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Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques

Optimized Approaches to Malware Detection: A Study of Machin...

引用

international conference on communication systems and Network Technologies (CSNT)

作者： Abrar Fahim Shamik Dey Md. Nurul Absur Md Kamrul Siam Md. Tahmidul Huque Jafreen Jafor Godhuli Department of Electrical and Electronic Engineering Islamic University of Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Computer Science City University of New York New York USA Department of Computer Science New York Institute of Technology New York New York USA Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Digital systems find it challenging to keep up with cybersecurity threats. the daily emergence of more than 560,000 new malware strains poses significant hazards to the digital ecosystem. the traditional malware detection methods fail to operate properly and yield high false positive rates with low accuracy of the protection system. this study explores the ways in which malware can be detected using these machine learning (ML) and deep learning (DL) approaches to address those shortcomings. this study also includes a systematic comparison of the performance of some of the widely used ML models, such as random forest, multi-layer perceptron (MLP), and deep neural network (DNN), for determining the effectiveness of the domain of modern malware threat systems. We use a considerable-sized database from Kaggle, which has undergone optimized feature selection and preprocessing to improve model performance. Our finding suggests that the DNN model outperformed the other traditional models with the highest training accuracy of 99.92% and an almost perfect AUC score. Furthermore, the feature selection and preprocessing can help improve the capabilities of detection. this research makes an important contribution by analyzing the performance of the model on the performance metrics and providing insight into the effectiveness of the advanced detection techniques to build more robust and more reliable cybersecurity solutions against the growing malware threats.

关键词： Deep learning Training Accuracy Biological system modeling Transfer learning Artificial neural networks Feature extraction Malware Real-time systems Reliability

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Marine Mammals Sound Classification Using a Lightweight CNN for Edge Computing Devices

Marine Mammals Sound Classification Using a Lightweight CNN ...

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international conference on communication systems and Network Technologies (CSNT)

作者： Ankit Kumar Vellalacheruvu Venkata Siva Sai Anil Kumar Bethi Pardhasaradhi Sagar Koorapati Linga Reddy Cenkeramaddi ICT Dept. University of Agder Norway Smart Manufacturing Department Indian Institute of Information Technology Design and Manufacturing Kancheepuram ICT Dept. Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru India Qualcomm Technologies CPU Design Group Austin TX

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

this paper presents a lightweight Convolutional Neural Network (CNN) for marine mammals sound classification. Seven classes are considered in this work, namely humpback whale, killer whale, pilot whale, sperm whale, spinner dolphin, striped dolphin, and white whale. the sound signals are transformed from the .wav file to time-frequency images through Continuous Wavelet Transform (CWT). the proposed CNN consumes time-frequency images as input and consumes only a 0.8 MB size model benchmarked against several well-known pre-trained models, including DenseNet, Efficient-Net, InceptionNet, MobileNet, NASNet, ResNet, VGGNet, and Xception. Experimental results demonstrate that the proposed lightweight CNN significantly outperformed pre-trained models, able to achieve a validation accuracy of 80.9% in a ten-fold cross-validation framework. the best performance among the pre-trained models is Mo-bileNetV3Small, which achieved only 78.91% validation accuracy in a ten-fold cross-validation framework with a considerably larger model size of 4 MB. Furthermore, the proposed model was successfully deployed on the Raspberry Pi IOT module and able to run with 30.134 milliseconds per inference. the lightweight CNN’s high accuracy and low latency make it an ideal candidate for applications in real-time audio event detection in underwater with resource-constrained devices.

关键词： Time-frequency analysis Accuracy Continuous wavelet transforms Computational modeling Whales Real-time systems Convolutional neural networks Internet of things Edge computing Dolphins

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Perceptual Learning Technology Based on Computer Vision and Machine Learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

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international conference on communication systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. the mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. this may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. the mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. the perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation Machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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Quantum Computation Using Coherent Ising Machines Based on Spiking Neural networks

Quantum Computation Using Coherent Ising Machines Based on S...

