Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

A Note on the Generalization Performance of Kernel Classifiers with Margin

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

The municipality and neighborhood associative entities in argentine cities: its resignification after the decentralization of the ‘80s A case study: The municipality of Río Cuarto and their neighborhood associations

In Argentina, the restructuring of the State initially raised as a public policy in the 1980s and in-depth in the ‘ 90s under the neoliberal model accentuated - between other processes, of the administrative  decentralization, which also resulted in new roles awarded to municipalities. That’s how various actors in society charged leadership. The local and urban were the subject of renewed interpretations,  scenarios where practices more fully participatory citizen could be settled. In the neighborhoods  of cities, grass-roots organizations cultivated his role as space intermediation. This article discusses  and reflects on these new roles that launched from the changes in articulation with the municipality  since the mid-’ 80s and ‘ 90s, and problematizes particularly about the contents and scope of participatory practices inside and outside of organizations of civil society in the neo-liberal situation.

Associative norms of 58 Spanish words for children from 8 to 13 years old. 'Normas asociativas para 58 palabras castellanas en una muestra de niños entre 8 y 13 años'.

Resumen tomado de la revista

False recognition production indexes in forward associative strength (FAS) lists with three critical words

Resumen tomado de la publicaci??n

Associative learning with and without perceptual awareness

Resumen tomado de la publicaci??n

Modeling infant speech sound discrimination using simple associative networks

Infants' responses in speech sound discrimination tasks can be nonmonotonic over time. Stager and Werker (1997) reported such data in a bimodal habituation task. In this task, 8-month-old infants were capable of discriminations that involved minimal contrast pairs, whereas 14-month-old infants were not. It was argued that the older infants' attenuated performance was linked to their processing of the stimuli for meaning. The authors suggested that these data are diagnostic of a qualitative shift in infant cognition. We describe an associative connectionist model showing a similar decrement in discrimination without any qualitative shift in processing. The model suggests that responses to phonemic contrasts may be a nonmonotonic function of experience with language. The implications of this idea are discussed. The model also provides a formal framework for studying habituation-dishabituation behaviors in infancy.

Structure and rheology of aqueous micellar solutions and gels formed from an associative poly(oxybutylene)-poly(oxyethylene)-poly(oxybutylene) triblock copolymer

The structure and shear flow behaviour of aqueous micellar solutions and gels formed by an amphiphilic poly(oxybutylene)-poly(oxyethylene)-poly(oxybutylene) triblock copolymer with a lengthy hydrophilic poly(oxyethylene) block has been investigated by rheology, small angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS). SANS revealed that bridging of chains between micelles introduces, in the micellar solution, an attractive long-range component which can be described through a potential of interaction corresponding to sticky soft spheres. The strength of the attractive interaction increases with increasing concentration. Rheology showed that the dependence of the storage modulus with temperature can be explained as a function of the micellar bridging, micellisation and phase morphology. SAXS studies showed that the orientation adopted by the system in the get phase under shear is similar to that previously observed by us for the gel phase of a poly(oxyethylene)-poly(oxybutylene) diblock copolymer with a long poly(oxyethylene) chain, suggesting that the micellar corona/core length ratio and not the architecture of the block copolymer influences the alignment of the gel phase under shear.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Comparison of extrasystolic ECG signal classifiers using discrete wavelet transforms

This work compares and contrasts results of classifying time-domain ECG signals with pathological conditions taken from the MITBIH arrhythmia database. Linear discriminant analysis and a multi-layer perceptron were used as classifiers. The neural network was trained by two different methods, namely back-propagation and a genetic algorithm. Converting the time-domain signal into the wavelet domain reduced the dimensionality of the problem at least 10-fold. This was achieved using wavelets from the db6 family as well as using adaptive wavelets generated using two different strategies. The wavelet transforms used in this study were limited to two decomposition levels. A neural network with evolved weights proved to be the best classifier with a maximum of 99.6% accuracy when optimised wavelet-transform ECG data wits presented to its input and 95.9% accuracy when the signals presented to its input were decomposed using db6 wavelets. The linear discriminant analysis achieved a maximum classification accuracy of 95.7% when presented with optimised and 95.5% with db6 wavelet coefficients. It is shown that the much simpler signal representation of a few wavelet coefficients obtained through an optimised discrete wavelet transform facilitates the classification of non-stationary time-variant signals task considerably. In addition, the results indicate that wavelet optimisation may improve the classification ability of a neural network. (c) 2005 Elsevier B.V. All rights reserved.