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bioinspired artificial intelligence based android malware detection and classification for cybersecurity applications

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ALEXANDRIA ENGINEERING JOURNAL 2024年 100卷 142-152页

作者： Alotaibi, Shoayee Dlaim Alabduallah, Bayan Said, Yahia Hassine, Siwar Ben Haj Alzubaidi, Abdulaziz A. Alamri, Maha Al Zanin, Samah Majdoubi, Jihen Univ Hail Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Hail Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 91431 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha Saudi Arabia Umm Al Qura Univ Coll Engn & Comp Al qunfudah Dept Comp Sci Mecca Saudi Arabia Al Baha Univ Fac Comp & Informat Dept Syst & Networks Bahah Saudi Arabia Prince Sattam bin Abdulaziz Univ Appl Coll Dept Comp Sci Kharj Saudi Arabia Majmaah Univ Coll Sci & Humanities Alghat Dept Comp Sci Al Majmaah 11952 Saudi Arabia

With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps;thus, it develops the aim of malware that uses the vulnerability of safety measures. Identifying Android malware in smartphones is a vital target for the cyber community to eliminate menacing malware instances. Drawing stimulus from the adaptability and efficacy of biological systems, these methods emulate nature's problem-solving systems for identifying malicious software. By integrating principles, namely, swarm intelligence (SI), neural networks (NN), and genetic algorithms (GA), these bioinspired systems reveal exceptional efficiency in identifying both known and developing Android malware attacks. This bioinspired system provides a capable avenue for robust Android malware detection from an ever-shifting threat landscape. This article designs a bioinspired Artificial Intelligence-based Android Malware Detection and Classification (BAI-AMDC) technique for Cybersecurity Applications. The BAIAMDC technique exploits the concept of bioinspired algorithms with a DL approach for the classification and detection of Android malware. In the BAI-AMDC technique, an improved cockroach swarm optimization algorithm-based feature selection (ICSOA-FS) technique can be applied to choose optimum features. The BAIAMDC technique employs a bidirectional gated recurrent unit (BiGRU) model for Android malware detection. An arithmetic optimization algorithm (AOA) can be utilized to enhance the detection performance of the BAIAMDC technique. The experimental validation of the BAI-AMDC system can be performed on the CICAndMal2017 database with 10,000 instances. The simulation values highlighted the productive ability of the BAIAMDC system on the Android malware recognition process.

关键词： bioinspired algorithms Metaheuristics Artificial intelligence Android malware Feature selection

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A review of bioinspired computer-aided design tools for hardware design

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2013年第8期25卷 1015-1036页

作者： Lanchares, Juan Garnica, Oscar Fernandez-de-Vega, Francisco Ignacio Hidalgo, J. UCM Parallel Architectures & Bioinspired Algorithms G Madrid Spain Univ Extremadura Merida Spain

During the tools have been also influenced by this evolutionary fashion. In this paper, we give a broader view of the application of techniques inspired by nature to hardware design and parallel architectures problem solving. Our aim is to furnish an overview of the various bioinspired techniques based tools that have been used so far to solve the problems of automatic hardware design. We can claim that a lot of the approaches found in the literature suffer from a lack of interdisciplinary interaction among researchers of both evolutionary computation and hardware design fields. In addition, we have also detected that some multi-objective problems do not use the appropriate algorithms. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： parallel architectures bioinspired algorithms hardware design

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A Hybrid bioinspired Algorithm for Facial Emotion Recognition Using CSO-GA-PSO-SVM 5

A Hybrid Bioinspired Algorithm for Facial Emotion Recognitio...

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5th International Conference on Communication Systems and Network Technologies (CSNT)

作者： Vivek, T. V. Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9781479917976

Human-Computer Interaction gets more natural when the machine can detect human emotions faster and accurate. A lot of research is being carried out in the field of affective computing in order to improve the accuracy with speed. Bio-inspired algorithms for feature extraction and classification stages, has improved accuracy and speed further. In this paper, we propose a hybrid algorithm using CSO (Cat Swarm Optimization) with PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) for emotion recognition (ER). This bioinspired algorithm in conjunction with the support vector machine (SVM) will find an optimal feature set from a bigger set. Results from CK+ (Cohn Kanade) [1] dataset demonstrate that our proposed method using CSO-GA-PSOSVM outperforms Emotion Recognition System with CSOSVM by 10.5% in accuracy. This paper also proposes a new E-Learning [2] system to demonstrate its effectiveness and efficiency in real-time scenario. The proposed algorithm is applied over the facial characteristics captured from students in teaching-learning environment. The optimized feature vector obtained is passed to the SVM classifier for classification. Experimental results yield 99% classification accuracy in a person dependent mode with six basic emotions namely Happy, Sad, Anger, Disgust, Surprise and Neutral.

