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Reverse engineering of Mobile Banking Applications

Computer Systems Science & engineering 2021年第9期38卷 265-278页

作者： Syeda Warda Asher Sadeeq Jan George Tsaramirsis Fazal Qudus Khan Abdullah Khalil Muhammad Obaidullah National Center for Cyber Security Department of Computer Science&Information TechnologyUniversity of Engineering&TechnologyPeshawar25120Pakistan Higher Colleges of Technology Abu Dhabi Women’s CollegeAbu DhabiUAE Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Software Engineering University of Engineering&TechnologyMardan23200Pakistan

software reverse engineering is the process of analyzing a software system to extract the design and implementation *** engineering provides the source code of an application,the insight view of the architecture and the third-party *** a security perspective,it is mostly used for finding vulnerabilities and attacking or cracking an *** process is carried out either by obtaining the code in plaintext or reading it through the binaries or ***,reverse engineering is widely used for mobile applications and is considered a security *** Open Web Application Security Project(OWASP),a leading security research forum,has included reverse engineering in its top 10 list of mobile application *** applications are used in many sectors,e.g.,banking,education,*** particular,the banking applications are critical in terms of security as they are used for financial transactions.A security breach of such applications can result in huge financial losses for the customers as well as the *** exist various tools for reverse engineering of mobile applications,however,they have deficiencies,e.g.,complex configurations,lack of detailed analysis *** this research work,we perform an analysis of the available tools for reverse engineering of mobile *** dataset consists of the mobile banking applications of the banks providing services in *** results indicate that none of the existing tools can carry out the complete reverse engineering process as a standalone *** addition,we observe significant differences in terms of the execution time and the number of files generated by each tool for the same file.

关键词： Reverse engineering mobile banking applications security analysis

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

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Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

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An improved AI-driven Data Analytics model for Modern Healthcare Environment

An improved AI-driven Data Analytics model for Modern Health...

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International Conference on computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Prashamsh Takkalapally Balaram Yadav Kasula J. Logeshwaran Senior Software Engineer USA Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years.

关键词： Accuracy Hospitals Face recognition Medical services Organizations Manuals Real-time systems Data models Artificial intelligence Information analysis

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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

引用

Measurement: Sensors 2023年 29卷

作者： Nithyashri, J. Poluru, Ravi Kumar Balakrishnan, S. Ashok Kumar, M. Prabu, P. Nandhini, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpet District Kattankulathur603203 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria Bengaluru560029 India Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. © 2023 The Authors

关键词： Internet of things

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A Multiobjective Particle Swarm Optimizer based Localized Feature Selection for Imbalanced Fault Diagnosis

A Multiobjective Particle Swarm Optimizer based Localized Fe...

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Congress on Evolutionary Computation

作者： Lin Gao Yu Zhou Hainan Guo Sam Tak Wu Kwong College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China College of Management Shenzhen University Shenzhen Guangdong China Department of Computing and Decision Sciences Lingnan University Hong Kong China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Modern fault diagnosis faces an imbalance issue at both the sample and feature levels. A common approach is to combine balancing strategies with feature selection algorithms to address this problem. However, this may result in feature subsets that cannot accurately represent the true data distribution. This paper proposes an imbalanced fault diagnosis method called RPLFS-MOBPSO, which combines region purity (RP) with local feature selection to achieve class balance implicitly by partitioning local regions. RP is used as a new objective in the optimization process to achieve accurate fault detection. The adaptive reference point method from Non-Dominated Sorting Genetic Algorithm III is employed to maintain a diverse and evenly distributed external archive. On both the simulated Tennessee Eastma (TE) benchmark datasets and the real bearing experimental bench datasets, the proposed method achieves the best results compared to two LFS methods and four imbalanced ensemble algorithms based on different balancing strategies. Our source codes are available at: https://***/EMRGSZU/papers-code/tree/main/RPLFS-MOBPSO.

关键词： Fault diagnosis Accuracy Source coding Fault detection Feature extraction Partitioning algorithms Particle swarm optimization

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Hydrogen Storage Prediction in Dibenzyltoluene Integrated with Supervised Machine Learning

SSRN

引用

SSRN 2023年

作者： Ali, Ahsan Khan, Muhammad Adnan Choi, Hoimyung Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Riphah School of Computing & Innovation Faculty of Computing Riphah International University Lahore Campus Lahore54000 Pakistan Department of Software Faculty of Artificial Intelligence and Software Gachon University Seongnam13120 Korea Republic of School of Computing Skyline University College University City Sharjah Sharjah1797 United Arab Emirates

