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Zero-day Android botnet detection using neural networks

Neural Computing and Applications 2025年第17期37卷 10795-10805页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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Optimal Design of Heliostat Field Using Random Search Algorithm 6

Optimal Design of Heliostat Field Using Random Search Algori...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Mou, Chenglin Wu, Kaiqi Du, Pengcheng Yang, Kaibo School of Civil Engineering and Architecture Henan University of Science and Technology Department of Engineering Mechanics Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Computer Science and Technology Luoyang471099 China School of Information Engineering Henan University of Science and Technology Department of Automation Luoyang471099 China School of International Education Henan University of Science and Technology Department of Civil Engineering Luoyang471099 China

ISBN: (纸本)9798350374315

Tower solar thermal power generation is a new type of low-carbon and environmentally friendly clean energy technology. In this paper, a single-objective optimization model is established with the maximum annual average thermal output per unit mirror area of the heliostat field as the objective function. By optimizing the heliostat width, heliostat height and heliostat coordinate, the annual average output thermal power can be maximized while meeting the rated power. The heliostat field is distributed in a circular shape. As the seasons change, the light intensity received by the heliostat field is uneven, so the optical efficiency of the heliostats is also different. We simplify the heliostat field into S area (area with higher optical efficiency) and W area (area with lower optical efficiency) based on the optical efficiency distribution of heliostats. At different locations, According to the heliostat width, heliostat height and heliostat coordinate generated by random functions, substituting these parameters into the efficiency model in this paper, and the random search algorithm is used to traverse the maximum annual output thermal power and the corresponding heliostat parameters. In the shadow occlusion loss model, we have grid to set up the heliostat and use the coordinate conversion to solve the shadow occlusion loss efficiency. © 2024 IEEE.

关键词： Heliostats (instruments)

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A Method on Online Learning Video Recommendation Method Based on Knowledge Graph 12th

A Method on Online Learning Video Recommendation Method Base...

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12th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2022

作者： Chen, Xin Sun, Yuhong Zhou, Tong Zeng, Qingtian Qi, Huafang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Civil Engineering and Architecture Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783031039478

The MOOC platforms usually recommend online course resources to users among a large number of courses on platform whose users are almost college students or students by selves. However, the entire course as the recommendation results may ignore student interests in some specific knowledge points, and so may reduce student learning interests. The recommendation not courses but online learning videos of course are researched. The connection between a single learning video and other entities is considered to construct learning video knowledge graph. Based on the end-to-end deep learning framework, a convolutional neural network KGCN-LV is applied in learning videos recommendation, which is integrated learning video knowledge graph. Experimental results on public data set MOOCCube show that the learning video recommendation method based on knowledge graph is up to 5% better than the traditional collaborative filtering method. The recommendation performance is effectively improved, and the recommendation effect is interpretable. © 2022, IFIP International Federation for Information Processing.

关键词： Convolution

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Recommending Online Course Resources Based on Knowledge Graph 19th

Recommending Online Course Resources Based on Knowledge Grap...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Chen, Xin Sun, Yuhong Zhou, Tong Wen, Yan Zhang, Feng Zeng, Qingtian College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Civil Engineering and Architecture Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9783031203084

Nowadays, it is challenging for college students or lifelong education learners to choose the courses they need under the constant growth of massive online course resources. Therefore the recommendation systems are used to meet their personalized interests. In the scenario of course recommendation, traditional collaborative filtering (CF) is not applicable because of the sparsity of user-item interactions and the cold start problem. Learned from MKR, MKCR is proposed to enhance online courses sources recommendation when the interaction between students and courses is extremely sparse. MKCR is an end-to-end framework that utilizes a knowledge graph embedding task to assist recommendation tasks. The experiment data partially come from the MOOC platform of Chinese universities. The results show MKCR is better performance than other methods in the experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Acorns: A Framework for Accelerating Deep Neural Networks with Input Sparsity 19

Acorns: A Framework for Accelerating Deep Neural Networks wi...

