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An ensemble deep learning classifier stacked with fuzzy ARTMAP for malware detection

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10477-10493页

作者： Al-Andoli, Mohammed Nasser Tan, Shing Chiang Sim, Kok Swee Goh, Pey Yun Lim, Chee Peng Multimedia Univ Fac Engn & Technol Cyberjaya Malaysia Multimedia Univ Fac Informat Sci & Technol Cyberjaya Malaysia Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic Australia

Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm;therefore, their training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods.

关键词： Ensemble learning fuzzy ARTMAP deep learning malware detection particle swarm optimization backpropagation algorithm

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Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2009年第1期23卷 13-26页

作者： Yetilmezsoy, Kaan Sapci-Zengin, Zehra Yildiz Tech Univ Fac Civil Engn Dept Environm Engn TR-34349 Istanbul Turkey

A three-layer Artificial Neural Network (ANN) model (9:12:1) for the prediction of Chemical Oxygen Demand Removal Efficiency (CODRE) of Upflow Anaerobic Sludge Blanket (UASB) reactors treating real cotton textile wastewater diluted with domestic wastewater was presented. To validate the proposed method, an experimental study was carried out in three lab-scale UASB reactors to investigate the treatment efficiency on total COD reduction. The reactors were operated for 80 days at mesophilic conditions (36-37.5C) in a temperature-controlled water bath with two hydraulic retention times (HRT) of 4.5 and 9.0 days and with organic loading rates (OLR) between 0.072 and 0.602 kg COD/m3p/day. Five different dilution ratios of 15, 30, 40, 45 and 60% with domestic wastewater were employed to represent seasonal fluctuations, respectively. The study was undertaken in a pH range of 6.20-8.06 and an alkalinity range of 1,350-1,855 mg/l CaCO3. The concentrations of volatile fatty acids (VFA) and total suspended solids (TSS) were observed between 420 and 720 mg/l CH3COOH and 68-338 mg/l, respectively. In the study, a wide range of influent COD concentrations (CODi) between 651 and 4,044 mg/l in feeding was carried out. CODRE of UASB reactors being output parameter of the conducted anaerobic treatment was estimated by nine input parameters such as HRT, pH, CODi concentration, operating temperature, alkalinity, VFA concentration, dilution ratio (DR), OLR, and TSS concentration. After backpropagation (BP) training combined with principal component analysis (PCA), the ANN model predicted CODRE values based on experimental data and all the predictions were proven to be satisfactory with a correlation coefficient of about 0.8245. In the ANN study, the Levenberg-Marquardt algorithm (LMA) was found as the best of 11 BP algorithms. In addition to determination of the optimal ANN structure, a linear-nonlinear study was also employed to investigate the effects of input variables on CODRE values

关键词： Textile wastewater Anaerobic treatment COD removal efficiency Neural network backpropagation algorithm Modeling

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Simulated annealing and weight decay in adaptive learning: The SARPROP algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1998年第4期9卷 662-668页

作者： Treadgold, NK Gedeon, TD Univ New S Wales Sch Engn & Comp Sci Dept Informat Engn Sydney NSW 2052 Australia

A problem with gradient descent algorithms is that they can converge to poorly performing local minima. Global optimization algorithms address this problem, but at the cost of greatly increased training times. This work examines combining gradient descent with the global optimization technique of simulated annealing (SA). Simulated annealing in the form of noise and weight decay is added to resiliant backpropagation (RPROP), a powerful gradient descent algorithm for training feedforward neural networks. The resulting algorithm, SARPROP, is shown through various simulations not only to be able to escape local minima, but is also able to maintain, and often improve the training times of the RPROP algorithm. In addition, SARPROP may be used with a restart training phase which allows a more thorough search of the error surface and provides an automatic annealing schedule.

关键词： adaptive backpropagation algorithm gradient descent neural network RPROP simulated annealing weight decay

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MODELING OF PAVEMENT ROUGHNESS UTILIZING ARTIFICIAL NEURAL NETWORK APPROACH FOR LAOS NATIONAL ROAD NETWORK

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2022年第4期28卷 261-277页

作者： Gharieb, Mohamed Nishikawa, Takafumi Nakamura, Shozo Thepvongsa, Khampaseuth Nagasaki Univ Grad Sch Engn 1-14 Bunkyo Machi Nagasaki 8528521 Japan Natl Univ Laos Fac Engn Lao Thai Rd Sisatanak Vientiane Laos

The International Roughness Index (IRI) has become the reference scale for assessing pavement roughness in many highway agencies worldwide. This research aims to develop two Artificial Neural Network (ANN) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Laos Pavement Management System (PMS) database for National Road Network (NRN). The final database consisted of 269 and 122 observations covering 1850 km of DBST NRN and 718 km of AC NRN, respectively. The proposed models predict IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The obtained data were randomly divided into training (70%), validation (15%), and testing (15%) datasets. The statistical evaluation results of the training dataset reveal that both ANN models (DBST and AC) have good prediction ability with high values of coefficient of determination (R-2 = 0.96 and 0.94) and low values of Mean Absolute Error (MAE = 0.23 and 0.19) and Mean Squared Percentage Error (RMSPE = 7.03 and 9.98). Eventually, the goodness of fit of the proposed ANN models was compared with the Multiple Linear Regression (MLR) models previously developed under the same conditions. The results show that ANN models yielded higher prediction accuracy than MLR models.

