Mobile network anomaly detection and mitigation: The NEMESYS approach

Mobile malware and mobile network attacks are becoming a significant threat that accompanies the increasing popularity of smart phones and tablets. Thus in this paper we present our research vision that aims to develop a network-based security solution combining analytical modelling, simulation and learning, together with billing and control-plane data, to detect anomalies and attacks, and eliminate or mitigate their effects, as part of the EU FP7 NEMESYS project. These ideas are supplemented with a careful review of the state-of-the-art regarding anomaly detection techniques that mobile network operators may use to protect their infrastructure and secure users against malware

Instance Selection for Semi-Supervised Learning in Multi-Expert Systems: A Comparative Analysis

Semi-Supervised learning methods utilize abundant unlabeled data in order to enlarge the training set and to update classifiers. For the purpose, standard methods label and select unknown data which are classified with high confidence by the current classifier. This paper presents an experimental investigation on the use of semi-supervised learning and discusses three methods (our feedback-based technique and two different algorithms known in literature) for retraining individual classifiers in a multi-expert scenario. More specifically, we analyze the entire system so that a misclassified sample for a particular expert, respect to the final decision, can be used to update itself if that sample is classified with a confidence greater than a specific threshold by multi-expert system. Experimental tests, carried out on the CEDAR (handwritten digits) database, are presented and some considerations about accuracy, space and time between different methods are provided. For the purpose, a SVM classifier and five different combination techniques at abstract and measurement level have been used. The results show that our feedback-based algorithm outperforms Self-Training and Co-Training algorithms when the training set is very small and a suitable number of iterations is performed in the feedback process

INSTANCE SELECTION METHOD IN MULTI-EXPERT SYSTEM FOR ONLINE SIGNATURE VERIFICATION

In real world applications, signature verification systems should be able to learn continuously, as new signatures providing additional information become available. In fact, new data are not equally relevant for system improvement and a suitable data filtering strategy is generally required. In this context, instance selection is an important task for signature verification systems in order to select useful signatures to be considered for updating system knowledge, removing irrelevant and/or redundant instances from new data. This paper proposes a new feedback-based learning strategy to update the knowledge-base in multi-expert signature verification system. In particular, the collective behavior of classifiers is considered to select the samples for updating system knowledge. Evaluation tests provide a comparison between our (not naïve) approach and the traditional approach, which uses the entire new dataset for feedback. For the purpose, two state-of-the-art classifiers (NB and k-NN) and two abstract level combination techniques (MV and WMV) were used. The experimental results, carried out considering the SUSig database, demonstrate the effectiveness of the new strategy

Evaluating Threshold for Retraining Rule in Semi-Supervised Learning using Multi-Expert System

The creation of training set, for pattern recognition, is a difficult, expensive and time consuming task because it requires the efforts of experienced human annotators. On the other hand, unlabeled data can be obtained cheaply, but there are few ways to use them. Semi-Supervised learning uses both labeled and unlabeled data for classification task. In this paper we propose to apply semi-supervised learning and three methods in order to re-train individual classifiers in a multi-expert scenario. More specifically, these experiments are focused on acceptance threshold that defines what data are selected in the feedback-based process. Our approach analyzes the entire system so that a misclassified sample, respect to the final decision, by particular expert can be used to update itself if that sample is classified with a confidence greater than a specific threshold. Experimental results, carried out on the CEDAR (handwritten digits) database, show a comparison between our approach and Self-Training and Co-Training algorithms. The SVM classifier and two different combination techniques at measurement level have been used

About Retraining Rule in Multi-Expert Intelligent System for Semi-Supervised learning using SVM classifiers

Training a system for pattern recognition is a task that require a large amount of labeled data. However, the creation of such training set is often difficult, expensive and time consuming because it requires the efforts of experienced human annotators. On the other hand, unlabeled data may be relatively easy to collect, but there are few ways to use them. Semi-Supervised learning is a useful approach to reduce human labor and improve accuracy using unlabeled data, together with labeled data. This paper proposes three methods in order to re-train classifiers in a multi-expert scenario, when new (unknown) data are available. In fact, when a multi-expert system is adopted, the collective behavior of classifiers can be used both for recognition aims and also selection of the most profitable samples for system re-train. More specifically a misclassified sample for a particular expert can be used to update the expert itself if the collective behavior of the multi-expert system allows to classify the sample with high confidence. In addition, this paper provides a comparison between the new approach and those available in literature for semi-supervised learning using the SVM classifier by taking into account four different combination techniques at abstract and measurement level. The experimental results, that have been obtained using the handwritten digits of the CEDAR database, demonstrate the effectiveness of the proposed approach

Learning Strategies for Knowledge-base Updating in Online Signature Verification Systems

Updating of reference information is a crucial task for automatic signature verification. In fact, signature characteristics vary in time and whatever approach is considered the effectiveness of a signature verification system strongly depends on the extent to which reference information is able to model the changeable characteristics of users’ signatures. This paper addresses the problem of knowledge-base updating in multi-expert signature verification sys-tems and introduces a new strategy which exploits the collective behavior of classifiers to select the most profitable samples for knowledge-base updating. The experimental tests, carried out using the SUSig database, demonstrate the effectiveness of the new strategy