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Machine learning based fileless malware traffic classification using image visualization

Cybersecurity 2024年第4期7卷 1-18页

作者： Fikirte Ayalke Demmese Ajaya Neupane Sajad Khorsandroo May Wang Kaushik Roy Yu Fu Department of Computer Science College of EngineeringNorth Carolina A&T State University1601 E Market StGreensboroNC 27411USA Palo Alto Networks Inc.3000 Tannery WaySanta ClaraCA 95054USA

In today's interconnected world,network traffic is replete with adversarial *** technology evolves,these attacks are also becoming increasingly sophisticated,making them even harder to ***,artificial intelli-gence(Al)and,specifically machine learning(ML),have shown great success in fast and accurate detection,classifica-tion,and even analysis of such ***,there is a growing body of literature addressing how subfields of Al/ML(e.g.,natural language processing(NLP))are getting leveraged to accurately detect evasive malicious patterns in network *** this paper,we delve into the current advancements in ML-based network traffic classification using image *** a rigorous experimental methodology,we first explore the process of network traffic to image ***,we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network *** the utilization of production-level tools and utilities in realistic experiments,our proposed solution achieves an impressive accuracy rate of 99.48%in detecting fileless malware,which is widely regarded as one of the most elusive classes of malicious software.

关键词： Network security Traffic classification Fileless malware image visualization Machine learning Intrusion

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image visualization: Dynamic and static images generate users' visual cognitive experience using eye-tracking technology

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DISPLAYS 2022年 73卷

作者： Pei, Huining Huang, Xueqin Ding, Man Hebei Univ Technol Coll Architecture & Art Design 29 Guangrong Rd Tianjin Peoples R China

This paper discusses the influence of dynamic images and traditional static images on user perception in web interface visualizations of products. First, thirty graduate students in industrial design participated in an eye tracking experiment, performing visual imagery (VI) tasks of the product images with different presentation formats and durations. The results of eye movement experiment show that the visual cognitive effect was better for the dynamic images than the static image, and the efficiency of visual search was improved. However, the emotional experience of viewing dynamic images was substantially affected by the presentation time. Secondly, there were significant differences in the cognitive level and emotional experience of the users between the dynamic images with different presentation times. The optimal perception experience was observed at a presentation time of 9000 ms, indicating that the subjective responses of the users' questionnaire survey did not represent the actual cognitive needs of the users. This study provides a scientific basis for product designers to achieve an improved browsing experience of their products.

关键词： image visualization Dynamic images Eye tracking Visual perceptual experience

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Malware detection in industrial internet of things based on hybrid image visualization and deep learning model

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AD HOC NETWORKS 2020年 105卷 102154-102154页

作者： Naeem, Hamad Ullah, Farhan Naeem, Muhammad Rashid Khalid, Shehzad Vasan, Danish Jabbar, Sohail Saeed, Saqib Neijiang Normal Univ Sch Comp Sci Neijiang 641100 Sichuan Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China COMSATS Univ Islamabad Dept Comp Sci Sahiwal Campus Sahiwal 57000 Pakistan Bahria Univ Dept Comp Engn Islamabad Pakistan Tsinghua Univ Sch Software Engn Beijing Peoples R China Manchester Metropolitan Univ Dept Comp & Math CfACS IoT Lab Manchester Lancs England Imam Abdurrahman Bin Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Informat Syst POB 1982 Dammam Saudi Arabia

Now the Industrial Internet of Things (IIoT) devices can be deployed to monitor the flow of data, the source of collection and supervision on a large scale of complex networks. It implements large networks for sending and receiving data connected by smart devices. Malware threats, which are primarily targeted at conventional computers linked to the Internet, can also be targeted at IoT machines. Therefore, a smart protection approach is needed to protect millions of IIoT users against malicious attacks. On the other hand, existing state-of - the-art malware identification methods are not better in terms of computational complexity. In this paper, we design architecture to detect malware attacks on the Industrial Internet of Things (MD-IIOT). For an in-depth analysis of malware, a methodology is proposed based on color image visualization and deep convolution neural network. The findings of the proposed method are compared to former approaches to malware detection. The experimental results indicate that the proposed method's predictive time and detection accuracy are higher than that of previous machine learning and deep learning methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： image visualization Deep Learning Industrial Internet of Things Malware Analysis

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Identification of malicious code variants based on image visualization

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COMPUTERS & ELECTRICAL ENGINEERING 2019年 76卷 225-237页

作者： Naeem, Hamad Guo, Bing Naeem, Muhammad Rashid Ullah, Farhan Aldabbas, Hamza Javed, Muhammad Sufyan Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Al Balqa Appl Univ Prince Abdullah Bin Ghazi Fac Informat & Technol Al Salt Jordan Jinan Univ Dept Phys Siyuan Lab Guangzhou 510632 Guangdong Peoples R China COMSATS Univ Islamabad Dept Phys Lahore Campus Punjab 54000 India

