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Advanced memory forensics for malware classification with deep learning algorithms

Cluster Computing 2025年第6期28卷 1-15页

作者： Odeh, Ammar Taleb, Anas Abu Alhajahjeh, Tareq Navarro, Francisco Department of Computer Science Princess Sumaya University of Technology Amman Jordan Faculty of Computing Engineering and Media De Montfort University Leicester UK

The growing complexity of malware, especially polymorphic and obfuscated variants, has exposed significant limitations in traditional detection methods. This study addresses these challenges using memory forensics to detect and classify malware through deep learning algorithms. Memory-based features, including memory pages, threads, open files, user sessions, system calls, and kernel modules, were extracted from memory dumps using the Volatility and Rekall frameworks. Three deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders—were applied to analyze the extracted features. The dataset was divided into ten subsets using 10-fold cross-validation to ensure robustness and prevent overfitting. The models’ performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNNs and RNNs consistently outperformed Autoencoders, with CNNs achieving the highest accuracy of 97.8%. These findings demonstrate the superior effectiveness of CNNs and RNNs in detecting malware using memory-based data. This research establishes deep learning algorithms, particularly CNNs and RNNs, as powerful tools for malware detection in cybersecurity. In conclusion, this study contributes to ongoing efforts to enhance malware detection systems by leveraging memory forensics and deep learning. Future work will explore additional feature extraction techniques and hybrid model architectures to improve detection accuracy further and reduce false positives.

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Improving the Efficiency of Smart Grid Scheduling: A Linear Regression Model with Meta Job Information

Improving the Efficiency of Smart Grid Scheduling: A Linear ...

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of electrical Engineering and information systems German Jordianian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In order to forecast the run time of the jobs that were submitted, this research provides two linear regression prediction models that include continuous and categorical factors. A continuous predictor is built using the number of CPUs, average CPU speed, and memory size, whereas a categorical predictor is built using the user ID, group ID, and executable ID. According to the findings, the prediction rates for categorical and continuous predictors are 61% and 1%, respectively. sixty times better than the earlier models, which used continuous variables as a foundational model to determine task complexity and weight. The effectiveness of the categorical predictor in enhancing a job scheduling problem is then evaluated by combining it with three suggested job scheduling strategies. The suggested algorithms incorporated metrics—predicted run time, waiting time, and resource requirement—to select the smallest jobs. According to the results, Algorithm 3 performs better than earlier models in both performance metrics. The variation in total execution time and average waiting time is 1.14 to 1.76 and 4.5, respectively compared to previous models, Additionally, Algorithms 1 and 2 demonstrating superior performance in every scenario.

关键词： Measurement Processor scheduling Computational modeling Linear regression Predictive models Prediction algorithms Smart grids

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Development of a Smart Shoe for Blind and Visual Impaired People

Development of a Smart Shoe for Blind and Visual Impaired Pe...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Aneeqa Ramzan Rafay Mustafa Yashfa Kamran Department of Electrical Engineering NUTECH Islamabad Pakistan Faculty of Computer Science and Engg. GIKI Topi Swabi Pakistan Department of Electrical and Computer Engineering IIUI Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

Providing adequate clinical and technical aid to blinds and visually impaired persons can be very challenging as it put financial strain on families due to the medical examination, treatment, surgical procedures and assistive devices expenses. The specialists to the mentioned individuals' ratio is low and exposure to ophthalmologists is limited. Either requirement of smartphone applications with need of internet connection or only direction oriented solutions but unable to avoid obstacles are the issues in existing market solutions. This paper proposes a novel technology of smart assistant aid for blind and visually impaired people (SSVIP) by developing a smart shoe prototype with sensors to detect obstacles, wet surfaces and ground vibrations, providing real-time feedback to the user. If any of these are detected, the controller activates the buzzer and vibration motor, providing both audible alerts based on the detected sensor signal in order to alarm the wearer and tactile feedback respectively. SSVIP is then tested on 20 iterations after configuring and calibrating sensors and getting the optimized 20 units for ultrasonic sensor, 650 threshold for water sensor. Output accuracies of 87%, 80% and 83% for ultrasonic sensor, water sensor and vibration sensor resnectively and resulting overall 83% accuracy was acheived.