关键词： International Roughness Index (IRI) Laos pavement management system (PMS) artificial neural network (ANN) backpropagation algorithm double bituminous surface treatment (DBST) asphalt concrete (AC) pavement age cumulative equivalent single-axle load (CESAL) pavement performance model

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Image noise types recognition using convolutional neural network with principal components analysis

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IET IMAGE PROCESSING 2017年第12期11卷 1238-1245页

作者： Khaw, Hui Ying Soon, Foo Chong Chuah, Joon Huang Chow, Chee-Onn Univ Malaya Dept Elect Engn Fac Engn Kuala Lumpur 50603 Malaysia

This study presents a model to effectively recognise image noise of different types and levels: impulse, Gaussian, Speckle and Poisson noise, and a mixture of multiple types of the noise. To classify image noise type, the convolutional neural network (CNN) method with backpropagation algorithm and stochastic gradient descent optimisation techniques are implemented. In order to reduce the training time and computational cost of the algorithm, the principal components analysis (PCA) filters generating strategy is deployed to obtain data adaptive filter banks. The authors validated their designed CNN with PCA for noise types recognition model with degraded images containing noise of single and combination of multiple types, with a total of 11,000 and 1650 datasets for training and testing purposes, respectively. The variety and complexity of data have never been addressed before in any other research work. The capability of their intelligent system in handling images degraded under this complicated environment has surpassed human-eye performance in noise types recognition. The authors' experiments have proven the reliability of the proposed noise types recognition model by having achieved an overall average accuracy of 99.3% while recognising eight classes of noise.

关键词： image classification Gaussian noise impulse noise principal component analysis speckle backpropagation stochastic programming gradient methods channel bank filters image filtering adaptive filters image noise types recognition model convolutional neural network principal components analysis Poisson noise speckle Gaussian noise impulse noise image noise type classification CNN method backpropagation algorithm stochastic gradient descent optimisation techniques computational cost reduction training time reduction PCA filter generating strategy data adaptive filter banks data complexity intelligent system human-eye performance

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On the use of neural networks to evaluate groundwater levels in fractured media

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JOURNAL OF HYDROLOGY 2005年第1-4期307卷 92-111页

作者： Lallahem, S Mania, J Hani, A Najjar, Y Univ Lille Ecole Polytech Dept Geotech & Genie Civil CNRSUMR 8107LML F-59655 Villeneuve Dascq France Kansas State Univ Dept Civil Engn Manhattan KS 66506 USA

This paper evaluates the feasibility of using artificial neural network (ANN) methodology for estimating the groundwater level in some piezometers implanted in unconfined chalky aquifer of Northern France. These aquifers are the most susceptible to depletion and contamination, with the recharge rate and dominant processes determining their level of vulnerability. Groundwater level simulation in a chalky media is regarded as a difficult subject in hydrogeology due to the complexity of the physical processes involved, the variability of piezometry in space and time and the aquifer response in fissured and matrix blocks. In the present project, after a detailed geologic and hydrogeologic study of the sector, we simulated the groundwater level by Neural Network. The first objective was to determine the most influential parameters which impact groundwater level in fissured chalky media. The second objective was to investigate the effect of temporal and spatial information by considering current and past data sets along with the use of a variety of piezometer readings. The third objective was to simulate the groundwater level in a selected piezometer. The reasonably good ANN-based simulations revealed the merit of using ANNs and specifically Multi Layer Perceptron (MLP) models. The proposed ANN methodology using minimal lag and number of hidden nodes, along with the optimal number of spatial and temporal variables consistently produced the best performing network-based simulation models. (c) 2004 Elsevier B.V. All rights reserved.

关键词： ANN chalk groundwater fractured media backpropagation algorithm

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Application of neural networks to software quality modeling of a very large telecommunications system

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第4期8卷 902-909页

作者： Khoshgoftaar, TM Allen, EB Hudepohl, JP Aud, SJ NORTEL RES TRIANGLE PK NC USA

Society relies on telecommunications to such an extent that telecommunications software must have high reliability. Enhanced measurement for early risk assessment of latent defects (EMERALD) is a joint project of Nortel and Bell Canada for improving the reliability of telecommunications software products. This paper reports a case study of neural-network modeling techniques developed for the EMERALD system. The resulting neural network is currently in the prototype testing phase at Nortel. Neural-network models can be used to identify fault-prone modules for extra attention early in development, and thus reduce the risk of operational problems with those modules. We modeled a subset of modules representing over seven million lines of code from a very large telecommunications software system. The set consisted of those modules reused with changes from the previous release. The dependent variable was membership in the class of fault-prone modules. The independent variables were principal components of nine measures of software design attributes. We compared the neural-network model with a nonparametric discriminant model and found the neural-network model had better predictive accuracy.

