TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-based Intrusion Detection System

Intrusion detection systems (IDS) play a pivotal role in computer security by discovering and repealing malicious activities in computer networks. Anomaly-based IDS, in particular, rely on classification models trained using historical data to discover such malicious activities. In this paper, an improved IDS based on hybrid feature selection and two-level classifier ensembles is proposed. An hybrid feature selection technique comprising three methods, i.e. particle swarm optimization, ant colony algorithm, and genetic algorithm, is utilized to reduce the feature size of the training datasets (NSL-KDD and UNSW-NB15 are considered in this paper). Features are selected based on the classification performance of a reduced error pruning tree (REPT) classifier. Then, a two-level classifier ensembles based on two meta learners, i.e., rotation forest and bagging, is proposed. On the NSL-KDD dataset, the proposed classifier shows 85.8% accuracy, 86.8% sensitivity, and 88.0% detection rate, which remarkably outperform other classification techniques recently proposed in the literature. Results regarding the UNSW-NB15 dataset also improve the ones achieved by several state of the art techniques. Finally, to verify the results, a two-step statistical significance test is conducted. This is not usually considered by IDS research thus far and, therefore, adds value to the experimental results achieved by the proposed classifier

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Improved Genetic Algorithm Multilayer Perceptron Network For Data Classification

Secara umumnya, algoritma genetik (GA) konvensional mempunyai beberapa kelemahan seperti penumpuan pramatang, kecenderungan terperangkap pada penyelesaian optima setempat dan ketidakupayaan penalaan di sekitar kawasan berpotensi. Oleh itu, GA ditambahbaik dengan strategi pencarian, penghasilan semula dan elitisma baharu dicadangkan dalam kajian ini. Pernambahbaikan pertama melibatkan perubahan kepada struktur operasi GA yang mana ia menumpu pencarian di sekitar kawasan berpotensi tinggi. Kedua, teknik baharu penghasilan semula yang dinamakan Segmented Multi-Chromosome Crossover (SMCC) telah diperkenalkan. Teknik tersebut mengelak kemusnahan maklumat hampir optima yang terkandung dalam segmen genetik dan membolehkan generasi baharu mewarisi maklumat penting daripada berbilang induk. Ketiganya, tiga jenis variasi elitism dinamakan sebagai Best Among Normal and Improved Population (BANI), Best Between Similar Rank (BBSR) dan Equally Contributed (EQ) telah dibangunkan. Ia melibatkan pertandingan di kalangan individu terbaik daripada populasi normal dan ditambahbaik untuk kelangsungan pada generasi selepasnya. GA yang ditambahbaik kemudiannya digunakan untuk mengoptimasi dan merekabentuk rangkaian perseptron berbilang lapisan (MLP) secara automatik bagi penyelesaian masalah pengkelasan corak. Bilangan nod terlindung, nilai pemberat sambungan awalan dan pemilihan ciri MLP yang memainkan peranan penting dalam menentukan prestasi pengkelasan dipilih untuk dioptimasi secara automatik oleh GA ditambahbaik. Prestasi GA ditambahbaik telah dinilai menggunakan fungsi ujian penanda aras yangv rumit serta berbilang mod dan dibandingkan dengan GA piawai. Berdasarkan kekerapan sesuatu algoritma menghasilkan keputusan terbaik terhadap fungsi ujian yang berbeza; ianya telah terbukti bahawa prestasi teknik yang dicadangkan mengatasi GA piawai. BANI, BBSR dan EQ mencatatkan 30, 18 dan 17 kekerapan keputusan terbaik masing-masing berbanding GA piawai yang hanya mencatatkan 3 keputusan terbaik. Manakala, prestasi pengkelasan GA-MLP yang ditambahbaik telah dinilai menggunakan set-set data yang berbeza dari segi saiz ciri kemasukan dan bilangan kelas keluaran. Keputusan menunjukkan keberkesanan algoritma baharu daripada segi peratusan ujian kejituan. Peratus peningkatan keseluruhan sebanyak 0.6%, 0.1% dan 0.3% bagi ujian kejituan dicatatkan oleh BANI, BBSR dan EQ berbanding dengan GA-MLP piawai. ____________________________________________________________ In general, conventional genetic algorithm (GA) has several drawbacks such as premature convergence, high tendency to get trapped in local optima solution and incapable of fine tuning around potential region. Thus, new improved GA that focuses on new search, reproduction and elitism strategy is proposed in this study. The first improvement involves changes in the operational structure of GA in which it concentrates the search in highly potential area in the search region. Secondly, a novel reproduction technique called Segmented Multi-Chromosome Crossover (SMCC) is introduced. The proposed technique avoids the destruction of nearly optimal information contained in the gene segment and allows offspring to inherit highly important information among multiple parents. Thirdly, three new variations of elitism scheme namely Best Among Normal and Improved Population (BANI), Best Between Similar Rank (BBSR) and Equally Contributed (EQ) are developed. It involves competition among best individuals from normal and improved population to ensure survival in the next generation. The improved GA is then applied for optimization and automatic design of multilayer perceptron (MLP) neural network in solving pattern classification problem. Hidden node size, initial weights and feature selection of the MLP that play significant role in the classification performance are selected to be automatically optimized by the improved GA. The performance of improved GA has been evaluated using highly complicated and multimodal benchmark test functions and compared with the standard GA. Based on the occurrences of the best result obtained by an algorithm across different test functions; it is proven that the proposed method outperforms standard GA. BANI, BBSR and EQ scores 30, 18 and 17 occurrences respectively compared to the standard GA that only scores 3 occurrences. Meanwhile, the improved GA-MLP classification performance has been evaluated using datasets that vary in input features and output sizes. The results demonstrate the effectiveness of the new algorithms in term of test accuracy percentage. There is an overall improvement of 0.6%, 0.1% and 0.3% in test accuracy of BANI, BBSR and EQ compared to the standard GA-MLP