关键词： Vibrations Visualization Accuracy Virtual assistants Tactile sensors Surgery Footwear

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Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the Teaching Profession 2

Leveraging Artificial Intelligence in Online Educational Pla...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Amankulovich, Khujakulov Sherzod Tokhirovich, Mannabov Javlon Bakhridinovich, Kadirov Tulkin Kuvandikovich, Taylakov Umid Ananth, Christo Kumar, T. Ananth Samarkand Regional National Center for Training Teachers in New Methods Uzbekistan Uzbekistan Samarkand Agroinnovations and Research University Uzbekistan Samarkand State University Uzbekistan&Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Chennai India Ifet College of Engineering Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350364699

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms. © 2024 IEEE.

关键词： Contrastive Learning

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Framework for IOT Based Real-Time Monitoring system of Rainfall Water Level for Flood Prediction Using LSTM Network 3

Framework for IOT Based Real-Time Monitoring System of Rainf...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： William, P. Oyebode, Oluwadare Joshua Ramu, Gandikota Lakhanpal, Sorabh Gupta, Keerat Kumar Al-Jawahry, Hassan M. Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Afe Babalola University Civil and Environmental Engineering Ekiti State Ado-Ekiti Nigeria Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad500043 India Lovely Professional University Phagwara India Graphic Era Deemed to Be University Department of Civil Engineering Uttarakhand Dehradun248002 India The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350322842

Recent floods have consistently resulted in human casualties in addition to harm to the environment and the economy. People are less likely to be aware of oncoming floods since there is not a reliable early warning system. A deep learning algorithm is suggested in this study to forecast groundwater levels. Even with inadequate data, the model can nevertheless successfully fulfil the prediction objective. In order to determine the degree of temporal dependence that exists between groundwater level and meteorological indicators, a hybrid model called CNN-LSTM-ML is developed. This model makes use of a network structure that is a mix of CNN and LSTM networks. Long Short-Term Memory (LSTM) networks are used by the rainfall forecasting model in order to provide predictions about future rainfall and water level values, which may lead to floods. In order to conduct experiments on the principal finding, historical data from two sites with rainfall and one location with streamflow were used. Additional information was acquired from two water level stations and one rainfall station in order to confirm the basic findings. With assessment errors for historical data MAE, RMSE, and MSE of 0.93, 1.7, and 3.025 correspondingly, the forecasting technique that used LSTM demonstrated great 'accuracy' of the outcome reaching more than 92% using IoT. According to these results, the system has the potential to be used as a cure that does not involve the construction of new structures in order to minimize the damage caused by urban floods. © 2023 IEEE.

关键词： Internet of things

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Preface to the Proceedings of the Doctoral Consortium AIxIA 2024

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CEUR Workshop Proceedings 2024年 3914卷

作者： Bacciu, Davide Donadello, Ivan University of Pisa Department of Computer Science Pisa Italy Free University of Bozen-Bolzano Faculty of Engineering Bolzano Italy

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A Trace-Selection Preprocessing Approach of Discovering Structurally Complete ICN-Process Models from Process Logs 3rd

A Trace-Selection Preprocessing Approach of Discovering Str...

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3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Pham, Dinh-Lam Batzaya, Tsagaantsooj Kim, Kyoung-Sook Chang, Jeong-Hyun Kim, Kwanghoon Pio CCSRI Kyonggi University Gyeonggi Suwon16227 Korea Republic of Department of Public Safety Bigdata Graduate School of Kyonggi University Gyeonggi Suwon16227 Korea Republic of Data Process Engineering Research Laboratory Faculty of AI Computer Science and Engineering Kyonggi University Gyeonggi Suwon16227 Korea Republic of

ISBN: (纸本)9783031809422

In business process life-cycle management and reengineering through process mining, it is crucial for the process mining system to discover structurally safe and complete business process models from process logs. However, most process mining systems typically suffer from discovering inaccurate and unstructured business process models due to various types of anomalous traces hidden in process logs. In this paper, we therefore propose a process log preprocessing approach that purifies a corresponding process log dataset by selecting a group of reasonable process log traces and applying to discover an essential business process model satisfying the structural completeness requirement of matched-pairing and proper-nesting properties. The theoretical basis of the proposed approach is the mathematical graph model of information control net model that is the typical representation of business process models. Finally, we practically demonstrate the conceptual excellence and functional correctness of the proposed preprocessing approach through experimental verifications using real business process enactment log datasets available in the 4TU Center for Research Data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reengineering

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Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole for 5G Base Station Antenna Application

Twin-Array Quasi FSS Reflector with Dual Band Cross-Dipole f...

