Vampire Attacks: Draining Life from Wireless Ad-hoc Sensor Networks
Journal: International Journal of Science and Research (IJSR) (Vol.4, No. 9)Publication Date: 2015-09-05
Authors : Trupti Pawar; Jyoti Patil;
Page : 264-268
Keywords : Denial of service; security; routing; ad-hoc networks; sensor networks; wireless networks;
Abstract
Ad-hoc low-power wireless networks are an exciting research direction in sensing and pervasive computing. Prior security work in this area has focused primarily on denial of communication at the routing or medium access control levels. This work explores resource depletion attacks, which permanently disable networks by quickly draining nodes battery power. These Vampire attacks are not specific to any specific protocol, but rather rely on the properties of many popular classes of routing protocols. Two types of vampire attacks are considered. In the carousel attack, attackers introduce some packet within a route as a sequence of loops and in the stretch attack, attackers construct falsely long routes. Whenever these two attacks are occurred the energy consumption is more as compared to the normal communication and data will reach very late to the destination. In the worst case, a single Vampire can increase network-wide energy usage by a factor of O (N), where N in the number of network nodes. Mitigation method used in this work is based on time, that is time taken by carousel and stretch attacks is compared with the time of normal communication and if the time in both the attacks is greater, than the new path is formed. Results shows, that secured transmission is done in the nodes by overcoming the vampire attacks, where the data travels in the honest route by mitigating the vampire attacks.
Other Latest Articles
- Epidemiological Trends in Brain Abscess: A Study at a Tertiary Care Centre
- Low Phase Noise Ring Oscillator Using Current Steering Technique
- Assess the Level of Health Risk Behaviors among Adolescent Boys in Government Higher Secondary School, Thandalam
- Accurate Sentiment Analysis using Enhanced Machine Learning Models
- Review Technique for Exploration of the Manufacturing Line for Improved Production
Last modified: 2021-06-30 21:53:24