34,161 research outputs found
Security in Pervasive Computing: Current Status and Open Issues
Million of wireless device users are ever on the move, becoming more dependent on their PDAs, smart phones, and other handheld devices. With the advancement of pervasive computing, new and unique capabilities are available to aid mobile societies. The wireless nature of these devices has fostered a new era of mobility. Thousands of pervasive devices are able to arbitrarily join and leave a network, creating a nomadic environment known as a pervasive ad hoc network. However, mobile devices have vulnerabilities, and some are proving to be challenging. Security in pervasive computing is the most critical challenge. Security is needed to ensure exact and accurate confidentiality, integrity, authentication, and access control, to name a few. Security for mobile devices, though still in its infancy, has drawn the attention of various researchers. As pervasive devices become incorporated in our day-to-day lives, security will increasingly becoming a common concern for all users - - though for most it will be an afterthought, like many other computing functions. The usability and expansion of pervasive computing applications depends greatly on the security and reliability provided by the applications. At this critical juncture, security research is growing. This paper examines the recent trends and forward thinking investigation in several fields of security, along with a brief history of previous accomplishments in the corresponding areas. Some open issues have been discussed for further investigation
Audio-Visual Speaker Identification using the CUAVE Database
The freely available nature of the CUAVE database allows it to provide a valuable platform to form benchmarks and compare research. This paper shows that the CUAVE database can successfully be used to test speaker identifications systems, with performance comparable to existing systems implemented on other databases. Additionally, this research shows that the optimal configuration for decisionfusion of an audio-visual speaker identification system relies heavily on the video modality in all but clean speech conditions
DoubleEcho: Mitigating Context-Manipulation Attacks in Copresence Verification
Copresence verification based on context can improve usability and strengthen
security of many authentication and access control systems. By sensing and
comparing their surroundings, two or more devices can tell whether they are
copresent and use this information to make access control decisions. To the
best of our knowledge, all context-based copresence verification mechanisms to
date are susceptible to context-manipulation attacks. In such attacks, a
distributed adversary replicates the same context at the (different) locations
of the victim devices, and induces them to believe that they are copresent. In
this paper we propose DoubleEcho, a context-based copresence verification
technique that leverages acoustic Room Impulse Response (RIR) to mitigate
context-manipulation attacks. In DoubleEcho, one device emits a wide-band
audible chirp and all participating devices record reflections of the chirp
from the surrounding environment. Since RIR is, by its very nature, dependent
on the physical surroundings, it constitutes a unique location signature that
is hard for an adversary to replicate. We evaluate DoubleEcho by collecting RIR
data with various mobile devices and in a range of different locations. We show
that DoubleEcho mitigates context-manipulation attacks whereas all other
approaches to date are entirely vulnerable to such attacks. DoubleEcho detects
copresence (or lack thereof) in roughly 2 seconds and works on commodity
devices
PATH: Person Authentication using Trace Histories
In this paper, a solution to the problem of Active Authentication using trace
histories is addressed. Specifically, the task is to perform user verification
on mobile devices using historical location traces of the user as a function of
time. Considering the movement of a human as a Markovian motion, a modified
Hidden Markov Model (HMM)-based solution is proposed. The proposed method,
namely the Marginally Smoothed HMM (MSHMM), utilizes the marginal probabilities
of location and timing information of the observations to smooth-out the
emission probabilities while training. Hence, it can efficiently handle
unforeseen observations during the test phase. The verification performance of
this method is compared to a sequence matching (SM) method , a Markov
Chain-based method (MC) and an HMM with basic Laplace Smoothing (HMM-lap).
Experimental results using the location information of the UMD Active
Authentication Dataset-02 (UMDAA02) and the GeoLife dataset are presented. The
proposed MSHMM method outperforms the compared methods in terms of equal error
rate (EER). Additionally, the effects of different parameters on the proposed
method are discussed.Comment: 8 pages, 9 figures. Best Paper award at IEEE UEMCON 201
Benefits of Location-Based Access Control:A Literature Study
Location-based access control (LBAC) has been suggested as a means to improve IT security. By 'grounding' users and systems to a particular location, \ud
attackers supposedly have more difficulty in compromising a system. However, the motivation behind LBAC and its potential benefits have not been investigated thoroughly. To this end, we perform a structured literature review, and examine the goals that LBAC can potentially fulfill, \ud
the specific LBAC systems that realize these goals and the context on which LBAC depends. Our paper has four main contributions:\ud
first we propose a theoretical framework for LBAC evaluation, based on goals, systems and context. Second, we formulate and apply criteria for evaluating the usefulness of an LBAC system. Third, we identify four usage scenarios for LBAC: open areas and systems, hospitals, enterprises, and finally data centers and military facilities. Fourth, we propose directions for future research:\ud
(i) assessing the tradeoffs between location-based, physical and logical access control, (ii) improving the transparency of LBAC decision making, and \ud
(iii) formulating design criteria for facilities and working environments for optimal LBAC usage
On the use of SIFT features for face authentication
Several pattern recognition and classification techniques
have been applied to the biometrics domain. Among them,
an interesting technique is the Scale Invariant Feature
Transform (SIFT), originally devised for object recognition.
Even if SIFT features have emerged as a very powerful image
descriptors, their employment in face analysis context
has never been systematically investigated.
This paper investigates the application of the SIFT approach
in the context of face authentication. In order to determine
the real potential and applicability of the method,
different matching schemes are proposed and tested using
the BANCA database and protocol, showing promising results
- …