Since the 1970s, a large number of encryption
schemes have been proposed, among which some have been standardised and
widely adopted all over the world, such as Data Encryption Standard (DES)
and Advanced Encryption Standard (AES). However, due to special features
of 3D content, these encryption standards may not be a suitable solution
for 3D applications. The problem of 3D content encryption is beyond the
application of established and well-known encryption algorithms. This is
primarily due to the structure of 3D content and the way it is used
commercially. Unlike data encryption, where a complete bitstream is
encrypted, 3D content encryption introduces several challenges. One of
the greatest challenges of 3D content encryption is that, in comparison
with traditional data and 2D images, 3D content implies a higher level
representation or semantics, and in many 3D applications, it is necessary
to maintain 3D semantics, such as the spatial and dimensional
requirements. In this project, we conduct research as to address such
requirements.
Anomaly Detection in Wireless Sensor
Networks
The recent advancements in technology have
enhanced the use of Wireless Sensor Network (WSN) in several application
domains such as environment, home, industry, military, health, and infrastructure
monitoring. In these application domains, sensor nodes and sensor
readings are vulnerable to various attacks and anomalies. These anomalies
may degrade overall performance of a Wireless Sensor Network (WSN)
application. Mobile Agents (MA) are being effectively employed in WSNs
for variety of purposes such as distributed data fusion, code and data
dissemination, localization, security, collaborative signal, and
information processing. In this research, MAs are employed for in-situ
verification of suspicious behaviour of a sensor node as part of an
anomaly detection system. This approach is particularly useful for large
scale WSNs where physical verification of anomalous node is cumbersome
and time consuming. Furthermore, this technique is also beneficial in
infrastructure monitoring WSN applications where mobile agent can
non-intrusively verify the anomalous behaviour of sensor nodes before
taking an appropriate action against them.
Body Sensor Networks
The availability of small, low-cost
networked sensors combined with advanced signal processing and
information extraction is driving a revolution in physiological monitoring
and intervention. Body Sensor Networks (BSN) are enabling technologies
for precision healthcare, enhanced sports and fitness training, novel
life-style monitoring, and individualized security. Expected growth of
elderly populations and the corresponding increase in healthcare costs
mandate systems for automated monitoring of physiological conditions,
triage, and remote diagnosis.
Semantics-Based Document
Classification for Data Leakage Detection
The protection of confidential data from
being leaked to the public is conventionally done through firewalls,
virtual private networks and intrusion detection/prevention systems.
However, these systems lack dedicated and proactive protection for confidential
data when it travels through legitimate channels. An emerging technology
in the field of information security called Data Leakage Prevention has
been developed to overcome these problems. It deals with tools working
under a central policy, which analyse networked environments to detect
sensitive data, prevent unauthorized access to it and block channels
associated with potential leak. This requires special data classification
capabilities to distinguish between sensitive and normal data. Not only
this task needs prior knowledge of the sensitive data, but also requires
knowledge of potentially evolved and unknown data. Most current DLPs use
content-based analysis in order to detect sensitive data. This mainly
involves the use of regular expressions, data fingerprinting and
statistical analysis. Regular expressions and data fingerprinting are
widely used as robust detection techniques when the confidential data is
known and unmodified. However, they usually become ineffective if the
confidential data is ambiguous or largely modified. Many forms of
advanced regular expression and data fingerprinting have been studied in
the literature, but they suffer from being inadequate when data is
extremely modified. Also, they require huge amount of indexing and
computations, which introduce significant system overheads.Content statistical analysis
on the other hand deals with nebulous forms of data, while preserving
exact data detection capabilities. It focuses on analysing frequencies
and relationships of words “terms” within a large corpus, to
construct a semantic weight for data. It also facilitates the use of
machine learning algorithms and Bayesian analysis, in order to classify
data.Although proven
effective in other fields like information retrieval (IR), statistical
analysis is not fully explored when it comes to detecting confidential
data leakage. Therefore, in our study we evaluate the effectiveness of
using content statistical analysis in constructing semantics of
confidential data. Moreover, we propose a semantic-aware document
classification model for data leakage detection. This model utilizes
statistical analysis of term weighting, to semantically compare documents
with existing category centroids.
