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Detecting Android Malware with Convolutional Neural Networks and Hilbert Space-Filling Curves

SN computer Science 2024年第7期5卷 810页

作者： Mbungang, Benedict Ngaibe Ali Wacka, Joan Beri Tchakounte, Franklin Polatidis, Nikolaos Nlong II, Jean Michel Tieudjo, Daniel Department of Mathematics and Computer Science University of Ngaoundere Ngaoundere Cameroon Cybersecurity with Computational and Artificial Intelligence Research Group University of Ngaoundere Ngaoundere Cameroon Department of Computer Science University of Buea Buea Cameroon School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

computer vision techniques have advanced greatly in recent years through deep learning, achieving unprecedented performance. This has motivated applying deep learning to malware detection through image-based approaches to circumvent extensive feature engineering for diverse threats. However, existing work converting Android binaries to rectangular images neglects the intrinsic byte sequence structure, introducing spurious spatial relationships that weaken detection accuracy. To address this, space-filling curves have mapped binaries to images while preserving ordering. This paper proposes a novel method using Hilbert space-filling curves to visualize and classify Android apps. Bytecode is extracted from Dalvik Executable (DEX) files and transformed to grayscale images via Hilbert coding for model training. Additionally, a novel and balanced image dataset is proposed consisting of Hilbert transformations for 4995 benign and 4995 malicious Android apps randomly sampled from the AndroZoo repository. Experiments using this dataset evaluated pre-trained InceptionV3, VGG16, ResNet50 and EfficientNetB0 via transfer learning. A custom Convolutional Neural Network (CNN) was also trained from scratch. InceptionV3 achieved the highest performance at 97.99% accuracy, 98.50% precision, 97.50% recall and 97.99% F1-score. Comparative assessment with previous image-based malware detection research indicates our approach outperforms state-of-the-art approaches. By leveraging Hilbert space-filling curves to map binaries to images while preserving sequential relationships, detection accuracy is improved over methods introducing extraneous spatial representations. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Android Hilbert space-filling curve Image Malware detection

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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Managing aging bridges under seismic hazards through deep reinforcement learning 12th

Managing aging bridges under seismic hazards through deep re...

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12th International Conference on Bridge Maintenance, Safety and Management, IABMAS 2024

作者： Metwally, Z. Andriotis, C.P. Molaioni, F. Faculty of Architecture and the Build Environment Delft University of Technology Delft Netherlands Department of Civil Engineering and Computer Science Engineering University of Rome Tor Vergata Rome Italy

ISBN: (纸本)9781032770406

Structural systems must satisfy multiple performance and functionality requirements during their life cycle, withstanding safety-reducing degradation mechanisms and hazards. Intervention strategies must be planned accordingly to maintain structural integrity and minimize total life-cycle costs and risks, posing a complex optimization problem. Recent advances in multi-agent deep reinforcement learning (DRL) in conjunction with partially observable Markov Decision Processes (POMDPs) have shown great potential for determining optimal structural integrity management policies for systems with large state and action spaces compared to traditional decision practices. This paper tackles the maintenance optimization problem of aging bridges in seismic-prone areas, creating an updatable environment that embeds chloride-induced corrosion and state-dependent seismic fragility throughout the bridge life-cycle. The evolution of the environment is captured by a dynamic Bayesian network, and it is further integrated with decentralized multi-agent DRL algorithms to identify near-optimal lifecycle decisions under risk constraints. Results on a multi-component bridge system show the suitability of the developed framework for minimizing expected life-cycle costs, and for providing detailed and adaptive policies that significantly outperform traditional condition- and time-based maintenance plans. © 2024 The Author(s).

关键词： Life cycle

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Boosting Solar Energy Production of a Building using New Architectural Design

Boosting Solar Energy Production of a Building using New Arc...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Khalvati, Ladan Sheykhshabani, Zahra Ebrahimi Balal, Afshin Arbabzadeh, Negin Texas Tech University Department of Design Lubbock United States Pars Institute of Art and Architecture Architecture Department Tehran Iran Texas Tech University Department of Electrical and Computer Engineering Lubbock United States College of Architecture Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798350323917

Fossil fuels and air pollution are significant problems nowadays, causing many countries to increase their investments in renewable energy development. As the world's energy demand grows, governments are looking into and developing alternative energy sources like solar energy. Solar energy is one of the most promising solutions to address the above problem. In addition, the development of sustainable design in architecture has become a crucial factor in the construction industry as the world faces the challenges of climate change and diminishing fossil fuel reserves. Architects are now seeking innovative ways to incorporate sustainable and renewable energy sources into their designs. Solar energy, as a sustainable and clean energy source, has gained significant attention in recent years. It offers an opportunity to reduce the dependence on non-renewable energy sources and lower carbon emissions. Additionally, the use of solar technology in architectural designs can enhance the aesthetic appeal of buildings while providing energy efficiency. Building-integrated photovoltaic systems have emerged as a popular solution that allows for the integration of photovoltaic cells into the structure of buildings. In this paper, a new architectural design for a building in Tehran is proposed, which leads to the increase of generated solar energy. Needless to say, the designs must be beautiful to look at, functional, and good for the environment. Grasshopper and rhino are utilized to demonstrate the design. In addition, System Advisor Model (SAM) is implemented to study the economic analysis of the solar photovoltaic system planted on the building. The simulation result indicates that with the new design, the power production is increased from 21437 kWh to 69484 kWh. In addition, by using the proposed system in comparison with fossil fuel power plants, a huge amount of air pollution is will be removed from the air. © 2023 IEEE.