The recent increases in Internet use and the number of malicious attacks are helping attackers generate malware variants through automated software. Because of these attacks, the amount of malware and the number of their variants are continuously increasing. Consequently, an improved malware analysis is a critical requirement to stop the rapid expansion of malicious activities. In this study, we propose a more accurate and slightly faster model to characterize malware variants. To implement the proposed model, we designed a method for transforming a malware binary into a grayscale image. We then propose the use of collective local and global malicious patterns for efficient malware variant identification. To reduce the computational time, the total number of dimensions of both types of patterns is reduced using selection methods. In addition, we prepared a baseline to compare the classification performance of our proposed model with previous state-of-the-art malware detection techniques. The experimental results indicate that the response time and classification performance of our model are better than those of previous models. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Cyber security Feature extraction and selection Grayscale image image visualization LGMP Malware detection Malware variants Machine learning

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Robust PDF Malware Detection with image visualization and Processing Techniques 2

Robust PDF Malware Detection with Image Visualization and Pr...

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2nd International Conference on Data Intelligence and Security (ICDIS)

作者： Corum, Andrew Jenkins, Donovan Zheng, Jun Calvin Coll Dept Comp Sci Grand Rapids MI 49546 USA New Mexico Inst Min & Technol Dept Comp Sci & Engn Socorro NM 87801 USA

ISBN: (纸本)9781728120805

PDF, as one of most popular document file format, has been frequently utilized as a vector by attackers to covey malware due to its flexible file structure and the ability to embed different kinds of content. In this paper, we propose a new learning-based method to detect PDF malware using image processing and processing techniques. The PDF files are first converted to grayscale images using image visualization techniques. Then various image features representing the distinct visual characteristics of PDF malware and benign PDF files are extracted. Finally, learning algorithms are applied to create the classification models to classify a new PDF file as malicious or benign. The performance of the proposed method was evaluated using Contagio PDF malware dataset. The results show that the proposed method is a viable solution fur PDF malware detection. It is also shown that the proposed method is more robust to resist reverse mimicry attacks than the slate-of-art learning-based method.

关键词： PDF malware malware detection image visualization feature extraction

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Spectral image visualization Using Generative Adversarial Networks 15th

Spectral Image Visualization Using Generative Adversarial Ne...

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15th Pacific Rim International Conference on Artificial Intelligence (PRICAI) / 15th Pacific Rim Knowledge Acquisition Workshop (PKAW)

作者： Chen, Siyu Liao, Danping Qian, Yuntao Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783319973043;9783319973036

Spectral images captured by satellites and radio-telescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands, so the visualization based on information fusion and dimensional reduction is required for proper display on a trichromatic monitor which is important for spectral image processing and analysis system. The visualizations of spectral images should preserve as much information as possible from the original signal and facilitate image interpretation. However, most of the existing visualization methods display spectral images in false colors, which contradicts with human's expectation and experience. In this paper, we present a novel visualization method based on generative adversarial network (GAN) to display spectral images in natural colors, in which a structure loss and an adversarial loss are combined to form a new loss function. The adversarial loss fits the visualized image to the natural image distribution using a discriminator network that is trained to distinguish false-color images from natural-color images. At the same time, we use an improved cycle loss as the structure constraint to guarantee structure consistency. Experimental results show that our method is able to generate structure-preserved and natural-looking visualizations.

关键词： image visualization Spectral image Generative adversarial nets

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VISAtlas: An image-Based Exploration and Query System for Large visualization Collections via Neural image Embedding

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第7期30卷 3224-3240页

作者： Ye, Yilin Huang, Rong Zeng, Wei Hong Kong Univ Sci & Technol Guangzhou Guangzhou 511400 Guangdong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

High-quality visualization collections are beneficial for a variety of applications including visualization reference and data-driven visualization design. The visualization community has created many visualization collections, and developed interactive exploration systems for the collections. However, the systems are mainly based on extrinsic attributes like authors and publication years, whilst neglect intrinsic property (i.e., visual appearance) of visualizations, hindering visual comparison and query of visualization designs. This paper presents VISAtlas, an image-based approach empowered by neural image embedding, to facilitate exploration and query for visualization collections. To improve embedding accuracy, we create a comprehensive collection of synthetic and real-world visualizations, and use it to train a convolutional neural network (CNN) model with a triplet loss for taxonomical classification of visualizations. Next, we design a coordinated multiple view (CMV) system that enables multi-perspective exploration and design retrieval based on visualization embeddings. Specifically, we design a novel embedding overview that leverages contextual layout framework to preserve the context of the embedding vectors with the associated visualization taxonomies, and density plot and sampling techniques to address the overdrawing problem. We demonstrate in three case studies and one user study the effectiveness of VISAtlas in supporting comparative analysis of visualization collections, exploration of composite visualizations, and image-based retrieval of visualization designs. The studies reveal that real-world visualization collections (e.g., Beagle and VIS30K) better accord with the richness and diversity of visualization designs than synthetic collections (e.g., Data2Vis), inspiring composite visualizations are identified in real-world collections, and distinct design patterns exist in visualizations from different sources.

