@article {52,
	title = {Towards Ubiquitous Computing with Call Prediction},
	journal = {SIGMOBILE Mob. Comput. Commun. Rev.},
	volume = {15},
	year = {2011},
	pages = {52{\textendash}64},
	abstract = {<p>With the long-awaited era of the pervasive computing approaches, the handheld devices such as personal mobile phones begin to evolve into ubiquitous computing devices. At this early stage of the evolution, we propose a model of a call predictor based on the naive Bayesian classifier. As an incoming call predictor, our model makes use of the user{\^a}s call history to generate a list of numbers/contacts that are the most likely to be the callers within the next hour. On the other hand, when the user wants to make an outgoing call (e.g., user flips open the phone or unlocks the phone, etc.), the outgoing call predictor generates a list of number/contacts to be called. Our model has been evaluated with the real-life call logs and it shows a promising result in accuracy.</p>
},
	issn = {1559-1662},
	doi = {10.1145/1978622.1978628},
	url = {http://doi.acm.org/10.1145/1978622.1978628},
	author = {Phithakkitnukoon, Santi and Ram Dantu}
}
@conference {32,
	title = {Traffic Shaping of Spam Botnets},
	booktitle = {2008 5th IEEE Consumer Communications and Networking Conference},
	year = {2008},
	month = {01/2008},
	address = {Las Vegas, NV},
	abstract = {<p>Compromised computers, known as bots, are the major source of spamming. Detecting them can help greatly improve control of unwanted traffic. In this paper, we develop a traffic control mechanism to detect and delay the traffic of suspicious senders and bots. By delaying spammer{\textquoteright}s traffic, it has been reported that 90\% of spam emails can be eliminated. In our proposed mechanism, we group spammers based on their behavior and transmission patterns. These patterns of spammers show high correlation between group members irrespective of geographic location, network ID, content, and kind of receivers. After identification of these botnet groups we applied traffic shaping techniques a pre-filtering analysis to avoid use of automated machines(bots) to spam a particular domain. Thus the source for majority of spam is blocked before reaching email servers. We also identify how randomly the botnets behave and how easy it is to capture a botnet behavior, based on Information theory. To our knowledge, there is no work reported on detecting and mitigating botnets based on their behavior and in particular transmission patterns.</p>
},
	keywords = {automated machine, compromised computer, E-mail server, Internet, prefiltering analysis, spam botnets, spammer traffic, spamming, suspicious senders, telecommunication network management, telecommunication traffic, traffic control mechanism, traffic shaping, unsolicited e-mail},
	doi = {10.1109/ccnc08.2007.181},
	author = {Husna, Husain and Phithakkitnukoon, Santi and Ram Dantu}
}
