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Gupacker: Generalized Unpacking Framework for Android Malware

IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Exploring blockchain solutions: Integrating cryptocurrency with IoT for enhanced security

Exploring blockchain solutions: Integrating cryptocurrency w...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shravan, M. Kakad, Shital Mulani, Umar M Sharma, Geeti Tiwari, Mohit Mary Shiba, C. Sr University School of Business Telangana Warangal India Vishwakarma Institute of Technology Artificial Intelligence and Data Science Maharashtra Pune India Kj College of Engineering and Management Research Pune India India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi Delhi India R.M.K Engineering College Department of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798331508685

Blockchain, initially developed as the underlying technology for Bitcoin, has garnered significant attention for its applications beyond cryptocurrency, particularly in complex non-monetary domains. Utilizing cryptographic methods like hashing and asymmetric encryption, combined with a distributed consensus model, Blockchain-based ledgers achieve high levels of security and immutability, effectively removing the need for third-party intermediaries. Meanwhile, the rapid growth in Internet of Things (IoT) devices being connected to networks raises substantial concerns around security and privacy. Addressing these security challenges is essential for the expanding IoT landscape. This paper explores the blockchain solution by integrating cryptocurrency with IoT for enhanced security, reviewing recent studies and applications to evaluate Blockchain's role in IoT security, pinpoint challenges, and propose Blockchain-driven solutions to improve security in the IoT ecosystem. © 2025 IEEE.

关键词： Blockchain Cryptocurrency Internet of Things privacy Security

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Transformer-Based Prognostic Modeling for Smart Grid Health Monitoring 71

Transformer-Based Prognostic Modeling for Smart Grid Health ...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Huynh, Phat K. Huynh, Phi Yadav, Om P. Le, Chau Pirim, Harun Le, Trung Q. Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 419 GreensboroNC27411 United States School of Industrial Engineering and Management International University - VNU-HCM Quarter 6 Linh Trung Ward Thu Duc City Ho Chi Minh City721400 Viet Nam Department of Civil Engineering Technology & Construction Management University of North Carolina at Charlotte 9201 University City Boulevard CharlotteNC28223 United States Department of Industrial and Manufacturing Engineering North Dakota State University 1315 Centennial Blvd FargoND58102 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 E. Fowler Avenue TampaFL33620 United States

ISBN: (纸本)9798350367744

Smart power grids, pivotal to modern infrastructure, face increased vulnerability due to their complex and interconnected nature, often leading to disruptions that can result in widespread power outages and significant economic losses. Traditional grid monitoring systems frequently fall short in both predicting and mitigating these disruptions, creating a critical need for more advanced predictive methods that can foresee and interpret the progression of these anomalies into distinct failure modes. This paper introduces a transformer-based prognostic model based on Phasor Measurement Unit (PMU) data to enhance the predictive monitoring of smart grids. The method capitalizes on the advanced capabilities of transformer models to manage high-dimensional time-series data with complex dependencies. The methodology, validated rigorously against historical data, progresses through three main phases: (1) PMU data acquisition and preprocessing, (2) PMU-based feature engineering, and (3) prognostic modeling. In the data acquisition and preprocessing phase, high-resolution PMU data were collected, encompassing parameters such as voltage and current magnitudes, frequency, and phase angles. Next, we extracted 43 distinct features across four temporal windows, which were essential in characterizing the dynamic behavior of the grid. The prognostic modeling phase employed a transformer architecture with a sophisticated self-attention mechanism that efficiently handles the sequential PMU data, identifying subtle patterns indicative of potential grid failures. The prognostic model had a superior performance in predicting different types of grid failures-severe weather, lightning, and failed AC circuit equipment, which achieved an accuracy of up to 84.07% with low variability using five-fold cross-validation. This prognostic model enhances the predictive capabilities of smart grid monitoring systems and offers a robust framework for proactive management of grid health, potentially red

