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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Kurniawan, Fandi Stiawan, Deris Antoni, Darius Heriyanto, Ahmad Idris, Mohd. Yazid Budiarto, Rahmad Universitas Sriwijaya Faculty of Engineering Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Indonesia Universitas Teknologi Malaysia Faculty of Computer Science & Information Ssystem Malaysia College of Computer Science and IT Universitas Albaha Saudi Arabia

ISBN: (纸本)9798350355314

Improvements in information technology bring new challenges in cyber security, especially on the Android platform which is the main target of malware attacks. The National Cyber and Crypto Agency (BSSN) as the national cybersecurity institute recorded millions of attacks involving the Android Package Kit (.apk) application for electronic wedding invitations in Indonesia. This research aims to develop a hybrid machine learning model to detect and anticipate malware on Android devices, using algorithms such as Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbors (KNN). The main challenges are the limited number of representative datasets and the lack of effective detection techniquesThis research utilizes a primary server with a virtual machine (VM) to analyze the security of Android applications using mobSF and Frida, involving decompilation, TLS testing, and sensitive feature extraction. The system also depends on an Android emulator and Android Debug Bridge (ADB) for simulation, along with PostgreSQL, Job Scheduler, and Remote Desktop Protocol (RDP) for data management and task scheduling. The dataset comprises 1314 malware samples and 770 benign samples, totaling 2048 Android applications, with an average analysis time of 35 minutes per application. Through data collection and pre-processing, as well as model training and evaluation, it is hoped that the proposed framework can improve the accuracy of malware detection, making a significant contribution in protecting Android users from ever-evolving cyber threats. After performing a comparison using the algorithms mentioned above, it was found that the Random Forest algorithm showed the best performance with an accuracy of 97.11%, a precision of 97.22%, and a recall of 97.11%. © 2024 IEEE.

关键词： Android malware

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Research on Human Activity Recognition Based on Random Forest Classifier

Research on Human Activity Recognition Based on Random Fores...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Feng, Zhiyong Shi, Yike Zhou, Duomu Mo, Liping Jishou University The School of Computer Science and Engineering Jishou416000 China Jishou University Faculty of Computer Science and Technology Computer Science and Engineering Jishou416000 China

ISBN: (纸本)9798350399028

The current machine learning algorithms classify human activities with inaccurate accuracy, poor generalization ability of the model, and poor classification effect. Proposing to use Random Forest classifier to classify the samples. The classifier has the advantage of good generalization ability, high accuracy, and the ability to handle a large number of sample data. After comparing with BP neural networks, Naive Bayesian networks, and decision trees, the random forest classifier achieved an accuracy of 98% on the test set, much higher than the 55%, 87%, and 88% of the other algorithms. In addition, we also tested the generalization ability of the model using the K-Folder cross-validation method, which yielded an average accuracy of 95.5%, also much higher than the 49.9%, 85%, and 90% of the other classifiers. The experimental results show that the random forest classifier has significantly improved in accuracy and generalization ability compared with BP neural network by 78% and 91%, respectively. Therefore, the superiority of the random forest classifier in human activity classification is proved. © 2023 IEEE.

关键词： Decision trees

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Negative Impact of Approximated Signed Addition on Power Reduction

Negative Impact of Approximated Signed Addition on Power Red...

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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Hama, Hiroyuki Ukezono, Tomoaki Sato, Toshinori Graduate School of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Fukuoka Japan Faculty of Engineering Fukuoka University Department of Electronics Engineering and Computer Science Fukuoka Japan

ISBN: (纸本)9798350315042

This paper presents evaluation results of a power- efficient JPEG compression circuit utilizing approximate computing. To achieve power efficiency, we replace summations in Discrete Cosine Transform (DCT) with approximate additions that follow Carry-Maskable Adder (CMA) [1]. We evaluate both the quality of the compressed images and the power consumed by the circuit. However, we observed unexpected results where power consumption increased despite the use of approximation, contradicting our expectations. Our detailed investigations revealed that the addition of negative values in 2's complement representation led to an increased wasted power consumption. Although reports on investigations related to power reduction through approximate computing offer valuable insights for researchers in this field, they are often difficult to find. This paper shows the reasons why power reduction could not be achieved in this specific case study. © 2023 IEEE.

