Categories Malware using Neural Networks based on Feature Selection by Genetic algorithm

Concurrent with the ever-increasing growth of information and communication technology (ICT) and the dramatic expansion ofcomputer networks, we observe different forms of attacks and intrusions to networks; thus intrusion detection systems (IDSs) are consideredas a vital part of each network connected to internet in the modern world. Neural networks are considered as a popular method used in IDS.Two major problems in these networks, i.e. long training time and inattention to features' domain, have made necessary development and/orimprovement of the model. Feature selection techniques are used in the neural networks in order to develop a new model to speed up theattack detection, to reduce error notification rate and finally to enhance system's efficiency. In this study, for enhancing efficiency of theneural network in detecting intrusions, a genetic algorithm was used for selecting features. The suggested model was examined and assessedon NSL-KDD dataset which is the modified version of the KDD-CUP99. The experimental results indicate that the suggested model is veryefficient in enhancing precision and recall of attack detection and reducing the error notification rate and also is able to offer more accuratedetections in contrast to the basic model

Comparing performance of MLP and RBF neural network models for predicting South Africa’s energy consumption

In view of the close association between energy and economic growth, South Africa’s aspirations for higher growth, more energy is required; formulating a long-term economic development plan and implementing an energy strategy for a country /industry necessitates establishing the correct relationship between energy and the economy. As insufficient energy or a lack thereof is reported to be a major cause of social and economic poverty, it is very important to select a model to forecast the consumption of energy reasonably accurately. This study presents techniques based on the development of multilayer perceptron (MLP) and radial basis function (RBF) of artificial neural network (ANN) models, for calculating the energy consumption of South Africa’s industrial sector between 1993 and 2000. The approach examines the energy consumption in relation to the gross domestic product. The results indicate a strong agreement between model predictions and observed values, since the mean absolute percentage error is below 5%. When performance indices are compared, the RBF-based model is a more accurate predictor than the MLP model

Αξιολόγηση Υπηρεσιών Βιβλιοθηκών μέσω Τεχνικού Νευρωνικού Δικτύου

Περιέχει το πλήρες κείμενοΣκοπός της επικείμενης εργασίας είναι η δημιουργία ενός συστήματος αξιολόγησης βιβλιοθηκών, το οποίο χρησιμοποιείται με σκοπό τη μέτρηση/κατάταξη της αποδοτικότητας των λειτουργιών μιας βιβλιοθήκης. Οι παρακάτω μετρήσεις έγιναν με βάση τους καθιερωμένους δείκτες της ΜΟΠΑΒ. Καταλήξαμε στους εν λόγω δείκτες, τους οποίους θα εξετάσουμε και στους οποίους θα βασίσουμε την έρευνα λόγω των χαρακτηριστικών που παρουσιάζουν. Αρχικά, πρόκειται για τους πιο αντιπροσω- πευτικούς δείκτες για την αξιολόγηση μιας βιβλιοθήκης, καθώς οι δείκτες αυτοί αφορούν όλα τα είδη των βιβλιοθηκών και όχι μόνο τις ειδικές ή κάποιο συγκεκριμένο είδος. Πέρα από τη συχνότητα με την οποία τους συναντάμε, σημαντικός λόγος για την επιλογή τους ήταν πως αφορούν δείκτες υπηρεσιών που σχεδόν όλες οι βιβλιοθήκες έχουν· επίσης, αφορούν τις δύο από τις τρεις κύριες λειτουργίες μιας βιβλιοθήκης (πρόσκτηση και δανεισμό) και επιπλέον είναι ευκατανόητοι και εύκολα επε- ξεργάσιμοι για την εξαγωγή αποτελεσμάτων. Τα αποτελέσματα από την εκάστοτε επεξεργασία και κρίση είναι έγκυρα και αξι- όλογα, ενώ αυτονόητο είναι ότι δεν υπάρχει κόστος για την εξαγωγή τους και δεν απαιτείται κάποιο συγκεκριμένο εργαλείο. Τα κριτήρια είναι καταρχήν υποκειμενικά, αλλά η ανάλυση και η αξιολόγηση γίνεται με αντικειμενικότητα. Για τη δημιουργία του συστήματος αξιολόγησης βιβλιοθηκών βασιστήκαμε στη μάθηση με επίβλεψη με τη βοήθεια ενός νευρωνικού δικτύου

Makine öğrenmesi teknikleriyle saldırı tespiti: Karşılaştırmalı analiz

İnternet, günlük hayatımızın vazgeçilmez bir parçasıdır. Artan web uygulamaları ve kullanıcı sayısı, veri güvenliği açısından bazı riskleri de beraberinde getirmiştir. Ağ güvenliği için önemli araçlardan biri olan saldırı tespit sistemleri, güvenli iç ağlara yapılan saldırıları ve beklenmeyen erişim taleplerini tespit etmede başarılı bir şekilde kullanılmaktadır. Günümüzde, pek çok araştırmacı, daha etkin saldırı tespit sistemi gerçekleştirilmesi amacıyla çalışma yapmaktadır. Bu amaçla literatürde farklı makine öğrenme teknikleri ile gerçekleştirilmiş pek çok saldırı tespit sistemi vardır. Yapılan bu çalışmada, saldırı tespit sistemlerinde sıklıkla kullanılan makine öğrenme teknikleri araştırılmış, kullandıkları sınıflandırıcılar, veri setleri ve elde edilen başarılar değerlendirilmiştir. Bu amaçla 2007-2013 yılları arasında SCI, SCI Expanded ve EBSCO indekslerince taranan ulusal ve uluslararası dergilerde yayınlanmış 65 makale incelenmiş, sonuçlar, karşılaştırılmalı bir şekilde sunulmuştur. Böylece, gelecekte yapılacak makine öğrenme teknikleri ile saldırı tespiti çalışmalarına bir bakış açısı kazandırılması amaçlanmıştır

Anomaly-based network intrusion detection enhancement by prediction threshold adaptation of binary classification models

Network traffic exhibits a high level of variability over short periods of time. This variability impacts negatively on the performance (accuracy) of anomaly-based network Intrusion Detection Systems (IDS) that are built using predictive models in a batch-learning setup. This thesis investigates how adapting the discriminating threshold of model predictions, specifically to the evaluated traffic, improves the detection rates of these Intrusion Detection models. Specifically, this thesis studied the adaptability features of three well known Machine Learning algorithms: C5.0, Random Forest, and Support Vector Machine. The ability of these algorithms to adapt their prediction thresholds was assessed and analysed under different scenarios that simulated real world settings using the prospective sampling approach. A new dataset (STA2018) was generated for this thesis and used for the analysis. This thesis has demonstrated empirically the importance of threshold adaptation in improving the accuracy of detection models when training and evaluation (test) traffic have different statistical properties. Further investigation was undertaken to analyse the effects of feature selection and data balancing processes on a model’s accuracy when evaluation traffic with different significant features were used. The effects of threshold adaptation on reducing the accuracy degradation of these models was statistically analysed. The results showed that, of the three compared algorithms, Random Forest was the most adaptable and had the highest detection rates. This thesis then extended the analysis to apply threshold adaptation on sampled traffic subsets, by using different sample sizes, sampling strategies and label error rates. This investigation showed the robustness of the Random Forest algorithm in identifying the best threshold. The Random Forest algorithm only needed a sample that was 0.05% of the original evaluation traffic to identify a discriminating threshold with an overall accuracy rate of nearly 90% of the optimal threshold."This research was supported and funded by the Government of the Sultanate of Oman represented by the Ministry of Higher Education and the Sultan Qaboos University." -- p. i