly detection system, which detects
the variants of known attacks without preparing the training data. In
this system, we use outputs of signature-based conventional IDS to
generate the training data for anomaly detection.
This system identifies novel features, and
generates generalized signatures from the output of IDS to detect the
variant attacks, which cannot be detected by conventional IDS.
We conducted experiments on the prototype system with three types of
traffic data, 1999 DARPA IDS Evaluation Data, attacks by vulnerability
scanner and actual traffic. The results show that our scheme can detect
the variants of attacks efficiently, which cannot be detected by
conventional IDS. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Network intrusion detection / Machine learning / Supervised learning / Anomaly detection / Misuse detection / Variant attacks / Automatic / |
| Reference Info. |
IEICE Tech. Rep., vol. 104, pp. 119-126, July 2004. |
| Paper # |
|
| Date of Issue |
2004-07-13 (ISEC) |
| ISSN |
Print edition: ISSN 0913-5685 |
| Download PDF |
|
| Conference Information |
| Committee |
ISEC IPSJ-CSEC |
| Conference Date |
2004-07-20 - 2004-07-21 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Tokushima Univ. |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
|
| Paper Information |
| Registration To |
IPSJ-CSEC |
| Conference Code |
2004-07-ISEC-JPSCSEC |
| Language |
English (Japanese title is available) |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Intrusion Detection System to Detect Variant Attacks Using Learning Algorithms with Automatic Generation of Training Data |
| Sub Title (in English) |
|
| Keyword(1) |
Network intrusion detection |
| Keyword(2) |
Machine learning |
| Keyword(3) |
Supervised learning |
| Keyword(4) |
Anomaly detection |
| Keyword(5) |
Misuse detection |
| Keyword(6) |
Variant attacks |
| Keyword(7) |
Automatic |
| Keyword(8) |
|
| 1st Author's Name |
Akira Yamada |
| 1st Author's Affiliation |
KDDI R&D Laboratories Inc. (KDDI Labs.) |
| 2nd Author's Name |
Yutaka Miyake |
| 2nd Author's Affiliation |
KDDI R&D Laboratories Inc. (KDDI Labs.) |
| 3rd Author's Name |
Keisuke Takemori |
| 3rd Author's Affiliation |
KDDI R&D Laboratories Inc. (KDDI Labs.) |
| 4th Author's Name |
Toshiaki Tanaka |
| 4th Author's Affiliation |
KDDI R&D Laboratories Inc. (KDDI Labs.) |
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| Speaker |
Author-1 |
| Date Time |
2004-07-20 11:35:00 |
| Presentation Time |
25 minutes |
| Registration for |
IPSJ-CSEC |
| Paper # |
ISEC2004-31 |
| Volume (vol) |
vol.104 |
| Number (no) |
no.199 |
| Page |
pp.119-126 |
| #Pages |
8 |
| Date of Issue |
2004-07-13 (ISEC) |
|