4 research outputs found
MVGL analyser for multi-classifier based spam filtering system
In the last decade, the rapid growth of the Internet and email, there has been a dramatic growth in spam. Spam is commonly defined as unsolicited email messages and protecting email from the infiltration of spam is an important research issue. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs, which is unacceptable to users sometimes. This paper presents an approach to overcome the burden of GL (grey list) analyzer as further refinements to our multi-classifier based classification model (Islam, M. and W. Zhou 2007). In this approach, we introduce a ldquomajority voting grey list (MVGL)rdquo analyzing technique which will analyze the generated GL emails by using the majority voting (MV) algorithm. We have presented two different variations of the MV system, one is simple MV (SMV) and other is the ranked MV (RMV). Our empirical evidence proofs the improvements of this approach compared to the existing GL analyzer of multi-classifier based spam filtering process.<br /
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Improving the performance of free-text keystroke dynamics authentication by fusion
Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR)
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Free-text keystroke dynamics authentication with a reduced need for training and language independency
This research aims to overcome the drawback of the large amount of training data required
for free-text keystroke dynamics authentication. A new key-pairing method, which is based
on the keyboard’s key-layout, has been suggested to achieve that. The method extracts
several timing features from specific key-pairs. The level of similarity between a user’s
profile data and his or her test data is then used to decide whether the test data was provided
by the genuine user. The key-pairing technique was developed to use the smallest amount of
training data in the best way possible which reduces the requirement for typing long text in
the training stage. In addition, non-conventional features were also defined and extracted
from the input stream typed by the user in order to understand more of the users typing
behaviours. This helps the system to assemble a better idea about the user’s identity from the
smallest amount of training data. Non-conventional features compute the average of users
performing certain actions when typing a whole piece of text. Results were obtained from the
tests conducted on each of the key-pair timing features and the non-conventional features,
separately. An FAR of 0.013, 0.0104 and an FRR of 0.384, 0.25 were produced by the timing
features and non-conventional features, respectively. Moreover, the fusion of these two
feature sets was utilized to enhance the error rates. The feature-level fusion thrived to reduce
the error rates to an FAR of 0.00896 and an FRR of 0.215 whilst decision-level fusion
succeeded in achieving zero FAR and FRR. In addition, keystroke dynamics research suffers
from the fact that almost all text included in the studies is typed in English. Nevertheless, the
key-pairing method has the advantage of being language-independent. This allows for it to be
applied on text typed in other languages. In this research, the key-pairing method was applied
to text in Arabic. The results produced from the test conducted on Arabic text were similar to
those produced from English text. This proves the applicability of the key-pairing method on
a language other than English even if that language has a completely different alphabet and
characteristics. Moreover, experimenting with texts in English and Arabic produced results
showing a direct relation between the users’ familiarity with the language and the
performance of the authentication system