关键词： Phasor measurement units

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Decision support system for port terminals: Design and development of a business intelligence tool

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Procedia Computer Science 2025年 253卷 2438-2447页

作者： Marco Gonçalves Leonor Teixeira Department of Economics Management Industrial Engineering and Tourism (DEGEIT) University of Aveiro Aveiro Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Intelligent Systems Associate Laboratory (LASI) Department of Economics Management Industrial Engineering and Tourism (DEGEIT) University of Aveiro Aveiro Portugal

The exponential growth in the volume of maritime cargo has presented significant challenges for ports, underscoring the urgent need for digitalized processes and more efficient solutions aligned with the Maritime 4.0 vision. However, there is a gap in research focusing on applying Business Intelligence (BI) tools to optimize port terminal operations. This article addresses this gap by developing a BI tool to characterize the flow of container delivery and pick-up services at a Portuguese port terminal, utilizing the CRISP-DM methodology. The tool, built using Microsoft Power BI, not only eases the extraction and correction of data inconsistencies but also presents key operational indicators through visually appealing dashboards. These dashboards enhance the understanding of both current and future operational states, leading to more informed and agile decision-making. On the terminal under study, the implementation of this BI tool revealed that scheduling is done with an average lead time of just 32 minutes in the current year, with 47% of services being scheduled in advance and only 21% adhering to their designated time slots. These results corroborate the challenges faced and demonstrate the critical role of data in monitoring operational performance. By addressing these challenges, this study not only enhances logistics flow analysis and reduces terminal congestion but also demonstrates the significant impact of BI tools on achieving Maritime 4.0 goals. The findings establish a solid foundation for future BI applications in the maritime sector, highlighting the transformative potential of digital solutions in optimizing seaport operations.

关键词： Business Intelligence Data Mining Performance Indicators Dashboard CRISP-DM Seaports Maritime 4.0

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Multi-Scale Temporal Analysis for Failure Prediction in Energy systems 71

Multi-Scale Temporal Analysis for Failure Prediction in Ener...

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71st Annual Reliability and Maintainability Symposium, RAMS 2025

作者： Le, Anh Huynh, Phat K. Yadav, Om P. Le, Chau Pirim, Harun Le, Trung Q. Department of Civil Engineering North Dakota State University 1340 Administration Ave FargoND58102 United States Department of Industrial and Systems Engineering North Carolina A&T State University 1601 E. Market Street 408 McNair Hall GreensboroNC27411 United States Department of Industrial and Systems Engineering North Carolina A&T State University 1121 E. Market Street 408 McNair Hall GreensboroNC27411 United States Department of Engineering Technology & Construction Management University of North Carolina at Charlotte 9201 University City Boulevard CharlotteNC28223 United States Department of Industrial and Manufacturing Engineering North Dakota State University 1315 Centennial Boulevard Offerdahl East Rm 106 FargoND58102 United States Department of Industrial & Management Systems Engineering University of South Florida 4202 East Fowler Ave TampaFL33620 United States

ISBN: (纸本)9798350367744

Many previous studies on power networks focus on identifying disturbances but they often miss how the system behaves across different time scales. Many existing models struggle to predict these systems’ complex, nonlinear behavior of these systems during extreme weather conditions. This study proposes a multi-scale system temporal analysis for failure prediction in energy systems, focusing on predicting failures in electric power networks using Phasor Measurement Unit (PMU) data. The model integrates multi-scale analysis with advanced machine learning techniques to capture both short-term and long-term behavior. By analyzing disturbances across multiple temporal scales, we aim to improve failure prediction accuracy in complex energy systems. One of the main challenges with PMU data is the lack of labeled states, despite the availability of disturbance times logged as failure records. This limitation makes distinguishing between normal, pre-disturbance, and post-disturbance conditions difficult. We apply multi-scale system dynamics modeling to address this issue and extract key predictive features from PMU data for failure prediction. First, multiple domain features were extracted from the PMU data from time series data. Second, a multi-scale windowing approach was applied, using window sizes of 30s, 60s, and 180s to capture both short-term and long-term system dynamics. This multi-scale analysis allowed the model to detect patterns across different time scales. Third, feature selection was performed using Recursive Feature Elimination (RFE) to reduce the dimensionality of the dataset and identify the most important features. We selected RFE as the best feature selection for its ability to systematically eliminate less important features, thus improving model interpretability and reducing complexity. This step was crucial for identifying the most relevant features for predicting disturbances. Fourth, these features were used to train multiple machine-learning models.