Liquid Organic Hydrogen Carriers (LOHCs) such as dibenzyltoluene (H0-DBT) offer a potentially safe and convenient method for hydrogen storage. DBT is less flammable and can store hydrogen in a liquid form at high densities. However, it is imperative to develop machine learning models to accurately predict the hydrogen storage capacity of H0-DBT under various experimental conditions. In this study, the hydrogen storage data is divided into three classes i.e. low class, medium class, and high class based on the hydrogen storage capacity values. Therefore, the Hydrogen Storage Prediction empowered with Support Vector Machine (HSP-SVM) model is proposed. The proposed HSP-SVM model predicts hydrogen storage classes. The effectiveness of the proposed HSP-SVM model was evaluated using various techniques including 5-Fold Cross Validation (5-FCV), Resubstitution Validation (RV), and Holdout Validation (HV). The HV approach has shown high accuracy in predicting hydrogen storage, with a rate of 98.50% for the low class, 97.00% for the medium class, and 98.50% for the high class. The overall accuracy of the HV approach was 97.00% with a miss classification rate of 3.00% whereas the accuracy and miss classification rate were 93.90% and 6.10%, respectively for the other two approaches. These results demonstrated the effectiveness of the HV approach for the proposed HSP-SVM model in predicting hydrogen storage classes in DBT using LOHC techniques. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Autonomous Vehicles: Open-Source Technologies, Considerations, and Development

arXiv

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arXiv 2022年

作者： Saoudi, Oussama Singh, Ishwar Mahyar, Hamidreza The Department of Computing and Software McMaster University Canada W Booth School of Engineering Practice and Technology Faculty of Engineering McMaster University Canada

Autonomous vehicles are the culmination of advances in many areas such as sensor technologies, artificial intelligence (AI), networking, and more. This paper will introduce the reader to the technologies that build autonomous vehicles. It will focus on open-source tools and libraries for autonomous vehicle development, making it cheaper and easier for developers and researchers to participate in the field. The topics covered are as follows. First, we will discuss the sensors used in autonomous vehicles and summarize their performance in different environments, costs, and unique features. Then we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for each modality. Third, we will review popular open-source driving simulators, a cost-effective way to train machine learning models and test vehicle software performance. We will then highlight embedded operating systems and the security and development considerations when choosing one. After that, we will discuss Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are areas that fuse networking technologies with autonomous vehicles to extend their functionality. We will then review the five levels of vehicle automation, commercial and open-source Advanced Driving Assistance Systems, and their features. Finally, we will touch on the major manufacturing and software companies involved in the field, their investments, and their partnerships. These topics will give the reader an understanding of the industry, its technologies, active research, and the tools available for developers to build autonomous vehicles. Copyright © 2022, The Authors. All rights reserved.

关键词： software testing

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Residential Electricity Power Forecasting using GRU-based CNN and SAE Ensembler

Residential Electricity Power Forecasting using GRU-based CN...

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Emerging Trends in engineering, Sciences and Technology (ICES&T), IEEE International Conference on

作者： Ali Raza Usman Ali Zain Ali Muhammad Hamza Saeed Raja Jalees Ul Hussen Khan Nasir Ayub Department of Computer Science COMSATS University Islamabad WahCantt Campus pakistan Computing Department Riphah International University Faisalabad Pakistan Department of Electrical Engineering HITEC University Taxila Pakistan Department of Computer Science National Textile University Faisalabad Pakistan Department of Software Engineering Superior University Gold Campus Lahore Pakistan Department of Software Engineering Capital University of Science and Technology Islamabad Pakistan

Smart grids play an important role to resolves issues related to electricity. Electrical load forecasting can be performed through smart grids to acquire knowledge about the electrical load that will be needed in the future. A combination of Stacked Autoencoding (SAE) and Gated Recurrent Unit (GRU) and a combination of Convolutional Neural Network (CNN), Grey Wolf Optimization (GWO), and GRU are proposed in this paper. Firstly, the historical load data is preprocessed and then preprocessed data is given to the feature selection and extraction methods to measure the importance of each feature. Features that have highest values are selected and input to the proposed classifiers. According to the results the proposed CNN-GRU-GWO gave the highest accuracy rate of 96.31%. This application of this article will help the utility purchase the exact amount of electricity from the grid, which will lead to grid stability.

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A Novel Contrastive Clustering Algorithm Based on K-means

A Novel Contrastive Clustering Algorithm Based on K-means

引用

Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Yu Yun Geng Yang Qin Li Jane You School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Benefitting from the simplicity and ease of implementation, K-means algorithm and its variants have been extensively studied and applied. However, the similarity measure manner limits the performance of previous K-means methods. Specifically, existing methods only take the similarity between the data point and itself cluster centroid into account while failing to consider the relationship with other centroids. In this study, we propose a method called Contrastive K-means (CKm). For training, to learn satisfactory cluster centroids of input data points for clustering, CKm simultaneously considers the similarity/dissimilarity between positive/negative pairs when optimizing the cluster centroids of data. Experimental results on seven prevalent datasets substantiate that CKm surpasses other K-means and variants in performance.

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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