IEICE TRANSACTIONS on Information
A Bio-Inspired Approach to Alarm Malware Attacks in Mobile Handsets
Summary :
With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.
- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.4 pp.742-745
- Publication Date
- 2009/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E92.D.742
- Type of Manuscript
- LETTER
- Category
- Dependable Computing
Authors
Keyword
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Taejin AHN, Taejoon PARK, "A Bio-Inspired Approach to Alarm Malware Attacks in Mobile Handsets" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 4, pp. 742-745, April 2009, doi: 10.1587/transinf.E92.D.742.
Abstract: With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.742/_p
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@ARTICLE{e92-d_4_742,
author={Taejin AHN, Taejoon PARK, },
journal={IEICE TRANSACTIONS on Information},
title={A Bio-Inspired Approach to Alarm Malware Attacks in Mobile Handsets},
year={2009},
volume={E92-D},
number={4},
pages={742-745},
abstract={With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.},
keywords={},
doi={10.1587/transinf.E92.D.742},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - A Bio-Inspired Approach to Alarm Malware Attacks in Mobile Handsets
T2 - IEICE TRANSACTIONS on Information
SP - 742
EP - 745
AU - Taejin AHN
AU - Taejoon PARK
PY - 2009
DO - 10.1587/transinf.E92.D.742
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2009
AB - With proliferation of smart handsets capable of mobile Internet, the severity of malware attacks targeting such handsets is rapidly increasing, thereby requiring effective countermeasure for them. However, existing signature-based solutions are not suitable for resource-poor handsets due to the excessive run-time overhead of matching against ever-increasing malware pattern database as well as the limitation of detecting well-known malware only. To overcome these drawbacks, we present a bio-inspired approach to discriminate malware (non-self) from normal programs (self) by replicating the processes of biological immune system. Our proposed approach achieves superior performance in terms of detecting 83.7% of new malware or their variants and scalable storage requirement that grows very slowly with inclusion of new malware, making it attractive for use with mobile handsets.
ER -
English
