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Android malware category and family detection and identification using machine learning

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arXiv 2021年

作者： El Fiky, Ahmed Hashem El Shenawy, Ayman Madkour, Mohamed Ashraf Systems and Computer Engineering Dept. Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo Egypt

Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still a long way to go. As a result, there is a need to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Each Android malware family and category has a distinct objective. As a result, it has impacted every corporate area, including healthcare, banking, transportation, government, and e-commerce. In this paper, we presented two machine-learning approaches for Dynamic Analysis of Android Malware: one for detecting and identifying Android Malware Categories and the other for detecting and identifying Android Malware Families, which was accomplished by analyzing a massive malware dataset with 14 prominent malware categories and 180 prominent malware families of CCCS-CICAndMal2020 dataset on Dynamic Layers. Our approach achieves in Android Malware Category detection more than 96 % accurate and achieves in Android Malware Family detection more than 99% accurate. Our approach provides a method for high-accuracy Dynamic Analysis of Android Malware while also shortening the time required to analyze smartphone malware. © 2021, CC BY-NC-ND.

关键词： Malware

A 2-channel time-to-digital converter with 20-ps resolution for pico-second timing application

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A 2-channel time-to-digital converter with 20-ps resolution ...

IEEE Global Conference on Consumer Electronics (GCCE)

作者： Zhouji Du Qin Ke Hang Yu Yan Li Binjie Ge Shanshan Yong Yonghong Cheng Shenzhen Laboratory of IC Design for IOT College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Electronics and Computer Engineering SHENZHEN MCU-BIT University Shenzhen China Shenzhen Graduate School Peking University Shenzhen China Hubble Sensor Solution Shenzhen Co. LTD Shenzhen China

ISBN: (纸本)9781665492331

Time-to-Digital Converter (TDC) provides picosecond accuracy timing and is widely used in many applications such as Light Detection and Ranging (LIDAR), 3-D vision, and ultrasonic flow sensors. This paper introduces a 2-channel TDC with two-level resolution control architecture. Designed in a 180 nm CMOS process, the TDC occupies an area of 2.3 mm $\times 1.3$ mm and achieves 20 ps resolution at 2 Msps measurement rate.

关键词： Laser radar Ultrasonic variables measurement Area measurement Delay lines CMOS process Distance measurement Acoustics

On Partly Smoothness, Activity Identification and Faster Algorithms of L1 over L2 Minimization

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arXiv 2024年

作者： Tao, Min Zhang, Xiao-Ping Xia, Zi-Hao The Department of Mathematics National Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China The Department of Tsinghua-Berkeley Shenzhen Institute Tsinghua University Beijing China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoONM5B 2K3 Canada The Dept. of Mathematics Nanjing University China

The L1/L2 norm ratio arose as a sparseness measure and attracted a considerable amount of attention due to three merits: (i) sharper approximations of L0 compared to the L1;(ii) parameter-free and scale-invariant;(iii) more attractive than L1 under highly-coherent matrices. In this paper, we first establish the partly smooth property of L1 over L2 minimization relative to an active manifold M and also demonstrate its prox-regularity property. Second, we reveal that ADMMp (or ADMM+p ) can identify the active manifold within a finite iterations. This discovery contributes to a deeper understanding of the optimization landscape associated with L1 over L2 minimization. Third, we propose a novel heuristic algorithm framework that combines ADMMp (or ADMM+p ) with a globalized semismooth Newton method tailored for the active manifold M. This hybrid approach leverages the strengths of both methods to enhance convergence. Finally, through extensive numerical simulations, we showcase the superiority of our heuristic algorithm over existing state-of-the-art methods for sparse recovery. © 2024, CC BY.

关键词： Heuristic methods

SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection

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arXiv 2024年

作者： Jakab, Daniel Braun, Alexander Agnew, Cathaoir Mohandas, Reenu Deegan, Brian Michael Molloy, Dara Ward, Enda Scanlan, Anthony Eising, Ciarán Dept. of Electronic and Computer Engineering University of Limerick Co. Limerick CastletroyV94 T9PX Ireland Faculty of Electrical Engineering & Information Technology University of Applied Sciences Düsseldorf40476 Germany Dept. of Electrical and Electronic Engineering University of Galway GalwayH91 TK33 Ireland Valeo Vision Systems Tuam GalwayDY1 22DJ Ireland Lero the Science Foundation Ireland Research Centre for Software University of Limerick LimerickV94 T9PX Ireland

Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px;object detection performance stays largely robust within 0.58%(Faster RCNN), 1.45%(YOLOF) and 1.93%(DETR) across all respective held-out test sets. © 2024, CC BY.

关键词： Automobile simulators

E-learning in the COVID-19 context - Epidemiological and educational challenges

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E-learning in the COVID-19 context - Epidemiological and edu...

2020 International Conference Automatics and Informatics, ICAI 2020

作者： Stefanov, Radkov Rosen Desislava Ivanova, Vankova Grigorova, Radkova Zhaneta Yulka Petkova, Petkova Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria Medical University of Varna Dept. of Social Medicine and Healthcare Organisation Varna Bulgaria Medical University of Varna Publising Dept. Varna Bulgaria Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria

ISBN: (纸本)9781728193083

The Coronavirus pandemic has created a number of challenges in all areas of life. In particular, continuing education in distance form, in conditions of social isolation. Both social and learning problems in the context of COVID-19 are *** challenges in e-learning are considered from an objective and subjective point of view, as well as from a technological point of view. Good examples from the practice of dealing with situations depending on the specifics of the studied disciplines are *** proposals for the construction of an appropriate infrastructure for the purposes of e-learning are presented. © 2020 IEEE.