关键词： Phasor measurement units

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Optimization of Logistical Models for Rocket-Based Cargo Delivery

Optimization of Logistical Models for Rocket-Based Cargo Del...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Nguyen, Khanh C. Elkamel, Marwen Rabelo, Luis Kinzel, Michael P. Virginia Polytechnic Institute and State University BlacksburgVA24061 United States Industrial Engineering and Management Systems University of Central Florida OrlandoFL32816 United States Department of Aerospace Engineering Embry-Riddle Aeronautical University OrlandoFL32816 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The ongoing advancements in rocket technology have set the stage for a shift in global logistics, presenting the capability to deliver payloads globally at unprecedented speeds. This study addresses the critical challenges and operational bottlenecks arising from the integration of rocket-based systems into traditional logistics frameworks. Our research develops and applies an optimization model, utilizing discrete event simulation combined with advanced optimization techniques facilitated by the Python tool, SimPy, and optimized through OpenMDAO. The model targets key performance indicators such as logistics time, cost efficiency, and resource allocation to assess the impact of evolving conditions on the logistics network. Initial simulations focus on basic operational scenarios to refine the methodology, setting the groundwork for more complex applications. This paper outlines the conceptual framework, methodology, and anticipated impacts of implementing high-speed rocket technology in relief supply delivery, emphasizing the potential to dramatically reduce delivery times and enhance the responsiveness of global supply chains. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Rockets

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OPTIMAL MULTI-OBJECTIVE BEST ARM IDENTIFICATION WITH FIXED CONFIDENCE

arXiv

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arXiv 2025年

作者： Chen, Zhirui Karthik, P.N. Chee, Yeow Meng Tan, Vincent Y.F. Department of Electrical and Computer Engineering Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Artificial Intelligence Indian Institute of Technology Hyderabad India

We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an M-dimensional vector reward upon selection. We assume that the reward of each dimension (a.k.a. objective) is generated independently of the others. The best arm of any given objective is the arm with the largest component of mean corresponding to the objective. The end goal is to identify the best arm of every objective in the shortest (expected) time subject to an upper bound on the probability of error (i.e., fixed-confidence regime). We establish a problem-dependent lower bound on the limiting growth rate of the expected stopping time, in the limit of vanishing error probabilities. This lower bound, we show, is characterised by a max-min optimisation problem that is computationally expensive to solve at each time step. We propose an algorithm that uses the novel idea of surrogate proportions to sample the arms at each time step, eliminating the need to solve the max-min optimisation problem at each step. We demonstrate theoretically that our algorithm is asymptotically optimal. In addition, we provide extensive empirical studies to substantiate the efficiency of our algorithm. While existing works on pure exploration with multi-objective multi-armed bandits predominantly focus on Pareto frontier identification, our work fills the gap in the literature by conducting a formal investigation of the multi-objective best arm identification problem. Copyright © 2025, The Authors. All rights reserved.