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Proceedings of the International Conference on Parallel architectures and Compilation Techniques

作者： Xiao Dong Lei Liu Peng Zhao Guangli Li Jiansong Li Xueying Wang Xiaobing Feng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728136134

Deep neural networks have been employed in a broad range of applications, including face detection, natural language processing, and autonomous driving. Yet, the neural networks with the capability to tackle real-world problems are intrinsically expensive in computation, hindering the usage of these models. Sparsity in the input data of neural networks provides an optimizing opportunity. However, harnessing the potential performance improvement on modern CPU faces challenges raised by sparse computations of the neural network, such as cache-unfriendly memory accesses and efficient sparse kernel *** this paper, we propose Acorns, a framework to accelerate deep neural networks with input sparsity. In Acorns, sparse input data is organized into our designed sparse data layout, which allows memory-friendly access for kernels in neural networks and opens the door for many performance-critical optimizations. Upon that, Acorns generates efficient sparse kernels for operators in neural networks from kernel templates, which combine directions that express specific optimizing transformations to be performed, and straightforward code that describes the *** evaluations demonstrate Acorns can outperform state-of-the-art baselines by significant speedups. On the real-world detection task in autonomous driving, Acorns demonstrates 1.8-22.6× performance improvement over baselines. Specifically, the generated programs achieve 1.8-2.4× speedups over Intel MKL-DNN, 3.0-8.8× speedups over TensorFlow, and 11.1-13.2× speedups over Intel MKL-Sparse.

关键词： Compiler

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Aware: Adaptive Distributed Training with Computation, Communication and Position Awareness for Deep Learning Model 24

Aware: Adaptive Distributed Training with Computation, Commu...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zeng, Yan Yi, Guangzheng Yin, Yuyu Wu, Jiyang Xue, Meiting Zhang, Jilin Wan, Jian Zhang, Yunquan School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Cyberspace Hangzhou Dianzi University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing China

ISBN: (纸本)9798350319934

The accuracy of the neural networks can usually be improved by increasing the size of the dataset and the layers or operators of the network, as it has strong composability. But, it makes a challenge to train these models efficiently, due to the limited resources of a single device, such as memory, computational resource and so on. For this challenge, it has become an inevitable trend to automatically implement model parallelism across multiple devices. This paper proposes Aware, an adaptive distributed parallel training method, to search distributed parallel strategy automatically for deep learning model. It firstly proposes an operator fusion strategy with computation and communication awareness to simplify the computational graph of deep learning model. And then, it introduces position-aware graph embedding algorithm to extract the structural features of models, which can make the searched parallel strategies to transplant to other networks with similar structures. On this basis, we use reinforcement learning algorithm to search distributed parallel strategy automatically. This paper makes experiments with neural networks such as Inception V3, NMT, GNM, NasNet and ResNet, and uses CIFAR10 and PTB datasets to compare the two methods of Hierarchical and Placeto. The results show that compared with Placeto, Aware achieves up to 5% reductions in runtime. Aware achieves more than 8% reductions in runtime compared with Hierarchical. Moreover, it has better transplantation and generalization capabilities, and supports pre-training for large-scale network parallel strategy search and accelerates convergence. © 2022 IEEE.

关键词： Deep learning

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CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction

arXiv

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arXiv 2024年

作者： Zhang, Shuai Jiang, Zite You, Haihang Meituan Beijing China SKL Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Graph neural network training is mainly categorized into mini-batch and full-batch training methods. The mini-batch training method samples subgraphs from the original graph in each iteration. This sampling operation introduces extra computation overhead and reduces the training accuracy. Meanwhile, the full-batch training method calculates the features and corresponding gradients of all vertices in each iteration, and therefore has higher convergence accuracy. However, in the distributed cluster, frequent remote accesses of vertex features and gradients lead to huge communication overhead, thus restricting the overall training efficiency. In this paper, we introduce the cached-based distributed full-batch graph neural network training framework (CDFGNN). We propose the adaptive cache mechanism to reduce the remote vertex access by caching the historical features and gradients of neighbor vertices. Besides, we further optimize the communication overhead by quantifying the messages and designing the graph partition algorithm for the hierarchical communication architecture. Experiments show that the adaptive cache mechanism reduces remote vertex accesses by 63.14% on average. Combined with communication quantization and hierarchical GP algorithm, CDFGNN outperforms the state-of-the-art distributed full-batch training frameworks by 30.39% in our experiments. Our results indicate that CDFGNN has great potential in accelerating distributed full-batch GNN training tasks. © 2024, CC BY.

