Article
Computer Science, Theory & Methods
Gianni D'Angelo, Eslam Farsimadan, Massimo Ficco, Francesco Palmieri, Antonio Robustelli
Summary: The emergence of new and sophisticated malware targeting Android-based IoT devices poses security risks and the need for effective detection models and strategies. Federated Learning-based solutions, which use Machine Learning models without sharing user data, are being developed. However, these methods are affected by non-independent and identically distributed data. Privacy-preserving approaches using Markov chains and associative rules are proposed to handle malware classification in the IoT scenario. The approach achieves high accuracy and comparable runtime performance with centralized methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Alfonso Gomez, Antonio Munoz
Summary: The proliferation of Android-based devices has made them a prime target for attackers. This study presents a supervised learning technique that demonstrates promising results in Android malware detection.
Article
Computer Science, Artificial Intelligence
Nan Zhang, Yu-an Tan, Chen Yang, Yuanzhang Li
Summary: Android mobile devices and applications are widely used in industry and smart city, where malware detection is crucial for security. TC-Droid, an automatic framework based on text classification method, uses a convolutional neural network to explore significant information in original report text, achieving superior performance in Android malware detection.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Yude Bai, Zhenchang Xing, Duoyuan Ma, Xiaohong Li, Zhiyong Feng
Summary: This paper conducts extensive experiments on Android malware family classification, showing that different classification methods perform similarly with neural network model slightly outperforming others. Features are the most important factor for classification, especially for enhancing API features on larger datasets. Furthermore, the model exhibits transferability across different malware datasets based on various transfer learning tasks.
Article
Computer Science, Information Systems
Younghoon Ban, Sunjun Lee, Dokyung Song, Haehyun Cho, Jeong Hyun Yi
Summary: This study focuses on deep learning-based familial analysis of Android malware by examining different features and their effectiveness in representing malicious behaviors. The evaluation on a real-world malware dataset of 28,179 samples reveals the contribution of different features to the performance of familial analysis. With all features combined, the study achieves a high accuracy and micro F1-score.
Article
Engineering, Multidisciplinary
Huijuan Zhu, Yang Li, Ruidong Li, Jianqiang Li, Zhuhong You, Houbing Song
Summary: The study introduces a stacking ensemble framework SEDMDroid to identify Android malware, utilizing techniques such as random feature subspaces and bootstrapping samples to ensure diversity, and employing Principal Component Analysis and Support Vector Machine for accuracy. Experimental results on two datasets demonstrate high accuracy rates, indicating the proposed method is effective for identifying Android malware.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
Shaojie Yang, Yongjun Wang, Haoran Xu, Fangliang Xu, Mantun Chen
Summary: This study proposed a framework based on contrastive learning to reduce the impact of past knowledge and pretrain the model without the participation of labels. The method achieved high accuracy in malware identification and multiclass detection, outperforming supervised models in limited labeled samples.
COMPUTERS & SECURITY
(2022)
Article
Computer Science, Information Systems
Sunder Ali Khowaja, Parus Khuwaja
Summary: This study integrates Q-learning characteristics into an active learning framework, allowing the network to request or predict labels during training. By using a handful of labeled examples, the network can classify malware applications more accurately.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Francesco Mercaldo, Giovanni Ciaramella, Giacomo Iadarola, Marco Storto, Fabio Martinelli, Antonella Santone
Summary: With the rapid growth of the mobile device market, mobile malware has become increasingly sophisticated. Researchers have focused on developing malware detection systems to enhance the security of sensitive information. In this study, five state-of-the-art Convolutional Neural Network models, one author-developed network, and two quantum models were compared to classify malware. The models achieved the best performance in Android malware detection, and the predictions were explained using the Gradient-weighted Class Activation Mapping.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Mulhem Ibrahim, Bayan Issa, Muhammed Basheer Jasser
Summary: Android is dominating the global smartphone market, leading to a strong need for effective security measures. This research proposes a new method for detecting and classifying Android malware using deep learning models and static analysis, achieving high accuracy in malware detection and classification.
