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Android Malware Detection: An Empirical Investigation into Machine Learning Classifiers

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Android Malware Detection: An Empirical Investigation into M...

IEEE International Conference on Information Reuse and Integration (IRI)

作者： Aaditya Raval Mohd Anwar Computer Science Department Human-Centered AI Lab North Carolina Agricultural and Technical State University Greensboro NC USA

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

Smartphones have become an all-in-one device due to their constant connectivity and ability to provide a wide range of functions. However, they can expose users to various forms of malware, posing significant threats to their privacy, security, and financial well-being. In this context, the detection and classification of Android malware have emerged as critical research areas in cybersecurity. Our study develops six distinct models/classifiers for detecting and classifying Android malware, leveraging the following machine-learning algorithms: MLP, logistic regression, random forest, SVM, XGBoost, and AdaBoost. Through a systematic evaluation process, we assess the efficacy of each model, highlighting their respective strengths and weaknesses. These findings not only contribute to the existing body of knowledge but also pave the way for future research and innovation in the field of Android security. Furthermore, we investigate the impact of data preprocessing and feature selection strategies on model performance and generalization capabilities. Our experimental results reveal that Random Forest (RF) and Extreme Gradient Boosting (XGBoost) classifiers outperformed others in classifying Android malware, showcasing performance of around $93 \%$ and $92 \%$, respectively across accuracy, precision, recall, and f1-score. With an AUC of 0.93 for RF and 0.92 for XGBoost, these models can clearly distinguish between malware and benign samples with minimum misclassifications. Our findings shed light on the effectiveness of machine learning algorithms in combating Android malware and offer valuable insights into the most suitable models. Ultimately, this research study advances the understanding of Android malware detection and classification, providing a foundation for developing robust security solutions in the mobile computing landscape.

关键词： Radio frequency Support vector machines Technological innovation Privacy Systematics Feature extraction Malware Rough surfaces Random forests Smart phones

MODEL AGNOSTIC INTERPRETABILITY FOR MULTIPLE INSTANCE LEARNING

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arXiv 2022年

作者： Early, Joseph Evers, Christine Ramchurn, Sarvapali Agents Interaction and Complexity Group Department of Electronics and Computer Science University of Southampton United Kingdom

In Multiple Instance Learning (MIL), models are trained using bags of instances, where only a single label is provided for each bag. A bag label is often only determined by a handful of key instances within a bag, making it difficult to interpret what information a classifier is using to make decisions. In this work, we establish the key requirements for interpreting MIL models. We then go on to develop several model-agnostic approaches that meet these requirements. Our methods are compared against existing inherently interpretable MIL models on several datasets, and achieve an increase in interpretability accuracy of up to 30%. We also examine the ability of the methods to identify interactions between instances and scale to larger datasets, improving their applicability to real-world problems. © 2022, CC BY.

关键词： Learning systems

A Modular Approach for 3D Reconstruction with Point Cloud Overlay

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A Modular Approach for 3D Reconstruction with Point Cloud Ov...

2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Schieber, Hannah Schmid, Fritz Mubashir-Ui-Hassan Zollmann, Stefanie Roth, Daniel Human-Centered Computing and Extended Reality Germany University of Otago Department of Computer Science Dunedin New Zealand Technical University of Munich School of Medicine and Health Klinikum Rechts der Isar Orthopaedics and Sports Orthopaedics Germany

ISBN: (纸本)9798350328912

Integrating the real-world through the power of 3D reconstruction plays an essential role in extended reality teleconsultation. Video streams and voxel-based representations are common techniques for teleconsultation visualization. However, video streaming applications miss 3D content information, and voxel-based representation can only provide visual details with increasing computational costs. To enhance the classical voxel-based representation and address the timeliness of voxels, we present a modular approach which provides a point cloud overlay to highlight the latest update in the current camera frustum. Our approach applies state-of-the-art algorithms and custom packages within ROS to enable 3D reconstruction with dynamic cameras. We present a modular approach allowing the flexible exchange of the individual part, i.e. the camera or SLAM algorithm. This work presents results from a pilot study involving five participants to gain an impression of what kind of visualization type would be preferred and whether the point cloud overlay would assist the user in recognizing changes in the surroundings. The point cloud overlay enabled the participants to perceive more changes. The pilot study revealed that 60% of the participants showed a preference for the point cloud overlay over the pure mesh representation. © 2023 IEEE.

关键词： Cameras

A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing

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arXiv 2023年

作者： Ghasemi, Safiye Meybodi, Mohammad Reza Fooladi, Mehdi Dehghan Takht Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering and Information Technology Amirkabir University of Technology Tehran Iran Computer Science University of Human Development Sulaymaniyah Iraq

Due to the recent wide use of computational resources in cloud computing, new resource provisioning challenges have been emerged. Resource provisioning techniques must keep total costs to a minimum while meeting the requirements of the requests. According to widely usage of cloud services, it seems more challenging to develop effective schemes for provisioning services cost-effectively;we have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. The contributions of our optimized resource provisioning (ORP) approach are as follows. Firstly, it is designed to provide a cost-effective method to efficiently handle the provisioning of requested applications;while most of the existing models allow only workflows in general which cares about the dependencies of the tasks, ORP performs based on services of which applications comprised and cares about their efficient provisioning totally. Secondly, it is a learning automata-based approach which selects the most proper resources for hosting each service of the demanded application;our approach considers both cost and service requirements together for deploying applications. Thirdly, a comprehensive evaluation is performed for three typical workloads: data-intensive, process-intensive and normal applications. The experimental results show that our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals. © 2023, CC BY.

