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Machine Learning-Based Malware Detection and Classification in Encrypted TLS Traffic 1

Second International Conference on security, Privacy and Data Analytics, ISPDA 2022

作者： Kashyap, Himanshu Pais, Alwyn Roshan Kondaiah, Cheemaladinne Information Security Research Lab Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal575025 India

ISBN: (数字)9789819935697

ISBN: (纸本)9789819935680

Malware has become a significant threat to Internet users in the modern digital era. Malware spreads quickly and poses a significant threat to cyber security. As a result, network security measures play an important role in countering these cyber threats. Existing malware detection techniques are unable to detect them effectively. A novel Ensemble Machine Learning (ML)-based malware detection technique from Transport Layer security (TLS)-encrypted traffic without decryption is proposed in this paper. The features are extracted from TLS traffic. Based on the extracted features, malware detection is performed using Ensemble ML algorithms. The benign and malware file datasets are created using features extracted from TLS traffic. According to the experimental results, the 65 new extracted features perform well in detecting malware from encrypted traffic. The proposed method achieves an accuracy of 99.85% for random forest and 97.43% for multiclass classification for identifying malware families. The ensemble model achieved an accuracy of 99.74% for binary classification and 97.45% for multiclass classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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security analysis and secured access design for networks of image remote sensing

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Digital Communications and Networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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Cryptanalysis of Full-Round Magpie Block Cipher

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Chinese Journal of Electronics 2023年第2期32卷 217-224页

作者： YANG Yunxiao SUN Bing LIU Guoqiang College of Liberal Arts and Sciences National University of Defense Technology State Key Laboratory of Cryptology Hunan Engineering Research Center of Commercial Cryptography Theory and Technology Innovation State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Science

Magpie is a lightweight block cipher proposed by Li et al. in Acta Electronica Sinica volumn 45,issue 10. It adopts an substitution-permutation network（SPN） structure with a block size of 64 bits and the key size of 96 bits, respectively. To achieve the consistency of the encryption and decryption, which is both hardware and software friendly, 16 bits of the key are used as control signals to select S-boxes and another 16 bits of the key are used to determine the order of the operations. As the designers claimed, the security might be improved as different keys generate different ciphers. This paper analyzes the security of Magpie, studies the difference propagation of Magpie, and finally finds that the cipher has a set of 280weak keys which makes the full-round encryption weak, and corrects the lower bound of the number of active S-boxes to 10 instead of 25 proposed by the designers. In the weak key model, the security of the cipher is reduced by the claimed 280to only 4×216.

关键词： Ciphers Force Aerospace electronics Software Hardware Encryption security

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Resource investment for DDoS attack resistant SDN: a practical assessment

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Science China(information Sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Optimized Magnetic Gradient Tensor Array Algorithm for Enhanced Positioning Accuracy of Underwater Weak Magnetic Targets

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IEEE Sensors Journal 2025年第11期25卷 20297-20308页

作者： Ma, Dingyi Yu, Qiang Li, Dan Meng, Xiangchao Harbin Engineering University National Key Laoratory of Underwater Acoustic Technology Harbin150001 China Ministry of Industry and Information Technology Key Laoratory of Marine Information Acquisition and Security Harbin Engineering University Harbin150001 China Harbin Engineering University College of Underwater Acoustic Engineering Harbin150001 China Harbin Engineering University College of Intelligent Systems Science and Engineering Harbin150001 China Design Institute Hull Room Dalian Shipbuilding Industry Group Co. Ltd Dalian China

This research utilized an 8-fluxgate array, based on the magnetic gradient tensor method (MGT), to perform positioning trials within a controlled aquatic environment. The experiment featured five designated locations and targeted marginal magnetic signals of approximately 2-3 nT, which are resolvable by the sensors. The standard deviation of the positioning outcomes ranged from 0.0724 to 0.0764, while the root mean square error (RMSE) was confined within 0.0921 to 0.1468. At a detection distance of about 6 m, the discrepancies in the x and y axes were comparatively minor, whereas the z-axis exhibited a more significant deviation in positioning. By comparing with STAR, which is also based on the MGT algorithm, and incorporating the averaging method from their approach, we have optimized the original MGT algorithm. This enhancement has led to improved positioning accuracy, with the RMSE reduced to a range of 0.0219 to 0.0734, resulting in superior localization performance. © 2001-2012 IEEE.