关键词： Emotion Recognition bioinspired algorithms Cat Swarm Optimization Genetic Algorithm Particle Swarm Optimization Support Vector Machines Hybrid bioinspired algorithms E-Learning

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection bioinspired algorithms Manta ray foraging optimizer

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Multiobjective artificial fish swarm algorithm for multiple sequence alignment

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INFOR 2020年第1期58卷 38-59页

作者： Dabba, Ali Tari, Abdelkamel Zouache, Djaafar Abderrahmane Mira Univ Fac Sci Comp Sci Dept Bejaia Algeria Mohamed Elbachir Elibrahimi Univ Comp Sci Dept Bordj Bou Arreridj Algeria

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with singleobjective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

关键词： Multiple sequence alignment artificial fish swarm algorithm bioinspired algorithms bioinformatics optimization algorithms evolutionary algorithm molecular biology swarm intelligence

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Fast and accurate synthesis of electronically reconfigurable annular ring monopole antennas using particle swarm optimisation and artificial bee colony algorithms

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第4期10卷 362-369页

作者： Brito Rodrigues, Eduardo Jorge Castro Lins, Hertz Wilton D'Assuncao, Adaildo Gomes Univ Fed Rio Grande do Norte UFRN Dept Commun Engn Caixa Postal 1655 BR-59072970 Natal RN Brazil Anatel Natl Agcy Telecommun BR-58043010 Joao Pessoa Paraiba Brazil

The synthesis of a simple electronically reconfigurable annular ring monopole antenna using a designing optimisation process based on particle swarm optimisation (PSO) and artificial bee colony (ABC) algorithms is proposed. Several antenna dimensions are selected as objective functions of PSO and ABC and their best solutions are considered as the optimised dimensions of the antenna geometry. Two radio-frequency p-i-n diodes, connecting the antenna feeding line to two microstrip stubs are used to change the antenna frequency response from ultra-wideband (UWB), from 3.1 to 10.6 GHz, to narrowband (NB) operation about 5.8 GHz. The antenna design and simulation are performed using Ansoft high-frequency structure simulator software. PSO and ABC algorithms are written in Java language. Thereafter, antenna prototypes are fabricated and measured for validation purposes. Simulation and measurement results are obtained showing good agreement. The measured optimised impedance bandwidths of the UWB and NB bands are up to 128 and 23%, respectively. Additionally, simulated and measured radiation patterns are very similar when the reconfigurable antenna is operating in OFF-state (UWB sensing antenna) and in ON-state (NB transmitting antenna) modes, indicating the proposed antenna geometry as a promising candidate for applications such as UWB and cognitive radio.

关键词： monopole antennas particle swarm optimisation ultra wideband antennas frequency response Java antenna radiation patterns electrical engineering computing electronically reconfigurable annular ring monopole antenna synthesis particle swarm optimisation artificial bee colony bioinspired algorithms antenna dimensions objective functions antenna geometry radiofrequency p-i-n diodes antenna feeding line microstrip stubs frequency response ultrawideband antenna antenna design PSO algorithms ABC algorithms Java language ANSOFT high-frequency structure simulator software antenna prototypes validation purposes impedance bandwidths antenna radiation patterns OFF-state modes ON-state modes cognitive radio NB transmitting antenna UWb sensing antenna

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Hybridization of fuzzy systems for modeling and control

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REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL 2014年第2期11卷 127-141页

作者： Manuel Andujar, Jose Javier Barragan, Antonio Univ Huelva Dep Ing Elect Sistemas Elect & Automat ETSI Palos De La Frontera 21819 Huelva Spain

Fuzzy logic has revolutionized, in a short period of time, the technology through a combination of mathematical fundamentals, logic and reasoning Its inherent hybridization ability and intrinsic robustness, have allowed to fuzzy logic get numerous successes in the field of modeling and control of systems, impulsing the intelligent control. In this paper, the more usual hybrid fuzzy systems and its importance in the field of modeling and control of dynamic systems are studied. The paper presents several examples that illustrate, for different hybridization techniques, how these enhance the innate qualities of fuzzy logic for modeling and control of dynamic systems. Also, more than a hundred and fifty references are included, which allow the interested reader to delve into the field of fuzzy logic, and more specifically, in its hybridization techniques with application to modeling and fuzzy control.