关键词： Pareto principle

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Optimal coordination of Production, Logistics, and Maintenance 4.0: A framework for Multi-Site Stamping Plants

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Procedia Computer Science 2025年 253卷 2899-2908页

作者： Raoua Zerrad Kamar Diaz Meriem Hayani Mechkouri Naoufal Sefiani Department of Mechanical Engineering Research Laboratory in Engineering Innovation and Management of Industrial Systems Faculty of Sciences and Techniques Abdelmalek Essaâdi University Tangier Morocco Department of industrial Engineering and Maintenance Computer Engineering Production and Maintenance Laboratory University of Lorraine Metz France

This paper presents a comprehensive framework for optimal coordination of production, logistics, and maintenance in multi-site stamping plants within the context of Industry 4.0. By integrating key technologies such as the Internet of Things, cloud computing and big data analytics, we developed a system that would enable real-time data sharing, predictive analytics, and intelligent decision-making. This framework aims to enhance operational efficiency, reduce downtime, and improve service rate. Additionally, our approach aims to optimize spare parts inventory management across different sites, minimizing inventory costs and lead times by ensuring timely availability and efficient sourcing of spare parts.

关键词： Industry 4.0 Maintenance 4.0 Production 4.0 Logistics 4.0 Stamping machine Spare parts Internet of things Prognostics Health management

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Wheat Supply Chain Network Design: Lesson for Resilience and Sustainability in a Situation of War and Crisis

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Journal of the Knowledge Economy 2025年 1-45页

作者： Rahbari, Misagh Tavakkoli-Moghaddam, Reza Razavi Hajiagha, Seyed Hossein Jafari, Mohammad Javad Department of Industrial Engineering Faculty of Engineering Kharazmi University Tehran Iran School of Industrial Engineering College of Engineering University of Tehran Tehran Iran Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey Department of Management Faculty of Management and Finance Khatam University Tehran Iran Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

Global crises, such as the Russia–Ukraine war and the COVID-19 pandemic, have caused disruptions to the agri-food supply chains. Resilience strategies can be used to address these disruptions. Therefore, this study aims to analyze the design of a bi-objective resilient and sustainable wheat supply chain problem, considering disruptions caused by the Russia-Ukraine war. Resilience strategies for inventory prepositioning and backup suppliers were employed to deal with these disruptions. Also, a carbon cap-and-trade mechanism was used to reduce carbon emissions in the wheat supply chain. The Minimax Relative Regret Scenario-based Stochastic Programming (MRRSSP) and p-Robust Scenario-based Stochastic Programming (PRSSP) were simultaneously used to control the uncertainty stemming from these disruptions. Furthermore, we investigated the possibility of the simultaneous occurrence of multiple global crises and their impacts on demand parameters in the wheat supply chain. Sensitivity analysis revealed that in the case study, a simultaneous decrease in the foreign retailers’ demand and an increase in the domestic retailers’ demand resulted in the location and resilience strategy costs, accounting for more than 90% of the total costs. Moreover, using the Robust Scenario-based Stochastic Programming (RSSP) approach provided a 10% advantage over the nominal approach. If the RSSP is not employed, the cost due to lack of access to perfect information would be increased by 27%.

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An Interior-Point Algorithm for Continuous Nonlinearly Constrained Optimization with Noisy Function and Derivative Evaluations

arXiv

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arXiv 2025年

作者： Curtis, Frank E. Dezfulian, Shima Waechter, Andreas Department of Industrial and Systems Engineering Lehigh University United States Department of Industrial Engineering and Management Sciences Northwestern University United States

An algorithm based on the interior-point methodology for solving continuous nonlinearly constrained optimization problems is proposed, analyzed, and tested. The distinguishing feature of the algorithm is that it presumes that only noisy values of the objective and constraint functions and their first-order derivatives are available. The algorithm is based on a combination of a previously proposed interior-point algorithm that allows inexact subproblem solutions and recently proposed algorithms for solving bound- and equality-constrained optimization problems with only noisy function and derivative values. It is shown that the new interior-point algorithm drives a stationarity measure below a threshold that depends on bounds on the noise in the function and derivative values. The results of numerical experiments show that the algorithm is effective across a wide range of *** Codes 65K05, 49M37, 90C53, 90C30, 90C51 © 2025, CC BY.

关键词： Optimization algorithms

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