关键词： Tensors

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Lung and Colon Cancer Classification of Histopathology Images Using Convolutional Neural Network

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SN Computer Science 2024年第2期5卷 223页

作者： Singh, Onkar Kashyap, Kanchan Lata Singh, Koushlendra Kumar Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Computing Science Engineering Indian Institute of Information Technology Kottayam Kerala India

The greatest significant contributor to cancer-related morbidity and mortality worldwide is malignant lung tumors. Lung cancer frequency has been seen to be on the rise recently. Lung cancer histopathology diagnosis is a crucial part of the patient’s treatment. The current study aims to demonstrate the efficiency of convolutional neural networks for the identification of squamous cell carcinoma and adenocarcinoma of the lung and colon by investigating the diagnosis of histopathology images. Five state-of-the-art pre-trained (ImageNet) convolutional neural network architectures, VGG-19, InceptionResNetV2, DenseNet201, EfficientNetB6, and MobileNetV2, are employed in this investigation to tri-categorize lung cancer images (normal, adenocarcinoma, and squamous cell carcinoma), together with colon cancer images (normal and adenocarcinoma). Regularization strategies have been applied to fine-tune the learning rate for improving accuracy. The LC25000 dataset has been used to validate the proposed method. EfficientNetB6, VGG19, InceptionResNetV2, DenseNet201, and MobileNetV2 accuracy on test data is reported to be 93.12, 98.00, 97.92, 99.12, and 99.32 percent respectively. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DenseNet201 EfficientNetB6 Histopathology images InceptionResNetV2 Lung cancer MobileNetV2 VGG19

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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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Multimedia Tools and Applications 2025年第10期84卷 7493-7518页

作者： Raju, Chintha Sri Pothu Neelapu, Bala Chakravarthy Laskar, Rabul Hussain Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar788010 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Recent advances in one-dimensional alkali-metal hexatitanate photocatalysts for environmental remediation and solar fuel production

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Journal of Materials Science & technology 2024年第35期202卷 201-239页

作者： Qiang Wang Xiaofan Yang Zhao Jing Hong Liu Pengyi Tang Hongmin Zhu Bing Li National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China 2020 X-Lab Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China School of Mechanical and Power Engineering East China University of Science and TechnologyShanghai 200237China Department of Metallurgy Graduate School of EngineeringTohoku UniversitySendai 980-8579Japan

The global energy crisis and environmental problems have become two unprecedented ***-conductor photocatalysis offers a promising strategy to solve them,while its practical application requires advanced photocatalytic *** recent years,one-dimensional(1D)alkali-metal hexatitanate(AHT)photocatalysts have attracted considerable attention in energy and environmental fields due to their high chemical stability,excellent photoactivity,unique ion-exchange,environment-friendly,and cost-effective *** this review,we firstly introduce the basic properties of AHT including crystal and electronic band structure,photoactivity,and structure-property-performance ***,the recent ad-vances in synthesis and modification strategies of 1D AHT photocatalysts are summarized thoughtfully,followed by a comprehensive discussion on their various environmental and energy applications,includ-ing pollutant degradation,H2 generation,and CO_(2)***,the key challenges and prospects are also highlighted for the development of high-performance 1D AHT-based photocatalysts for practi-cal *** hope that this review will shed some light on the rational design of 1D Ti-O-based nanomaterials for efficient environmental remediation and solar fuel production.

关键词： Alkali-metal hexatitanates One-dimensional nanomaterials Photocatalysis Pollutant degradation Solar fuels

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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Computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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An ANN-BP based Multi-Alcohol Classification Model Through Different QCM Gas Sensors 4

An ANN-BP based Multi-Alcohol Classification Model Through D...

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4th International Conference on Communication Technologies, ComTech 2023

作者： Sultan, Aiman Masood, Asif Tahir, Shahzaib Khan, Fawad Sultan, Ahmed Raheeq National University of Sciences and Technology Department of Information Security Islamabad Pakistan Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350337211

Alcohol content detection provides several advantages and is crucial in research encompassing product and business development. The findings of a sensor's precise detection cannot be immediately classified;instead, they need to be processed further to identify the proper type of alcohol. A quartz crystal microbalance (QCM) is one among many sensors that can identify various types of alcohol. In order to identify numerous categories of alcohols, this paper presents an ANN-BP based model trained over 5 different datasets of QCM 3, QCM 6, QCM 7, QCM 10, and QCM 12. The ANN-BP model employs keras optimizer, one-hot encoding to display results and is trained via a fixed learning rate. In this article, five different forms of alcohol are used i.e., 1-octanol, 1-propanol, 2-butanol, and 1-isobutanol. The model is run through several epochs of training with a learning rate of 0.01 and the impact of loss and accuracy with respect to the number of epochs is discussed in detail. The performance analysis of the classification of the test set is carried out by plotting the confusion matrix as well as the metrics of precision, recall, accuracy, and f1 score. The model is also trained by alteration of different hyperparameters and analysis is carried out based on their results. © 2023 IEEE.

关键词： Neural networks

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