Article
Computer Science, Artificial Intelligence
Hui-juan Zhu, Wei Gu, Liang-min Wang, Zhi-cheng Xu, Victor S. Sheng
Summary: The popularity and flexibility of the Android platform make it a prime target for malicious attackers. By extracting permissions, API calls, and hardware features, a new malware detection framework called MSerNetDroid is proposed. The framework utilizes a novel architectural unit, Multi-Head Squeeze-and-Excitation Residual block (MSer), to learn the correlation between features and recalibrate them from multiple perspectives. Experimental results show that MSerNetDroid successfully detects malware with an accuracy of 96.48%, outperforming state-of-the-art approaches.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Theory & Methods
Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
Summary: Malicious applications, especially those targeting Android, pose a serious threat to developers and end-users. Existing defense approaches based on manual rules or traditional machine learning may not be effective due to the rapid growth of Android malware and the advancement of evasion technologies. Deep learning (DL) techniques have shown promising performance in various domains, so applying DL to Android malware defenses has gained significant research attention. This article presents a systematic literature review that identifies 132 studies from 2014 to 2021, revealing the prevalence of DL-based Android malware detection and other defense approaches based on DL.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Information Systems
Esra Calik Bayazit, Ozgur Koray Sahingoz, Buket Dogan
Summary: Advancements in microelectronics have increased the popularity of mobile devices and made Android the leading operating system. While Android's openness brings benefits, it also poses security risks. Deep learning models offer an effective solution for detecting and classifying malware on Android systems.
Article
Computer Science, Theory & Methods
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yang Xiang
Summary: Deep Learning (DL) is a disruptive technology that has revolutionized cyber security research, especially in the detection and classification of Android malware. While offering many advantages, DL faces challenges such as choice of architecture, feature extraction, and obtaining high-quality data.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Theory & Methods
Syed Ibrahim Imtiaz, Saif ur Rehman, Abdul Rehman Javed, Zunera Jalil, Xuan Liu, Waleed S. Alnumay
Summary: As the use of Android smartphones becomes more widespread, there is an increasing need for more efficient methods to detect and prevent malicious applications from attacking and compromising user devices.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Engineering, Aerospace
Emma Stevenson, Victor Rodriguez-Fernandez, Edmondo Minisci, David Camacho
Summary: This study employs a novel deep learning method, N-BEATS, for predicting solar proxy index in space operations a few days ahead. The experimental results show that this method performs well in single point forecasting and can generate uncertainty estimates. The N-BEATS model outperforms baseline models and statistical methods, demonstrating significant advantages in performance.
Review
Computer Science, Artificial Intelligence
Javier Torregrosa, Gema Bello-Orgaz, Eugenio Martinez-Camara, Javier Del Ser, David Camacho
Summary: This article discusses the issue of extremism as a global problem and explores the application of natural language processing (NLP) in extremism research. The article reviews the definition of extremism, the characteristics of extremist discourse, and the application and achievements of NLP techniques. It also suggests future research directions and challenges in this field.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, Julian Fierrez, David Camacho
Summary: This paper proposes an ensemble method for accurate image classification, which combines automatically detected features and statistical indicators to achieve better performance. Testing on various datasets shows that including additional indicators and using an ensemble classification approach can improve performance.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Javier Torregrosa, Sergio D'Antonio-Maceiras, Guillermo Villar-Rodriguez, Amir Hussain, Erik Cambria, David Camacho
Summary: Political tensions have increased in Europe since the beginning of the new century, leading to social movements and political changes in various countries. This study examines the political discourse and underlying tensions during Madrid's elections in May 2021, using a mixed methodology approach. The findings suggest that the electoral campaign is not as negative as perceived by the citizens, and that ideologically extreme parties tend to use more aggressive language.
COGNITIVE COMPUTATION
(2023)
Article
Chemistry, Multidisciplinary
Yuxuan Gu, Jiakai Gu, Gen Li, Heeseung Yun, Jason J. Jung, Sojung An, David Camacho
Summary: This paper presents a system called the abnormal-weather monitoring and curation service (AWMC), which analyzes weather datasets to show abnormal conditions in specific cities on certain dates. The system uses a dynamic graph-embedding-based anomaly detection method to measure anomaly scores, and evaluations show high precision, recall, and F1 score for all cities monitored by AWMC.
APPLIED SCIENCES-BASEL
(2022)
Article
Automation & Control Systems
Francesco Piccialli, Fabio Giampaolo, David Camacho, Gang Mei
Summary: Deep learning technology is driving the in-depth development of industrial automation. Wang et al. interpret the decision process of convolutional neural networks (CNNs) using a percolation model from a statistical physics perspective. They introduce the concept of differentiation degree and present an empirical formula for quantifying it.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Alvaro Huertas-Garcia, Alejandro Marin, Javier Huertas-Tato, David Camacho
Summary: Content moderation is crucial in stopping unacceptable behaviors in online platforms. This article presents an innovative approach involving the simulation and detection of content evasion techniques using a multilingual transformer model. The developed multilingual tool, pyleetspeak, allows for the generation and simulation of content evasion through word camouflage, while a multilingual NER model is designed for the detection of such evasion techniques.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Javier Huertas-Tato, Alejandro Martin, David Camacho
Summary: The emergence of complex attention-based language models like BERT, RoBERTa or GPT-3 has enabled the tackling of highly complex tasks in various scenarios. However, these models face significant difficulties when applied to specific domains, such as social networks like Twitter. In order to address the challenges of natural language processing in this domain, we present BERTuit, the largest transformer proposed for the Spanish language, pre-trained on a massive dataset of Spanish tweets. Our motivation is to provide a powerful resource for better understanding Spanish Twitter and combating the spread of misinformation. BERTuit is evaluated and compared against competitive multilingual transformers, showing its utility through applications like visualizing groups of hoaxes and profiling authors spreading disinformation.