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international conference on Wireless communications and Signal Processing (WCSP)

作者： Bo Lu Kai Wen Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing China Beijing QBoson Quantum Technology Co. Ltd. Beijing China

ISBN: (纸本)9781665450867

Coherent Ising machines (CIMs) are a promising approach for quantum computation. It exhibits excellent per-formance in solving the combinatorial optimization problems, however, the existence of amplitude heterogeneity limits the applications of CIMs. Here in this work, we present a system of the spiking neural network composed of antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. It works based on the principles of CIM during solving the combinatorial optimization problems. Moreover, a nonlinear transfer function is chosen to mitigate the amplitude inhomogeneities and destabilize the resulting local minima ac-cording to the dynamical behavior of spiking neurons.

关键词： Wireless communication Quantum computing Transfer functions Optical fiber networks Optical coupling Optical signal processing Biological neural networks

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Performance Analysis of Dual-Hop UAV-Assisted FSO communication System with DF Protocol

Performance Analysis of Dual-Hop UAV-Assisted FSO Communicat...

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international conference on Wireless communications and Signal Processing (WCSP)

作者： Jialin Wang Guanjun Xu Zhongyuan Zhao Maozhe Xu Xiaozong Yu Qinyu Zhang School of Communication and Electronic Engineering East China Normal University Shanghai China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China Hangzhou Dianzi University Hangzhou China Peng Cheng Laboratory Shenzhen China Communication Engineering Research Centre Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)9781665450867

Relay-assisted free-space optical (FSO) communication has been an effective means of mitigating the fading affect caused by atmospheric turbulence. In addition, the emergence of unmanned aerial vehicles (UAVs) has further extended the coverage of relay-assisted FSO communication systems, owing to their mobility and flexibility. In this paper, we analyze the performance of a UAV-assisted dual-hop FSO relay communication system in which the combined effects of attenuation loss, at-mospheric turbulence (following the Málaga and gamma-gamma distributions), and pointing errors caused by UAV fluctuations are considered. Closed-form expressions for the outage probability and average bit error rate (BER) of the system are derived. It is found that the atmospheric turbulence, the position deviation of the UAV, the link length of the source-to-UAV link, and the system modulation scheme have significant effects on the system performance. Finally, we find that a system with coherent binary phase-shift keying yields the best average BER performance.

关键词： Closed-form solutions Protocols System performance Bit error rate Modulation Probability Autonomous aerial vehicles

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Find the Spreading Ability of the Influential Nodes using the IC Model in Social networks

Find the Spreading Ability of the Influential Nodes using th...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Koduru Hajarathaiah Karthik Divvela Yeswanth Attaluri Nithin Chimata Murali Krishna Enduri Satish Anamalamudi Department of CSE SRM University AP India Department of CSE SRM University Amaravati AP India

ISBN: (纸本)9781665487726

In the world of fast-growing technology, we have social media and networks have reached a place where they tend to influence the largest percentage of the population in their respective area or language. In today's world, people are influenced by popular public figures around the world. So in this research, we will identify the most influential people on social networks so that we can easily share the information, which helps in different ways like marketing, stopping the spread of false information, cautions or hazardous information, etc. this helps us spread information to large groups of people with comparatively less capital. Finding influential people can now be done by finding influential nodes in social networks. Different researchers around the world have proposed various ways of finding influential nodes like PageRank, degree centrality, betweenness centrality, closeness centrality, etc. Of these, some come under global-structure-based and some come under local-structure-based. Our idea is for an independent cascade model to be applied to the basic centralities to test the spreading ability. We analyze the relationship between centrality values and information spread. Finally, in this research, we will discuss various centralities that help in finding influential nodes and pick the best centrality depending on the cause or situation.

关键词： Social networking (online) Computational modeling Biological system modeling Sociology Data models communication networks Integrated circuit modeling

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Design of Telkom University Logo Antenna and Its Experimental Characterization

