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Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware

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Zero-Vuln: Using deep learning and zero-shot learning techni...

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Zakaria Sawadogo Jean-Marie Dembele Gervais Mendy Samuel Ouya Laboratory of Numerical Analysis and Computer Science Gaston Berger University Dakar Senegal Lab of Numerical Analysis and Computer Science Gaston Berger University Saint Louis Senegal Computer Science Telecommunications and Applications Cheikh Anta Diop University Dakar Senegal

The prevalence of cyber security threats, such as the Android Zero-day vulnerability, is becoming increasingly worrisome. With the widespread use of Android-powered mobile devices, attackers are leveraging zero-day vulnerabilities to infect Android software at an alarming rate. Detecting zero-day vulnerabilities in Android applications is particularly challenging due to their unpatched and undiscovered nature, resulting in a lack of reference points for identification. In response to this issue, we propose a novel and effective system called Zero-Vuln, which is designed to classify and identify zero-day Android malware. Zero-Vuln leverages deep learning and zero-shot learning techniques, as well as established data-sets, to identify previously unknown malware. Our approach achieves a remarkable performance of 83% accuracy, as well as high precision and recall, and represents a significant contribution to the field of cyber security.

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The injectivity radius of the compact Stiefel manifold under the Euclidean metric

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arXiv 2024年

作者： Zimmermann, Ralf Stoye, Jakob Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute for Numerical Analysis TU Braunschweig Germany

The injectivity radius of a manifold is an important quantity, both from a theoretical point of view and in terms of numerical applications. It is the largest possible radius within which all geodesics are unique and length-minimizing. In consequence, it is the largest possible radius within which calculations in Riemannian normal coordinates are well-defined. A matrix manifold that arises frequently in a wide range of practical applications is the compact Stiefel manifold of orthogonal p-frames in Rn. We observe that geodesics on this manifold are space curves of constant Frenet curvatures. Using this fact, we prove that the injectivity radius on the Stiefel manifold under the Euclidean metric is π.MSC Codes 15B10, 15B57, 65F99, 53C30, 53C80 © 2024, CC BY-NC-SA.

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Pole Identification using discrete Laguerre expansion and variable projection

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IFAC-PapersOnLine 2023年第2期56卷 3994-3999页

作者： Tamás Dózsa Mátyás Szabari Alexandros Soumelidis Péter Kovács Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis Eötvös Loránd University Faculty of Science Institute for Computer Science and Control Systems and Control Laboratory Eötvös Loránd University Department of Numerical Analysis

We propose a novel algorithm for identifying the poles of transfer functions describing SISO-LTI (single input single output, linear time invariant) systems. Our Identification method works in the frequency domain and consists of two parts. In the first part, we extend a discrete Laguerre expansion based method with an automatic parameter selection scheme. This allows us to find an initial estimate of the poles of SISO-LTI transfer functions without the need for human intuition. Then, in the second part, we propose a novel optimization problem to improve our initial estimates. The proposed optimization aims to reduce the least squared error of a parameterized model, which can be interpreted as an orthogonal projection of the system's frequency response onto a subspace spanned by Generalized Orthogonal Rational Basis functions (GOBFs). We solve the corresponding nonlinear optimization task using gradient based methods, where we can analytically calculate the gradient of the error functional. Through robust numerical experiments, we investigate the behavior of the developed methods and show that they work even in scenarios, when the transfer function has a high number of poles.

关键词： Frequency domain identification Recursive identification Linear systems Variable Projection Generalized Orthogonal Basis Functions, Malmquist-Takenaka functions

Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification 18

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Wang, Rui Shen, Chuanfu Marin-Jimenez, Manuel J. Huang, George Q. Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong University of Cordoba Department of Computing and Numerical Analysis Spain The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798350364132

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition. © 2024 IEEE.

关键词： Unattended sensors

Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity

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arXiv 2024年

作者： Barbudo, Rafael Ramírez, Aurora Romero, José Raúl Department of Computer Science and Numerical Analysis University of Córdoba Córdoba14071 Spain Córdoba Spain

The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper-parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper-parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFlow, a grammar-based evolutionary approach for AWC. EvoFlow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFlow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper-parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFlow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFlow's unique components. Then, we compare EvoFlow with other AWC approaches, encompassing both evolutionary and non-evolutionary techniques. Our findings show that EvoFlow's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFlow is capable of discovering workf

关键词： Machine learning

Artificial intelligence to automate the systematic review of scientific literature

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arXiv 2024年

作者： de la Torre-López, José Ramírez, Aurora Romero, José Raúl Dept. Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Córdoba14071 Spain

Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

ROLE OF PARTIAL FUNCTIONALS IN THE STUDY OF VARIATIONAL SYSTEMS

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Topological Methods in Nonlinear analysis 2025年第1期65卷 383-399页

作者： Stan, Andrei Faculty of Mathematics and Computer Science Babeş-Bolyai University Cluj-Napoca 400084 Romania Tiberiu Popoviciu Institute of Numerical Analysis Romanian Academy Cluj-Napoca 400110 Romania

Applying techniques originally developed for systems lacking a variational structure, we establish conditions for the existence of solutions in systems that possess this property but their energy functional is unbounded both above and below. We show that, in general, our conditions differ from those in the classical mountain pass approach by Ambrosetti– Rabinowitz when dealing with systems of this type. Our theory is put into practice in the context of a coupled system of Stokes equations with reaction terms, where we establish sufficient conditions for the existence of a solution. The systems under study are intermediary between gradienttype systems and Hamiltonian systems. © 2025 Juliusz Schauder Centre for Nonlinear Studies Nicolaus Copernicus University in Toruń.

关键词： mountain pass geometry Stokes system Variational method

HIGH CURVATURE MEANS LOW-RANK: ON THE SECTIONAL CURVATURE OF GRASSMANN AND STIEFEL MANIFOLDS AND THE UNDERLYING MATRIX TRACE INEQUALITIES

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arXiv 2024年

作者： Zimmermann, Ralf Stoye, Jakob University of Southern Denmark Department of Mathematics and Computer Science Odense Denmark TU Braunschweig Institute for Numerical Analysis Braunschweig Germany

Methods and algorithms that work with data on nonlinear manifolds are collectively summarized under the term ‘Riemannian computing’. In practice, curvature can be a key limiting factor for the performance of Riemannian computing methods. Yet, curvature can also be a powerful tool in the theoretical analysis of Riemannian algorithms. In this work, we investigate the sectional curvature of the Stiefel and Grassmann manifold. On the Grassmannian, tight curvature bounds are known since the late 1960ies. On the Stiefel manifold under the canonical metric, it was believed that the sectional curvature does not exceed 5/4. Under the Euclidean metric, the maximum was conjectured to be at 1. For both manifolds, the sectional curvature is given by the Frobenius norm of certain structured commutator brackets of skew-symmetric matrices. We provide refined inequalities for such terms and pay special attention to the maximizers of the curvature bounds. In this way, we prove for the Stiefel manifold that the global bounds of 5/4 (canonical metric) and 1 (Euclidean metric) hold indeed. With this addition, a complete account of the curvature bounds in all admissible dimensions is obtained. We observe that ‘high curvature means low-rank’, more precisely, for the Stiefel and Grassmann manifolds under the canonical metric, the global curvature maximum is attained at tangent plane sections that are spanned by rank-two matrices, while the extreme curvature cases of the Euclidean Stiefel manifold occur for rank-one matrices. numerical examples are included for illustration *** Codes 15B10, 15B57, 15B30, 65F99, 22E70, 53C30, 53C80 © 2024, CC BY-NC-SA.

关键词： Matrix algebra

Studying the Effect of Different Lp Norms in the Context of Time Series Ordinal Classification 19th

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Studying the Effect of Different Lp Norms in the Context of ...

19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020/2021

作者： Guijo-Rubio, David Vargas, Víctor Manuel Gutiérrez, Pedro Antonio Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain

ISBN: (纸本)9783030857127

Time Series Ordinal Classification (TSOC) is yet an unexplored field of machine learning consisting in the classification of time series whose labels follow a natural order relationship between them. In this context, a well-known approach for time series nominal classification was previously used: the Shapelet Transform (ST). The exploitation of the ordinal information was included in two steps of the ST algorithm: 1) by using the Pearson’s determination coefficient (R2 ) for computing the quality of the shapelets, which favours shapelets with better ordering, and 2) by applying an ordinal classifier instead of a nominal one to the transformed dataset. For this, the distance between labels was represented by the absolute value of the difference between the corresponding ranks, i.e. by the L1 norm. In this paper, we study the behaviour of different Lp norms for representing class distances in ordinal regression, evaluating 9 different Lp norms with 7 ordinal time series datasets from the UEA-UCR time series classification repository and 10 different ordinal classifiers. The results achieved demonstrate that the Pearson’s determination coefficient using the L1.9 norm in the computation of the difference between the shapelet and the time series labels achieves a significantly better performance when compared to the rest of the approaches, in terms of both Correct Classification Rate (CCR) and Average Mean Absolute Error (AMAE). © 2021, Springer Nature Switzerland AG.

关键词： Time series