3,716 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
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction
Moral rhetoric plays a fundamental role in how we perceive and interpret the
information we receive, greatly influencing our decision-making process.
Especially when it comes to controversial social and political issues, our
opinions and attitudes are hardly ever based on evidence alone. The Moral
Foundations Dictionary (MFD) was developed to operationalize moral values in
the text. In this study, we present MoralStrength, a lexicon of approximately
1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary,
based on WordNet synsets. Moreover, for each lemma it provides with a
crowdsourced numeric assessment of Moral Valence, indicating the strength with
which a lemma is expressing the specific value. We evaluated the predictive
potentials of this moral lexicon, defining three utilization approaches of
increased complexity, ranging from lemmas' statistical properties to a deep
learning approach of word embeddings based on semantic similarity. Logistic
regression models trained on the features extracted from MoralStrength,
significantly outperformed the current state-of-the-art, reaching an F1-score
of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of
86.25% over six different datasets. Such findings pave the way for further
research, allowing for an in-depth understanding of moral narratives in text
for a wide range of social issues
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
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