关键词： bioinspired algorithms fuzzy control fuzzy modeling fuzzy systems hybrid systems intelligent control neuronal networks

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Tangent search algorithm for solving optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第11期34卷 8853-8884页

作者： Layeb, Abdesslem Univ Constantine 2 Comp Sci & Its Applicat Dept LISIA Lab NTIC Fac El Khroub Algeria

This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses a mathematical model based on the tangent function to move a given solution toward a better solution. The tangent flight function has the advantage to balance between the exploitation and the exploration search. Moreover, a novel escape procedure is used to avoid to be trapped in local minima. Besides, an adaptive variable step-size is also integrated in this algorithm to enhance the convergence capacity. The performance of TSA is assessed in three classes of tests: classical tests, CEC benchmarks, and engineering optimization problems. Moreover, several studies and metrics have been used to observe the behavior of the proposed TSA. The experimental results show that TSA algorithm is capable to provide very promising and competitive results on most benchmark functions thanks to better balance between exploration and exploitation of the search space. The main characteristics of this new optimization algorithm is its simplicity and efficiency and it requires only a small number of user-defined parameters.

关键词： Optimization Metaheuristics bioinspired algorithms Constrained optimization Unconstrained optimization

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An adaptive flocking algorithm for performing approximate clustering

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INFORMATION SCIENCES 2009年第18期179卷 3059-3078页

作者： Folino, Gianluigi Forestiero, Agostino Spezzano, Giandomenico Natl Res Council CNR Inst High Performance Comp & Networking ICAR I-87036 Arcavacata Di Rende Italy

This paper presents an approach based on an adaptive bio-inspired method to make state of the art clustering algorithms scalable and to provide them with an any-time behavior. The method is based on the biology-inspired paradigm of a flock of birds, i.e. a population of simple agents interacting locally with each other and with the environment. The flocking algorithm provides a model of decentralized adaptive organization useful to solve complex optimization, classification and distributed control problems. This approach avoids the sequential search of canonical clustering algorithms and permits a scalable implementation. The method is applied to design two novel clustering algorithms based on the main principles of two popular clustering algorithms: DBSCAN and SNN. This apporach can identify clusters of widely varying shapes and densities and is able to extract an approximate view of the clusters whenever it is required. Both the algorithms have been evaluated on synthetic and real world data sets and the impact of the flocking strategy on performance has been evaluated. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Swarm intelligence Spatial clustering bioinspired algorithms

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Modeling and designing self-organized aggregation in a swarm of miniature robots

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2011年第5期30卷 615-626页

作者： Correll, Nikolaus Martinoli, Alcherio Univ Colorado Dept Comp Sci Boulder CO 80309 USA Ecole Polytech Fed Lausanne Distributed Intelligent Syst & Algorithms Lab Sch Architecture Civil & Environm Engn CH-1015 Lausanne Switzerland

We model the dynamics of self-organized robot aggregation inspired by a study on the aggregation of gregarious arthropods. In swarms of German cockroaches, aggregation into clusters emerges solely from local interactions between the individuals, whereas the probabilities of joining or leaving a cluster are a function of the cluster size. We propose a non-spatial population dynamics model that keeps track of the number of robots in clusters of specific size using control parameters of the individual robots and the probability of detecting another robot in the environment. The model is able to quantitatively and qualitatively predict the dynamics observed in extensive realistic multi-robot simulation, and provides qualitative agreement with data obtained from aggregation of Blattela germanica larvae. In particular, we show by analysis, numerical and realistic simulation that the emergence of a single aggregate requires a minimal communication range between individuals.

关键词： Aggregation self-assembly bioinspired algorithms modeling of swarm systems

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