Effect of nano black rice husk ash on the chemical and physical properties of porous concrete pavement

Black rice husk is a waste from this agriculture industry. It has been found that majority inorganic element in rice husk is silica. In this study, the effect of Nano from black rice husk ash (BRHA) on the chemical and physical properties of concrete pavement was investigated. The BRHA produced from uncontrolled burning at rice factory was taken. It was then been ground using laboratory mill with steel balls and steel rods. Four different grinding grades of BRHA were examined. A rice husk ash dosage of 10% by weight of binder was used throughout the experiments. The chemical and physical properties of the Nano BRHA mixtures were evaluated using fineness test, X-ray Fluorescence spectrometer (XRF) and X-ray diffraction (XRD). In addition, the compressive strength test was used to evaluate the performance of porous concrete pavement. Generally, the results show that the optimum grinding time was 63 hours. The result also indicated that the use of Nano black rice husk ash ground for 63hours produced concrete with good strengt

English character recognition algorithm by improving the weights of MLP neural network with dragonfly algorithm

Character Recognition (CR) is taken into consideration for years. Meanwhile, the neural network plays an important role in recognizing handwritten characters. Many character identification reports have been publishing in English, but still the minimum training timing and high accuracy of handwriting English symbols and characters by utilizing a method of neural networks are represents as open problems. Therefore, creating a character recognition system manually and automatically is very important. In this research, an attempt has been done to incubate an automatic symbols and character system for recognition for English with minimum training and a very high recognition accuracy and classification timing. In the proposed idea for improving the weights of the MLP neural network method in the process of teaching and learning character recognition, the dragonfly optimization algorithm has been used. The innovation of the proposed detection system is that with a combination of dragonfly optimization technique and MLP neural networks, the precisions of the system are recovered, and the computing time is minimized. The approach which was used in this study to identify English characters has high accuracy and minimum training time

Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks

Evolving Spiking Neural Network (ESNN) is the third generation of artificial neural network that has been widely used in numerous studies in recent years. However, there are issues of ESSN that need to be improved; one of which is its parameters namely the modulation factor (Mod), similarity factor (Sim) and threshold factor (C) that have to be manually tuned for optimal values that are suitable for any particular problem. The objective of the proposed work is to automatically determine the optimum values of the ESNN parameters for various datasets by integrating the Firefly Algorithm (FA) optimizer into the ESNN training phase and adaptively searching for the best parameter values. In this study, FA has been modified and improved, and was applied to improve the accuracy of ESNN structure and rates of classification accuracy. Five benchmark datasets from University of California, Irvine (UCI) Machine Learning Repository, have been used to measure the effectiveness of the integration model. Performance analysis of the proposed work was conducted by calculating classification accuracy, and compared with other parameter optimisation methods. The results from the experimentation have proven that the proposed algorithms have attained the optimal parameters values for ESNN