关键词： Discrete cosine transforms

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Preliminary Study on Detection of Breasts 47

Preliminary Study on Detection of Breasts

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47th ICT and Electronics Convention, MIPRO 2024

作者： Pristavnik Vrešnjak, Matic Perušić, Ani Emeršič, Žiga Peer, Peter Batagelj, Borut University Of Ljubljana Faculty Of Computer And Information Science Ljubljana Slovenia University Of Rijeka Faculty Of Engineering Rijeka Croatia

ISBN: (纸本)9798350382495

In the realm of digital image manipulation, deep fakes, predominantly sourced from pornographic materials, present a significant challenge, especially prevalent in the form of face and body swapping techniques. This emerging issue involves substituting the faces or bodies of individuals in explicit content, using advanced methods like those demonstrated in the DeepNude application. In response, we present an approach which solves the first part of the pipeline - detection of breasts. There has been limited research regarding this biometric modality, with notable exceptions such as breast cancer identification. Due to the lack of research and the absence of open, freely available data, we developed our own dataset. Images with annotations were acquired from *** and curated by experts. Annotations include name, cup size, possible breast augmentations, ethnicity, among others. To demonstrate that dataset is challenging enough for future research we used images to train in class-agnostic way three CNN-based detection models. The results show the feasibility of not only the proposed detection approaches for the task, but also the dataset and hopefully pave the way for future applications such as supporting court decisions, enhancing virtual clothing fitting techniques, and more. © 2024 IEEE.

关键词： computer vision

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AI-Infused Predictive Digital Twin Systems for Resilient IoT Software Architectures 4

AI-Infused Predictive Digital Twin Systems for Resilient IoT...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Gupta, Raj Kumar Dhasmana, Gulshan Benvin, Shalet Bhargav, H.K. Jadhav, Girish Rao, Tarini Department of Physics Bhabua India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Bgs Institute of Technology Adichunchanagiri University Department of Computer Science and Engineering Mandya India Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Tumakuru India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electrical Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The proliferation of Internet of Things (IoT) networks has significantly increased the complexity of software architectures, leading to heightened vulnerabilities and system inefficiencies. AI-infused Predictive Digital Twin Systems provide a promising approach by integrating real-time data analytics with predictive AI models to monitor, forecast, and enhance the health and security of IoT software architectures. This paper introduces an AI-Infused Predictive Digital Twin System designed to create resilient IoT software architectures. Experimental validation was conducted using a large-scale IoT network with 1,000 interconnected devices. The AI-Infused Predictive Digital Twin System achieved a 97.8% accuracy in anomaly detection, with a 28% reduction in system disruptions compared to conventional monitoring solutions. The RL-based optimization module resulted in a 20% improvement in resource utilization efficiency and a 30% increase in overall system resilience under varying load conditions. The integration of the blockchain audit layer significantly improved traceability and reduced security breach recovery time by 40%. The AI-Infused Predictive Digital Twin System provides an innovative solution for developing resilient IoT software architectures. By integrating predictive AI models and real-time digital twin technology, the framework enables continuous monitoring, proactive anomaly detection, and dynamic optimization. The experimental results demonstrate the system's capability to enhance resilience, efficiency, and security in complex IoT environments, making it a valuable tool for managing and securing next-generation IoT architectures. Future work will explore expanding the system's capabilities to support multi-cloud IoT networks and developing more sophisticated AI models for enhanced predictive accuracy and operational efficiency. © 2024 IEEE.

关键词： Blockchain

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Relationship of IQ with Causal Connections Between Brain Salient Network Regions 31

Relationship of IQ with Causal Connections Between Brain Sal...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Ghoorchian, Mina Soltanian-Zadeh, Hamid Pedram, Mir Mohsen Sadeghi, Mohsen Mir Mohammad Institute for Cognitive Science Studies Department of Cognitive Science Tehran Iran University of Tehran School of Electrical and Computer Engineering Tehran Iran Kharazmi University Department of Electrical and Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798331529710

IQ is one of the indicators that has always been of interest to psychiatrists, doctors and cognitive science researchers. Since this index plays a key role in people's lives and also in the occurrence of brain abnormalities, many studies have been conducted on it. In this paper, we used the resting state magnetic resonance imaging data available in the HCP database to classify people into three groups with low, medium, and high IQ and applied machine learning methods on the matrix of causal connections between related network regions. We applied brain highlights. We calculated the matrix of causal relationships in this network by the spDCM algorithm and then obtained the differences of the three groups using the ANOVA statistical test and 6 edges out of 169 edges with p-value © 2024 IEEE.