关键词： Data visualization visualization Feature extraction Task analysis Layout Taxonomy Semantics visualization collection image embedding visual query image visualization design pattern

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Open Source Applications for image visualization and Processing in Neuroimaging Training

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JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2014年第2期7卷 75-87页

作者： Juanes, Juan A. Ruisoto, Pablo Prats-Galino, Alberto Framinan, Andres Univ Salamanca VisualMed Syst Res Grp Salamanca Spain Univ Barcelona VisualMed Syst Res Grp Barcelona Spain Univ Barcelona Lab Surg NeuroAnat LSNA Barcelona Spain

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS;Second, 3D Slicer version 4.3;and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three- dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

关键词： 3D Slicer image Processing image visualization Medical Informatics Mricron Neuroimaging OsiriX

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Three-dimensional complementary breast ultrasound (3D CBUS): Improving 3D spatial resolution uniformity with orthogonal images

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MEDICAL PHYSICS 2025年第4期52卷 2438-2453页

作者： Park, Claire Keun Sun Aziz, Amal Trumpour, Tiana Bax, Jeffrey Scott Tessier, David Gyacskov, Igor Gardi, Lori Fenster, Aaron Western Univ Schulich Sch Med & Dent Dept Med Biophys London ON Canada Robarts Res Inst 1151 Richmond St N London ON N6A 3K7 Canada Harvard Med Sch Brigham & Womens Hosp Dept Radiat Oncol Boston MA USA Harvard Med Sch Dana Farber Canc Inst Boston MA USA Western Univ Fac Engn Sch Biomed Engn London ON Canada Western Univ Schulich Sch Med & Dent Dept Med Imaging Div Imaging Sci London ON Canada

BackgroundWith increasing evidence supporting three-dimensional (3D) automated breast (AB) ultrasound (US) for supplemental screening of breast cancer in increased-risk populations, including those with dense breasts and in limited-resource settings, there is an interest in developing more robust, cost-effective, and high-resolution 3DUS imaging techniques. Compared with specialized ABUS systems, our previously developed point-of-care 3D ABUS system addresses these needs and is compatible with any conventional US transducer, which offers a cost-effective solution and improved availability in clinical practice. While conventional US transducers have high in-plane resolution (axial and lateral), their out-of-plane resolution is constrained by the poor intrinsic elevational US resolution. Consequently, any oblique view plane in an acquired 3DUS image will contain high in-plane and poor out-of-plane resolution components, diminishing spatial resolution uniformity and overall diagnostic *** develop and validate a novel 3D complementary breast ultrasound (CBUS) approach to improve 3DUS spatial resolution uniformity using a conventional US transducer by acquiring and generating orthogonal 3DUS *** previously developed a cost-effective, portable, dedicated 3D ABUS system consisting of a wearable base, a compression assembly, and a mechanically driven scanner for automated 3DUS image acquisition, compatible with any commercial linear US transducer. For this system, we have proposed 3D CBUS approach which involves acquiring and registering orthogonal 3DUS images (VA${V}_A$ and VB${V}_B$) with an aim of overcoming the poor resolution uniformity in the scanning direction in 3D US images. The voxel intensity values in the 3D CBUS image are computed with a spherical-weighted algorithm from the original orthogonal 3DUS images. Experimental validation was performed with 2DUS frame densities of 2, 4, 6 frames mm-1 using an agar-based phantom with a speed o

关键词： automated breast ultrasound complementary breast ultrasound image reconstruction image visualization orthogonal images resolution uniformity three-dimensional ultrasound

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TV-ADS: A Smarter Attack Detection Scheme Based on Traffic visualization of Wireless Network Event Cell

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JOURNAL OF INTERNET TECHNOLOGY 2024年第2期25卷 301-311页

作者： Zhang, Zhiwei Tang, Guiyuan Ren, Baoquan Li, Hongjun Shen, Yulong Acad Mil Sci Acad Syst Engn Beijing Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

To protect the increasing cyberspace assets, attack detection systems (ADS s) as well as intrusion detection systems (IDS s) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect malicious software and malicious traffic. However, we found that in wireless networks, the results vary significantly depending on the mapping methods used to transform the original network traffic data into visual images. Therefore, in this paper, we propose an AI -based attack detection scheme (TV -ADS) by introducing a novel trafficimage mapping method, which segments the sequential network traffic into individual event cells and transforms variant images to a uniform standard size, and design a CNN model to recognize normal and malicious traffics with these visible network event images. Finally, the results of our experiments on the AWID3 dataset demonstrate that our TVADS outperforms the existing schemes in terms of accuracy, precision, recall, F1 -score and efficiency.

关键词： Wireless network Attack detection image visualization Convolutional neural network

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