关键词： Graph neural networks

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HL-HDR: Multi-Exposure High Dynamic Range Reconstruction with High-Low Frequency Decomposition

HL-HDR: Multi-Exposure High Dynamic Range Reconstruction wit...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiang Zhang Genggeng Chen Tao Hu Kangzhen Yang Fan Zhang Qingsen Yan College of Information and Control Engineering Xi’an University of Architecture and Technology School of Computer Science Northwestern Polytechnical University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Generating high-quality High Dynamic Range (HDR) images in dynamic scenes is particularly challenging. Recent Transformer have been introduced in HDR imaging, demonstrating promising performance, particularly in scenarios involving large-scale motion compared to previous CNN-based methods. However, Transformer-based methods face hurdles capturing local details and come with high computational complexity, hindering further progress. In this paper, inspired by the distinct characteristics of high and low-frequency in image patterns, we propose a Frequency Decomposition Processing Block (FDPB) for ghost-free HDR imaging. In the image reconstruction process, FDPB decouples features into resolution-invariant high-frequency features and resolution-reduced low-frequency features to separately address local and global information. Specifically, considering the characteristics of different frequencies, for the high-frequency components, we design a Local Feature Extractor (LFE) based on CNN to extract local feature maps. Meanwhile, for the low-frequency components, we propose a Global Feature Extractor (GFE) that learns long-range dependencies through carefully designed Transformer modules. Importantly, the downscaled low-frequency features exploit Transformer’s remote learning capabilities while substantially reducing self-attention computational costs. By incorporating the FDPB as basic components, we further build a Low/High-Frequency Aware Network (HL-HDR), a hierarchical network to reconstruct high-quality ghost-free HDR images. Extensive experiments on four public datasets confirm the superior performance of the proposed method, both in terms of quantitative and qualitative evaluations.

关键词： Image resolution Neural networks Imaging Feature extraction Transformers High dynamic range Image restoration

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Optimizing Self-training Sample Selection for Euphemism Detection in Special Scenarios 24th

Optimizing Self-training Sample Selection for Euphemism Dete...

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24th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Zhiwei Chang, Shuyu Huang, Haiping Wang, Rui Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China Nanjing University of Posts and Telecommunications Nanjing210023 China State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Academy of Sciences Beijing100864 China

ISBN: (纸本)9789819615476

Euphemism, which is often used to disguise true intentions or soften the tone of speech, has garnered significant attention for its detection in recent years. Current approaches to euphemism detection struggle with uncommon data, where new and rare vocabularies frequently emerge in the test set, leading to decreased model performance. To address these challenges, we propose a novel framework that combines data augmentation, self-training, and a special sample selection method. We expand the original dataset using lexical and sentence-level data augmentation techniques, enhancing the representation of rare vocabulary and allowing the model to familiarize itself with their contexts and usage characteristics. Next, we employ self-training as a semi-supervised learning approach, utilizing the augmented datasets to iteratively generate and refine pseudo-labels. During each iteration, a KNN framework is used to generate pseudo-labels by searching for the nearest neighbor texts, thus enhancing the quality of these labels. Finally, to further increase the presence of rare vocabulary in the training data, we implement a sample selection strategy based on euphemism frequency and predictive uncertainty, integrating it with the earlier steps. Experimental results demonstrate that our framework outperforms previous research on most metrics and achieves a 10% increase over the baseline model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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SDQ: SPARSE DECOMPOSED QUANTIZATION FOR LLM INFERENCE

arXiv

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arXiv 2024年

作者： Jeong, Geonhwa Tsai, Po-An Keckler, Stephen W. Krishna, Tushar School of Computer Science Georgia Institute of Technology United States Architecture Research Group NVIDIA United States

Recently, large language models (LLMs) have shown surprising performance in task-specific workloads as well as general tasks with the given prompts. However, to achieve unprecedented performance, recent LLMs use billions to trillions of parameters, which hinder the wide adaptation of those models due to their extremely large compute and memory requirements. To resolve the issue, various model compression methods are being actively investigated. In this work, we propose SDQ (Sparse Decomposed Quantization) to exploit both structured sparsity and quantization to achieve both high compute and memory efficiency. From our evaluations, we observe that SDQ can achieve 4× effective compute throughput with © 2024, CC BY-NC-ND.

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