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		<title><![CDATA[ Dependable and Secure Computing, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 8858 </description>
		<year>2013</year>
		<month>May      </month>
		<day>23</day>
		<item>
			<title><![CDATA[A System for Timely and Controlled Information Sharing in Emergency Situations]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461890]]></link>
			<description><![CDATA[During natural disasters or emergency situations, an essential requirement for an effective emergency management is the information sharing. In this paper, we present an access control model to enforce controlled information sharing in emergency situations. An in-depth analysis of the model is discussed throughout the paper, and administration policies are introduced to enhance the model flexibility during emergencies. Moreover, a prototype implementation and experiments results are provided showing the efficiency and scalability of the system.]]></description>
			<pubDate><![CDATA[May-June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461890]]></guid>
			<volume>10</volume>
			<issue>3</issue>
			<startPage>129</startPage>
			<endPage>142</endPage>
			<fileSize>700</fileSize>
			<authors><![CDATA[Carminati, Barbara;Ferrari, Elena;Guglielmi, Michele;]]></authors>
		</item>
		<item>
			<title><![CDATA[DNS for Massive-Scale Command and Control]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461889]]></link>
			<description><![CDATA[Attackers, in particular botnet controllers, use stealthy messaging systems to set up large-scale command and control. To systematically understand the potential capability of attackers, we investigate the feasibility of using domain name service (DNS) as a stealthy botnet command-and-control channel. We describe and quantitatively analyze several techniques that can be used to effectively hide malicious DNS activities at the network level. Our experimental evaluation makes use of a two-month-long 4.6-GB campus network data set and 1 million domain names obtained from &#x003E;alexa.com. We conclude that the DNS-based stealthy command-and-control channel (in particular, the codeword mode) can be very powerful for attackers, showing the need for further research by defenders in this direction. The statistical analysis of DNS payload as a countermeasure has practical limitations inhibiting its large-scale deployment.]]></description>
			<pubDate><![CDATA[May-June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6461889]]></guid>
			<volume>10</volume>
			<issue>3</issue>
			<startPage>143</startPage>
			<endPage>153</endPage>
			<fileSize>1047</fileSize>
			<authors><![CDATA[Xu, Kui;Butler, Patrick;Saha, Sudip;Yao, Danfeng;]]></authors>
		</item>
		<item>
			<title><![CDATA[On the Privacy Risks of Virtual Keyboards: Automatic Reconstruction of Typed Input from Compromising Reflections]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6509878]]></link>
			<description><![CDATA[We investigate the implications of the ubiquity of personal mobile devices and reveal new techniques for compromising the privacy of users typing on virtual keyboards. Specifically, we show that so-called compromising reflections (in, for example, a victim's sunglasses) of a device's screen are sufficient to enable automated reconstruction, from video, of text typed on a virtual keyboard. Through the use of advanced computer vision and machine learning techniques, we are able to operate under extremely realistic threat models, in real-world operating conditions, which are far beyond the range of more traditional OCR-based attacks. In particular, our system does not require expensive and bulky telescopic lenses: rather, we make use of off-the-shelf, handheld video cameras. In addition, we make no limiting assumptions about the motion of the phone or of the camera, nor the typing style of the user, and are able to reconstruct accurate transcripts of recorded input, even when using footage captured in challenging environments (e.g., on a moving bus). To further underscore the extent of this threat, our system is able to achieve accurate results even at very large distances&amp;#x2014;up to 61 m for direct surveillance, and 12 m for sunglass reflections. We believe these results highlight the importance of adjusting privacy expectations in response to emerging technologies.]]></description>
			<pubDate><![CDATA[May-June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6509878]]></guid>
			<volume>10</volume>
			<issue>3</issue>
			<startPage>154</startPage>
			<endPage>167</endPage>
			<fileSize>1555</fileSize>
			<authors><![CDATA[Raguram, Rahul;White, Andrew M.;Xu, Yi;Frahm, Jan-Michael;Georgel, Pierre;Monrose, Fabian;]]></authors>
		</item>
		<item>
			<title><![CDATA[To Lie or to Comply: Defending against Flood Attacks in Disruption Tolerant Networks]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336753]]></link>
			<description><![CDATA[Disruption Tolerant Networks (DTNs) utilize the mobility of nodes and the opportunistic contacts among nodes for data communications. Due to the limitation in network resources such as contact opportunity and buffer space, DTNs are vulnerable to flood attacks in which attackers send as many packets or packet replicas as possible to the network, in order to deplete or overuse the limited network resources. In this paper, we employ rate limiting to defend against flood attacks in DTNs, such that each node has a limit over the number of packets that it can generate in each time interval and a limit over the number of replicas that it can generate for each packet. We propose a distributed scheme to detect if a node has violated its rate limits. To address the challenge that it is difficult to count all the packets or replicas sent by a node due to lack of communication infrastructure, our detection adopts claim-carry-and-check: each node itself counts the number of packets or replicas that it has sent and claims the count to other nodes; the receiving nodes carry the claims when they move, and cross-check if their carried claims are inconsistent when they contact. The claim structure uses the pigeonhole principle to guarantee that an attacker will make inconsistent claims which may lead to detection. We provide rigorous analysis on the probability of detection, and evaluate the effectiveness and efficiency of our scheme with extensive trace-driven simulations.]]></description>
			<pubDate><![CDATA[May-June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336753]]></guid>
			<volume>10</volume>
			<issue>3</issue>
			<startPage>168</startPage>
			<endPage>182</endPage>
			<fileSize>1177</fileSize>
			<authors><![CDATA[Li, Qinghua;Gao, Wei;Zhu, Sencun;Cao, Guohong;]]></authors>
		</item>
		<item>
			<title><![CDATA[WarningBird: A Near Real-Time Detection System for Suspicious URLs in Twitter Stream]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6409356]]></link>
			<description><![CDATA[Twitter is prone to malicious tweets containing URLs for spam, phishing, and malware distribution. Conventional Twitter spam detection schemes utilize account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. These detection schemes are ineffective against feature fabrications or consume much time and resources. Conventional suspicious URL detection schemes utilize several features including lexical features of URLs, URL redirection, HTML content, and dynamic behavior. However, evading techniques such as time-based evasion and crawler evasion exist. In this paper, we propose WarningBird, a suspicious URL detection system for Twitter. Our system investigates correlations of URL redirect chains extracted from several tweets. Because attackers have limited resources and usually reuse them, their URL redirect chains frequently share the same URLs. We develop methods to discover correlated URL redirect chains using the frequently shared URLs and to determine their suspiciousness. We collect numerous tweets from the Twitter public timeline and build a statistical classifier using them. Evaluation results show that our classifier accurately and efficiently detects suspicious URLs. We also present WarningBird as a near real-time system for classifying suspicious URLs in the Twitter stream.]]></description>
			<pubDate><![CDATA[May-June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6409356]]></guid>
			<volume>10</volume>
			<issue>3</issue>
			<startPage>183</startPage>
			<endPage>195</endPage>
			<fileSize>2064</fileSize>
			<authors><![CDATA[Lee, Sangho;Kim, Jong;]]></authors>
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