Article
Computer Science, Artificial Intelligence
Alejandro Martin, Alfonso Hernandez, Moutaz Alazab, Jason Jung, David Camacho
Summary: Images are commonly used for hiding information using steganography techniques. A wide range of steganography methods and steganalysis techniques are available, with recent techniques relying on Convolutional Neural Networks to minimize visual changes. This article demonstrates the use of a Generative Adversarial Network (GAN) to enhance a spatial domain steganalysis method and insert secret information with minimal image alteration. The results show that this approach successfully avoids detection by a state-of-the-art Deep Learning steganalysis architecture.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Victor Rodriguez-Fernandez, David Montalvo-Garcia, Francesco Piccialli, Grzegorz J. Nalepa, David Camacho
Summary: Deep Visual Analytics (DVA) is a field that aims to develop Visual Interactive Systems supported by deep learning for large-scale data processing and implementation across different data and domains. This paper presents DeepVATS, an open-source tool for time series data that uses a self-supervised masked time series autoencoder to discover patterns and anomalies.
KNOWLEDGE-BASED SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Eva Garcia-Soto, Alejandro Martin, Javier Huertas-Tato, David Camacho
Summary: This study utilizes CodeT5 pre-trained language model to generate context and semantic aware embeddings for a better representation of the behavior of Android applications. It shows how these embeddings can be used to train a recurrent neural network for malware detection tasks, and presents promising results.
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON UBIQUITOUS COMPUTING & AMBIENT INTELLIGENCE (UCAMI 2022)
(2023)
Article
Computer Science, Artificial Intelligence
Helena Liz-Lopez, Mamadou Keita, Abdelmalik Taleb-Ahmed, Abdenour Hadid, Javier Huertas-Tato, David Camacho
Summary: Generative deep learning techniques have been widely discussed in the public, but the slow progress in applying these techniques to counter disinformation is concerning. With the ease and credibility of manipulating multimedia content, developing effective forensic techniques becomes invaluable. This survey comprehensively describes modern manipulation and forensic techniques, focusing on their applications in video, audio, and multimodal fusion. The classification of manipulation techniques and the generation of datasets using generative techniques are provided for forensic purposes. The review and comparative analysis of forensic techniques from 2018 to 2023, as well as the comparison of end-to-end forensic tools for end-users, are presented. Clear trends and challenges, such as multilinguality, multimodality, and improving data quality, are identified for future research in an ever-changing adversarial environment.
INFORMATION FUSION
(2024)
Article
Computer Science, Information Systems
Angel Panizo-Lledot, Javier Torregrosa, Raquel Menendez-Ferreira, Daniel Lopez-Fernandez, Pedro P. Alarcon, David Camacho
Summary: Extremist ideologies are spreading in today's society, affecting both the political and social levels. Young people, in particular, are vulnerable to these influences due to their developmental stage. Therefore, it is crucial to equip them with psychological skills to rationalize and resist these ideologies. Video games, already a popular technology among young generations, can be used as an innovative approach to motivate and engage youngsters in interventions to increase psychological resilience. This study adapted a traditional emotional intelligence training program into a serious game-based intervention called YoungRes and evaluated its impact on students. The findings showed that the intervention was well received by students, especially those who frequently play video games, and resulted in improvements in emotional intelligence competences and knowledge about the Islamic culture.
Article
Computer Science, Information Systems
Angel Panizo-Lledot, Martin Pedemonte, Gema Bello-Orgaz, David Camacho
Summary: Multi-Objective Genetic Algorithms (MOGAs) have been successfully applied to dynamic problems in various domains, but they often require special adaptation to work properly in such environments. Different techniques, including immigrant strategies, have been proposed to address the challenges of dynamic environments. This work proposes a new methodology that evaluates the performance of immigrant strategies in two levels: coarse-grain evaluation based on quality, stability, and speed, and fine-grain study of the status of immigrant individuals during the algorithm evolution. A visualization technique for population mixing analysis is also proposed. The proposed methodology is validated in the context of the Dynamic Community Detection problem.
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)