关键词： Cost reduction

A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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arXiv 2024年

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar The Thumbay Institute for AI in Healthcare Gulf Medical University Ajman United Arab Emirates The Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan The School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland The Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 2024, CC BY-SA.

关键词： Electronic health record

Higher-Order DeepTrails: Unified Approach to *Trails

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arXiv 2023年

作者： Koopmann, Tobias Pfister, Jan Markus, André Carolus, Astrid Wienrich, Carolin Hotho, Andreas University of Würzburg Department of Computer Science CAIDAS Chair for Data Science Germany University of Würzburg Institute Human-Computer-Media Psychology of Intelligent Interactive Systems Germany University of Würzburg Institute Human-Computer-Media Media Psychology Germany

Analyzing, understanding, and describing human behavior is advantageous in different settings, such as web browsing or traffic navigation. Understanding human behavior naturally helps to improve and optimize the underlying infrastructure or user interfaces. Typically, human navigation is represented by sequences of transitions between states. Previous work suggests to use hypotheses, representing different intuitions about the navigation to analyze these transitions. To mathematically grasp this setting, first-order Markov chains are used to capture the behavior, consequently allowing to apply different kinds of graph comparisons, but comes with the inherent drawback of losing information about higher-order dependencies within the sequences. To this end, we propose to analyze entire sequences using autoregressive language models, as they are traditionally used to model higher-order dependencies in sequences. We show that our approach can be easily adapted to model different settings introduced in previous work, namely HypTrails, MixedTrails and even SubTrails, while at the same time bringing unique advantages: 1. Modeling higher-order dependencies between state transitions, while 2. being able to identify short comings in proposed hypotheses, and 3. naturally introducing a unified approach to model all settings. To show the expressiveness of our approach, we evaluate our approach on different synthetic datasets and conclude with an exemplary analysis of a real-world dataset, examining the behavior of users who interact with voice assistants. © 2023, CC BY.

关键词： User interfaces

Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing

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Auto-PIP: Real-time Identification of Critical Performance I...

IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Shenglin Zhang Xiao Xiong Mengyao Li Yongqian Sun Yongxin Zhao Xia Chen Bowen Deng Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area" (SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research.

关键词： Accuracy Manuals Software systems Market research Real-time systems Software reliability Stress Anomaly detection Optimization Testing

Can't Take My Eyes off You: Increasing Task Involvement in Cooperative Driving With Natural Habituated Interfaces

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Can't Take My Eyes off You: Increasing Task Involvement in C...

International Conference on Intelligent Transportation

作者： Jürgen Pichen Nikol Figalova Martin Baumann Department of Human Factors Faculty of Engineering Computer Science and Psychology Ulm University Ulm Germany Department of Clinical and Health Psychology Faculty of Engineering Computer Science and Psychology Ulm University Germany

Vehicle automation is one of the challenges for future transportation, and the question of how to design future interfaces remains unanswered. The goal of this study is to compare two cooperative approaches using (a) a natural habituated steering wheel and (b) a touchscreen interface with (c) an SAE L3 manual take-over approach. We conducted a driving simulator experiment with 26 participants who experienced several overtaking scenarios. Our findings suggest that the manual take-over approach requires more attentional resources than cooperative approaches. Moreover, we observed higher task engagement in the safety-critical phases of the overtaking manoeuvre when using the steering wheel, which could there-after result in safer driving behaviour. Additionally, participants reported that they prefer the steering wheel interface over the touchscreen interface. In conclusion, the natural habituated approach and using the steering wheel interface should be considered when designing the driver-vehicle interaction for SAE L3 vehicles.

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Exciting Action: Investigating Efficient Exploration for Learning Musculoskeletal humanoid Locomotion

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arXiv 2024年

作者： Geiß, Henri-Jacques Al-Hafez, Firas Seyfarth, Andre Peters, Jan Tateo, Davide Computer Science Department Technical University Darmstadt Germany Human Sciences Department Technical University Darmstadt Germany Centre for Cognitive Science Hessian.AI Germany

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning can address this issue by analyzing key problems and providing solutions using both current literature and novel techniques. We validate our methodology by learning walking and running gaits on a simulated humanoid model with 16 degrees of freedom and 92 Muscle-Tendon Units, achieving natural-looking gaits with only a few demonstrations. Code is available at https://***/henriTUD/musculoco learning. © 2024, CC BY-SA.

关键词： Musculoskeletal system