关键词： Support vector regression

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Multi-View Human Activity Recognition in Ambient Assisted Living Using Lightweight Deep Learning Models 5

Multi-View Human Activity Recognition in Ambient Assisted Li...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Bari, Ahsanul Karim, Hezerul Abdul Farid, Fahmid Al Asaduzzaman, Mina Amirabdollahian, Farshid Mansor, Sarina Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Multimedia University Faculty of Information Science and Technology Melaka63100 Malaysia University of Hertfordshire Faculty of Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798331529437

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and precise activity detection. Utilizing the Robot House Multi-View Human Activity Recognition (RHM-HAR) dataset, which contains four different views-front, back, ceiling (omni), and mobile robot-our models effectively address challenges related to viewpoint variation and motion dynamics. The dataset includes 14 multi-view daily living action classes, providing a balanced set of synchronized human actions suitable for multi-domain neural network learning. MobileNet and CCNet are employed for their high recognition accuracy, computational efficiency, and real-time application capabilities in AAL scenarios. We propose a Mutual Information (MI)-based method to assess the redundancy and relevance of each viewpoint, ensuring the fusion of multi-view data with minimum redundancy and maximum relevance. Benchmarking results demonstrate that multi-view combinations significantly enhance recognition performance compared to single-view models, particularly in complex activities involving high levels of movement. © 2024 IEEE.

关键词： Assistive technology

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CRISP: Cloud resilient infrastructure for self-healing platforms in dynamic adaptation

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International Journal of Information Technology (Singapore) 2024年 1-14页

作者： Rawas, Soha Samala, Agariadne Dwinggo Fortuna, Aprilla Faculty of Science Department of Mathematics and Computer science Beirut Arab University Soha Beirut Lebanon Faculty of Engineering Universitas Negeri Padang West Sumatera Indonesia

As cloud computing evolves, traditional resilience strategies must adapt to new challenges. This paper introduces CRISP, Cloud Resilient Infrastructure for Self-Healing Platforms, a pioneering framework designed to enhance cloud resilience through autonomous fault management. By leveraging advanced machine learning, predictive analytics, and dynamic adaptation, CRISP Clouds provides a robust solution for real-time fault detection, diagnosis, and automated recovery. Our findings highlight CRISP Clouds’ ability to significantly improve system agility, minimize downtime, and optimize resource usage. This work presents a critical advancement in resilience engineering, setting the stage for future innovations in cloud technology. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous systems Cloud Computing Dynamic adaptation Fault Management Machine learning Predictive analytics Resilience enhancement Self-healing

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Clustering Data Log CITA Blockchain Performance Monitoring with Mean Shift Method

Clustering Data Log CITA Blockchain Performance Monitoring w...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Ubaya, Huda Stiawan, Deris Suprapto, Bhakti Yudho Perdana, Muhammad Iqbal Universitas Sriwijaya Faculty of Computer Science Dept. of Computer Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Dept. of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350307306

CITA is a high-performance enterprise blockchain system with six microservices (RPC, Auth, Consensus, Chain, Executor, and Network) that exchange information through Message Bus. There is a dataset in the form of performance logs from CITA in the format of CPU, Memory, Network, PReads, PWrite, and VMem taken in real-time using the Performance Monitoring Framework for Blockchain. This framework has the advantage of lower overhead, more details, and better scalability than other performance monitoring approaches, but there is no clustering feature in the monitoring results. Thus, it is necessary to include a clustering feature so that users know the performance directly without the need to analyze it more deeply. This research uses the Mean Shift algorithm, which does not require the number of clusters for the initial process but only determines the maximum distance of centroid displacement through calculation. The number of clusters estimated is 2 clusters, where CITA produces 7 cluster samples with cluster evaluation results with the highest average silhouette score from CITA, which is 0.555 in the Network-Received Network-Sent feature pair. So, it is known that the Mean Shift clustering algorithm produces goodness of fit results from clustering on the CITA Blockchain performance log, which means that the clusters are well separated and can be clearly distinguished. © 2023 IEEE.

关键词： Blockchain

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