关键词： backpropagation algorithm classification discriminant analysis fault-prone modules neural network principal components analysis software metrics

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Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning

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MATHEMATICAL PROGRAMMING 2021年第1期188卷 19-51页

作者： Bolte, Jerome Pauwels, Edouard Univ Toulouse 1 Capitole Toulouse Sch Econ Toulouse France Univ Toulouse CNRS IRIT Toulouse France DEEL IRT St Exupery Toulouse France

Modern problems in AI or in numerical analysis require nonsmooth approaches with a flexible calculus. We introduce generalized derivatives called conservative fields for which we develop a calculus and provide representation formulas. Functions having a conservative field are called path differentiable: convex, concave, Clarke regular and any semialgebraic Lipschitz continuous functions are path differentiable. Using Whitney stratification techniques for semialgebraic and definable sets, our model provides variational formulas for nonsmooth automatic differentiation oracles, as for instance the famous backpropagation algorithm in deep learning. Our differential model is applied to establish the convergence in values of nonsmooth stochastic gradient methods as they are implemented in practice.

关键词： Deep learning Automatic differentiation backpropagation algorithm Nonsmooth stochastic optimization Definable sets o-Minimal structures Stochastic gradient Clarke subdifferential First order methods

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Analysis of pattern classification for the multidimensional parity-bit-checking problem with hybrid evolutionary feed-forward neural network

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NEUROCOMPUTING 2007年第7-9期70卷 1511-1524页

作者： Mangal, Manish Singh, Manu Pratap Dr BR Ambedkur Univ Inst Comp & Informat Sci Dept Comp Sci Agra Uttar Pradesh India

This paper describes the simulation of two hybrid evolutionary algorithms (EAs) to the feedforward neural networks (NNs) used in classification problems. Besides backpropagation algorithm, simple genetic algorithm and random search algorithm, the paper considers simple hybrid genetic algorithm and hybrid random search algorithm. The objective is to analyze the performance of hybrid genetic algorithm and hybrid random search algorithm over other discussed algorithms for the classification problem. The experiments considered a feedforward NN trained with simple hybrid genetic algorithm/hybrid random search algorithm and 39 types of network structures and artificial data sets. In most cases, the hybrid evolutionary feedforward NNs seemed to be better than the other algorithms. We found few differences in the performance of the networks trained by applying the hybrid genetic algorithms, but found ample differences in the execution time. The results suggest that the hybrid evolutionary feedforward NN might be the best algorithm on the data sets we tested. (c) 2006 Elsevier B.V. All rights reserved.

关键词： hybrid evolutionary algorithm multilayer feedforward neural network backpropagation algorithm genetic algorithm and random search algorithm

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Neighbor discovery protocol anomaly-based detection system using neural network algorithm

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2024年第3期23卷 1787-1803页

作者： Saad, Redhwan M. A. Anbar, Mohammed Manickam, Selvakumar Shaheen, Samir I. Hasbullah, Iznan H. Ibb Univ Fac Engn Dept Elect Engn Ibb 70270 Yemen Univ Sains Malaysia USM Natl Adv Ctr IPv6 Gelugor Penang Malaysia Cairo Univ Fac Engn Dept Comp Engn Giza 12613 Egypt

The exponential increase in Internet-facing devices in the last decade has resulted in IP address exhaustion due to the limitations of the existing IPv4 address space. Therefore, the Internet Engineering Task Force engineered a new version of the Internet protocol known as Internet Protocol Version 6 (IPv6) to resolve the issue. However, IPv6 is highly dependent on the neighbor discovery protocol (NDP), which, unfortunately, has well-known vulnerabilities in its underlying messaging protocol, the Internet Control Message Protocol version 6. So, the NDP flaws leave the IPv6 network open to many security threats and attacks, including man-in-the-middle, spoofing, and denial-of-service attacks, which are the most annoying attack at the network layer. Unfortunately, one of the critical issues plaguing the existing anomaly-based detection system is the effectiveness of detecting NDP-based DDoS attacks, which requires urgent attention. This paper suggests a system to find network traffic patterns that are not normal that are caused by NDP-based attacks. It does this by teaching neural networks how to recognize network attack patterns using the backpropagation algorithm. The proposed system is a big step forward from where the field is now because it uses a complex neural network algorithm to create an NDP anomaly-based detection system. Using a real dataset to test the proposed system's performance shows that it can find NDP anomalies with a 99.95% success rate, a 99.92% precision rate, a 99.98% recall rate, an F1-Score of 99.98%, and a 0.040% false positive rate. Also, the proposed approach shows better results compared to other existing approaches.

关键词： NDP Anomaly detection Neural networks backpropagation algorithm Feature selection IPv6 network

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