关键词： Solar energy

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AGV indoor localization: a high fidelity positioning and map building solution based on drawstring displacement sensors

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2277-2293页

作者： Wang, Shih-Yuan Li, Che-Ming Liong, Sze-Teng Sheng, Yu-Ting Huang, Yen-Chang Liong, Gen-Bing Gan, Yee-Siang Graduate Institute of Architecture National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronic Engineering Feng Chia University Taichung40724 Taiwan School of Architecture Feng Chia University Taichung40724 Taiwan Department of Applied Mathematics National University of Tainan Tainan Taiwan Faculty of Computer Science & amp Information Technology University of Malaya Kuala Lumpur50603 Malaysia

The rapid evolution of artificial intelligence (AI) and robotics technologies are bringing drastic changes to society and industry in these recent years. The impressive progress in facilitating smart manufacturing in this era of Industry 4.0 has made our lives much more convenient than ever before. Despite the greater reliability and stability of the robotic system, there are several challenges to overcome such as the restrictions of scenes, obstacles, and hardware specifications. Since a high precision positioning algorithm is of paramount importance in devising a mobile robot, the research in developing simultaneous localization and mapping has been garnered immerse attention especially from domains of the computer vision and autonomous robots. In this paper, a novel method is presented to significantly enhance the positioning precision of indoor unmanned guided vehicles. The approach involves several steps, including setting up hardware configurations and collecting relevant data by installing necessary devices and system packages within the robot operating system (ROS). Trilateration is employed to determine the relative position of the mobile robot using distance measurements. Coordinate transformation is then conducted to convert the collected input data of relative distances and orientations. Trajectory paths are obtained, and occupancy maps are constructed to estimate the resulting trajectory and generate a 2D grid map. Indoor localization and mapping are achieved using three drawstring displacement sensors along with orientation information from an Inertial Measurement Unit (IMU). The proposed method is extensively evaluated through experimentation on predefined navigation paths, and its performance is compared to state-of-the-art methods such as RealSense T265, Hector SLAM, and wheel odometry. The results show that the proposed method exhibits compelling performance in both mean error and occupancy map construction. Ultimately, the findings reported herein

关键词： Mobile robots

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Effect of Environment Size on Spatial Perception in Virtual Reality 6

Effect of Environment Size on Spatial Perception in Virtual ...

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6th International Conference on computer-Human Interaction Research and Applications, CHIRA 2022

作者： Bach, Morten Trier Jensen, Poul Anker Lucaci, Andrei Iuliu Pupczyk, Damian Grzegorz Madsen, Claus Brøndgaard Computer Graphics Group Department of Architecture and Media Technology Aalborg University Aalborg Denmark

ISBN: (纸本)9789897586095

A trend of distance underestimations in Virtual Reality (VR) is well documented, but the reason still remains unclear. Therefore, this paper investigates the effect of differently sized Virtual Environments (VEs) on egocentric distance perception in VR as a potential influence. Verbal assessment, blind walking, and our own proposed method: walk and assess, were compared in an experiment, and blind walking was found to be the most accurate. A virtual replica of a real-life location was created as a transitional environment, while small (15m2), medium (35m2) and large (95m2) rooms were created to investigate the effect of VE size on spatial perception in VR. To establish the differences in estimations between the real world and VR when using blind walking, a study was conducted with the virtual replica and its real life counterpart at distances between 1 and 10 meters. Following this, the three distinct room sizes were used in an experiment to investigate the effect of the size of rooms on spatial perception in VR. The findings showed consistent underestimates of distances, and a trend for underestimation to grow as the distance grows was observed. Similarly, underestimates grew with the size of the environment. Copyright © 2022 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Virtual reality

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Evolvable Agents, a Fine Grained Approach for Distributed Evolutionary Computing: Walking towards the Peer-to-Peer Computing Frontiers

arXiv

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arXiv 2024年

作者： Laredo, Juan Luis Jiménez Castillo, Pedro A. Mora, Antonio M. Merelo, Juan Julián Department of Architecture and Computer Technology ETSIIT University of Granada Spain

In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system;such a feature is crucial concerning large-scale parallel architectures such as Peer-to-Peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: First, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study. © 2024, CC BY.

关键词： Scalability

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Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 205-234页

作者： Xin Liu Mingyu Yan Lei Deng Guoqi Li Xiaochun Ye Dongrui Fan State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086 School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049 IEEE Department of Precision Instrument Center for Brain Inspired Computing ResearchTsinghua UniversityBeijing 100084China

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.

关键词： Efficient training graph convolutional networks(GCNs) graph neural networks(GNNs) sampling method

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Exploring the Use of WebAssembly in HPC 23

Exploring the Use of WebAssembly in HPC

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian and Institute of Information Technology Kottayam Kerala India

ISBN: (纸本)9798400700156

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. © 2023 ACM.

关键词： Containers

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Yogapriya, G. Kumari, R. Shalini Suganthi, S. Agalya, K. Elangovan, T. Pandian, D. Selva School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai India Department of Architecture Periyar Maniammai Institute of Science and Technology Tamilnadu Thanjavuri India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India

ISBN: (纸本)9798350334357

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction. © 2023 IEEE.

关键词： Convolutional neural networks

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