Design of Telkom University Logo Antenna and Its Experimenta...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Edwar Shafira Amkha Zahra Luthfi Noor Syadzy Zamachsyari Rezki Benedikto Renwarin Muhammad Farhan Maulana Achmad Munir Satellite Communication & Radar Lab. School of Electrical Engineering Telkom University Bandung Indonesia Radio Telecomm. and Microwave Lab. School of Electrical Eng. & Informatics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9781665487726

the patch radiator of microstrip antenna has been getting a lot of attentions whereby it can be designed to resemble some certain shapes including company logo or university logo. this paper presents the design and experimental characterization of a microstrip antenna in which the patch radiator resembles the Telkom University logo. the radiator patch has two main features, i.e., a letter ‘U’ and a hat-strip, which are separated by a gap each other. the proposed antenna, which is later named as Telyutenna, is designed on two layers of 1.6 mm thick FR4 epoxy dielectric substrate which has the length of 34 mm and the width of 36 mm. A proximity coupled feed is used to excite the radiator patch yielding the antenna operates at the C-band frequency. the experimental characterization is performed for the realized antenna to measure its radiation characteristics. the characterization result demonstrates that the realized antenna has the reflection coefficient value of -20.28 dB, the fractional bandwidth of 7.47%, and the gain of 3.83 dBi at the frequency of 4.35 GHz.

关键词： Antenna measurements C-band Shape Wearable computers Dielectric substrates Microstrip antennas Bandwidth

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Deep Learning Based Localization Scheme for UAV Aided Wireless Sensor networks

Deep Learning Based Localization Scheme for UAV Aided Wirele...

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international conference on Wireless communications and Signal Processing (WCSP)

作者： Hanjun Zhang Feng Yan Hao Li Kai Ding Tao Wu Weiwei Xia Lianfeng Shen National Mobile Communications Research Laboratory Southeast University Nanjing China Science and Technology on Near-Surface Detection Laboratory Wuxi China Jiangsu ZhongLi Electronic Information Sci-Tech Co. Ltd Changshu China

ISBN: (纸本)9781665450867

In this paper, a deep learning based localization scheme is proposed for unmanned aerial vehicle (UAV) aided wireless sensor networks (WSN s), the scheme contains two components which are ranging and localization. In the ranging component, a UAV works as a data collector and calculates the distances between the ground sensor nodes. In the localization component, a localization convolutional neural network (CNN) is built and trained to estimate the nodes coordinates using the ranging information. Simulations results show that the proposed localization scheme has a higher accuracy comparing with a related method.

关键词： Location awareness Deep learning Wireless communication Training Wireless sensor networks Simulation Signal processing

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A Multi-Fuzzy Logic Model for Assessing Consumer Adoption of Solar Photovoltaic systems

A Multi-Fuzzy Logic Model for Assessing Consumer Adoption of...

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international conference on Humanoid, Nanotechnology, Information Technology, communication and Control, Environment and Management (HNICEM)

作者： Jeremy Jay Magdaong Alvin Culaba Elmer Dadios Mechanical Engineering Department De La Salle University Manila Philippines Manufacturing Engineering Department De La Salle University Manila Philippines

A multi-fuzzy logic model was developed to assess consumer adoption of solar photovoltaic systems. Different factors, such as consumer ratings for energy cost and electricity access, knowledge level, installation costs, and generation capacities, were included in the model. the framework developed includes multiple fuzzy systems, such as Mamdani fuzzy inference systems and fuzzy goal programming to consolidate different data types and reduce the total modeling effort. the model was evaluated using case studies based on different scenarios in the Philippines. the case study results show that the generated adoption index adequately describes how likely a consumer will adopt a solar PV system. Future work for this study includes simulations using actual consumer data and optimization of generation capacity to maximize the economic satisfaction of consumers.

关键词： Photovoltaic systems Fuzzy logic Costs Programming Data models Indexes Information technology

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Improved Tensor-Based Near-Field Target Localization Method in Bistatic MIMO Radar

Improved Tensor-Based Near-Field Target Localization Method ...

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international conference on Signal Processing systems, ICSPS

作者： Ningbo Xie Shan Ouyang Kefei Liao Junzheng Jiang School of Information and Communication Guilin University of Electronic Technology Guilin China

In this paper, we focus on the near-field multitarget localization problem implemented on a bistatic MIMO radar. Difference from the conventional matrix-based methods like Multiple Signal Classification (MUSIC), we establish the signal model with a tensor-based model and solve the target parameters with the power of tensor analysis and numerical optimization methods. the proposed method provides relatively good performance compared to conventional matrix-based methods, both in estimation accuracy and computation cost. Numerical experiments demonstrate the effectiveness and good performance of the proposed method, and it showcases its potential application value in various engineering scenarios.

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