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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Santos, Ian Jerome Q. Tangjitjetsada, Maleeya Rakluea, Paitoon Wongsin, Norakamon Rakluea, Chawalit Mahatthanajatuphat, Chatree Chudpooti, Nonchanutt Thianthong, Tawan Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Thanyaburi Pathum Thani 12110 Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Department of Electronics and Telecommunication Engineering Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Wongsawang Bangsue Bangkok10800 Thailand King Mongkut's University of Technology North Bnagkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Wongsawang Bangsue Bangkok10800 Thailand Siam Airport Ground Service Co. Ltd. Don Mueang Bangkok10210 Thailand

ISBN: (纸本)9798350344585

This article provides an explanation of the use of a Twin-Array Quasi Frequency Selective Surface (FSS) Reflector with Dual Band Cross-Dipole for antenna applications in 5G base stations. The base material employed in this study is FR4 sheet, which has a dielectric constant (ϵr) of 4.3. The antenna obtained has dimensions of 100x100 mm2 and functions throughout the frequency spectrum spanning from 730 to 860 MHz, 1700 to 2130 MHz. The primary benefits of this antenna design are its lightweight structural composition, compatibility with the frequency range of 730-860 MHz, and discernible performance attributes. The CST Studio Suite software was used to conduct a simulation of the antenna's performance. The antenna has an operating frequency range of 730-860 MHz and 1700-2130 MHz, enabling it to support a unidirectional wave propagation pattern that is suitable for the implementation of a 5G mobile communication base station. © 2023 IEEE.

关键词： Dipole antennas

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Large-scale Augmented Granger Causality (lsAGC) for discovery of causal brain connectivity networks in schizophrenia patients using functional MRI neuroimaging

Large-scale Augmented Granger Causality (lsAGC) for discover...

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Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Wismüller, Axel Vosoughi, Ali Kasturi, Akhil Hadjiyski, Nathan Department of Electrical and Computer Engineering University of Rochester NY United States Department of Imaging Sciences University of Rochester NY United States Department of Biomedical Engineering University of Rochester NY United States Faculty of Medicine and Institute of Clinical Radiology Ludwig Maximilian University Munich Germany Department of Computer Science University of Rochester NY United States

ISBN: (纸本)9781510660410

The literature suggests that schizophrenia is associated with alterations in brain network connectivity. We investigate whether large-scale Augmented Granger Causality (lsAGC) can capture such alterations using restingstate fMRI data. Our method utilizes dimension reduction combined with the augmentation of source time-series in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsAGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsAGC as features for classification. After feature extraction, we perform feature selection by Kendall's tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 30 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 82.89% ± 17.25% and mean Area Under the receiver operating characteristic Curve (AUC) values of 93.33% ± 12.81% across all tested numbers of features for lsAGC, which is significantly better than the results obtained with cross-correlation (AUC=78.33% ± 25.60%, f1-score=66.22% ± 24.73%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsAGC as a potential imaging biomarker for schizophrenia. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Time series

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Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

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Microsystems & Nanoengineering 2022年第5期8卷 183-196页

作者： Nasim Bakhshaee Babaroud Merlin Palmar Andrada Iulia Velea Chiara Coletti Sebastian Weingärtner Frans Vos Wouter A.Serdijn Sten Vollebregt Vasiliki Giagka Department of Microelectronics Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyMekelweg 4Delft 2628 CDThe Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection TechnologiesFraunhofer Institute for Reliability and Micro-integration IZMGustav-Meyer-Allee 25Berlin 13355Germany Department of Imaging Physics Faculty of Applied ScienceDelft University of TechnologyLorentzweg 1Delft 2628 CJThe Netherlands Erasmus University Medical Center(Erasmus MC) dr.Molewaterplein 40Rotterdam 3015 GDThe Netherlands

Multimodal platforms combining electrical neural recording and stimulation,optogenetics,optical imaging,and magnetic resonance(MRI)imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific *** conductive,optically transparent,and MRI-compatible electrodes can optimally combine all *** as a suitable electrode candidate material can be grown via chemical vapor deposition(CVD)processes and sandwiched between transparent biocompatible ***,due to the high graphene growth temperature(≥900℃)and the presence of polymers,fabrication is commonly based on a manual transfer process of pre-grown graphene sheets,which causes reliability *** this paper,we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural *** fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and *** also exhibit the highest charge storage capacity(CSC)reported to date among all previously fabricated CVD graphene *** graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse ***,we show here,for the first time,that CVD graphene electrodes do not cause any image artifact in a 3T MRI *** results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal *** fabricated graphene electrodes enable multimodal neural recording,electrical and optogenetic stimulation,while allowing for optical imaging,as well as,artifact-free MRI studies.

关键词： transparent transfer process

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