关键词： Public health

Type III solar radio burst detection and classification: A deep learning approach

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arXiv 2021年

作者： Scully, Jeremiah Carley, Eoin Gallagher, Peter Flynn, Ronan Daly, Mark Dept. of Computer and Software Engineering Athlone Institute of Technology Athlone Ireland School of Cosmic Physics Dublin Institute of Advanced Studies Dublin Ireland

Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterization, a challenge in solar radio physics is the automatic detection and classification of such radio bursts. Classification of SRBs has become fundamental in recent years due to large data rates generated by advanced radio telescopes such as the LOw-Frequency ARray, (LOFAR). Current state-of-the-art algorithms implement the Hough or Radon transform as a means of detecting predefined parametric shapes in images. These algorithms achieve up to 84% accuracy, depending on the Type of radio burst being classified. Other techniques include procedures that rely on Constant-False-Alarm-Rate detection, which is essentially detection of radio bursts using a de-noising and adaptive threshold in dynamic spectra. It works well for a variety of different Types of radio bursts and achieves an accuracy of up to 70%. In this research, we are introducing a methodology named You Only Look Once v2 (YOLOv2) for solar radio burst classification. By using Type III simulation methods we can train the algorithm to classify real Type III solar radio bursts in real-time at an accuracy of 82.63% with a maximum 77 frames per second (fps). © 2021, CC BY.

关键词： Radio astronomy

Delayed asynchronous iterative graph algorithms

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arXiv 2021年

作者： Blanco, Mark P. McMillan, Scott Low, Tze Meng Dept. of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Software Engineering Institute Carnegie Mellon University PittsburghPA United States

Iterative graph algorithms often compute intermediate values and update them as computation progresses. Updated output values are used as inputs for computations in current or subsequent iterations;hence the number of iterations required for values to converge can potentially reduce if the newest values are asynchronously made available to other updates computed in the same iteration. In a multi-threaded shared memory system, the immediate propagation of updated values can cause memory contention that may offset the benefit of propagating updates sooner. In some cases, the benefit of a smaller number of iterations may be diminished by each iteration taking longer. Our key idea is to combine the low memory contention that synchronous approaches have with the faster information sharing of asynchronous approaches. Our hybrid approach buffers updates from threads locally before committing them to the global store to control how often threads may cause conflicts for others while still sharing data within one iteration and hence speeding convergence. On a 112-thread CPU system, our hybrid approach attains up to 4.5% - 19.4% speedup over an asynchronous approach for Pagerank and up to 1.9% - 17% speedup over asynchronous Bellman Ford SSSP. Further, our hybrid approach attains 2.56x better performance than the synchronous approach. Finally, we provide insights as to why delaying updates is not helpful on certain graphs where connectivity is clustered on the main diagonal of the adjacency matrix. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

Adaptive Security Policy Management in Cloud Environments Using Reinforcement Learning

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arXiv 2025年

作者： Saqib, Muhammad Mehta, Dipkumar Yashu, Fnu Malhotra, Shubham Dept. of Computer Science Texas Tech University LubbockTX United States C.K.Pithawalla College of Engineering and Technology Gujrat India Dept. of Computer Science Stony Brook University Stony BrookNY United States Dept. of Software Engineering Rochester Institute of Technology RochesterNY United States

The security of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. Static security policies have become inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learning algorithms, including deep Q Networks and proximal policy optimization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity and Access Management (IAM) policies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results demonstrate that our adaptive RL based framework significantly outperforms static policies, achieving higher intrusion detection rates (92% compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In addition, it maintains high conformity with security requirements and efficient resource usage. These findings validate the effectiveness of adaptive reinforcement learning approaches in improving cloud security policy management. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Ransomware: A framework for security challenges in internet of things 2

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Ransomware: A framework for security challenges in internet ...

2nd International Conference on computer and Information Sciences, ICCIS 2020

作者： Saeed, Soobia Jhanjhi, N.Z. Naqvi, Mehmood Humayun, Mamoona Ahmed, Shakeel Universiti Teknologi Malaysia Dept. of Software Engineering Malaysia Taylor's University Selangor Malaysia Faculty of Engineering Technology Mohawk College Canada Jouf University Dept. of Information Systems Al-Jouf Saudi Arabia King Faisal University Dept. of Computer Science Al-Hofuf Saudi Arabia

ISBN: (数字)9781728154671

ISBN: (纸本)9781728154671

With the increasing volume of smartphones, computers, and sensors in the Internet of Things (IoT) model, enhancing security and preventing ransom attacks have become a major concern. Traditional security mechanisms are no longer applicable due to the involvement of devices with limited resources, which require more computing power and resources. Ransomware is comparatively a new and cruel malware in cyberspace with higher rates of attacks around the world. Ransomware could encrypt entire data to make users unable to access their files and important information. In some cases, the system has been hostage completely by the hackers, and the user may receive a demand for ransom money using different resources o access of his/her own data/system. One of the problems associated with the Internet of Things is how to keep your smartphones secure and keep your data safe as most of the antivirus solutions are not useful in this case. This research concludes the impact of ransomware on the IoT, malware processes, and work on detecting and monitoring smartphone infections. The paper also discusses ransomware awareness to end-user with strategy to defeat it. © 2020 IEEE.

关键词： Smartphones