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

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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Machine learning based fileless malware traffic classification using image visualization

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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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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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On statistical band selection for image visualization

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PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 2001年第5期67卷 571-574页

作者： Beaudemin, M Fung, KB Canada Ctr Remote Sensing Ottawa ON K1A 0Y7 Canada

We show that two often-cited statistical band selection methods for image visualization provide significantly different results when applied to the same data set. The cause of the difference is first identified. Then, an alternative method based on the minimization of redundant information between bands is presented. The new measure is robust against the existence of multicollinearity. A procedure to help determine an appropriate band subset size is also proposed.

关键词： .bands pairs subset combinations Chavez et Band Selection image visualization Chavezet Mauselet al

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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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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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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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MULTIPARAMETER image visualization WITH SELF-ORGANIZING MAPS

MULTIPARAMETER IMAGE VISUALIZATION WITH SELF-ORGANIZING MAPS

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Conference on image Processing

作者： MANDUCA, A MAYO CLIN & MAYO FDN DEPT PHYSIOL & BIOPHYSROCHESTERMN 55905

ISBN: (纸本)081941462X

The effective display of multiparameter medical image data sets is assuming increasing importance as more distinct imaging modalities are becoming available. For medical purposes, one desirable goal is to fuse such data sets into a single most informative gray-scale image without making rigid classification decisions. A visualization technique based on a non-linear projection onto a 1D self-organizing map is described and examples are shown. The SOM visualization technique is fast, theoretically attractive, a useful complement to projection- pursuit or other linear techniques, and may be of particular value in calling attention to specific regions in a multiparameter image where the component images should be examined in detail.

关键词： image visualization Medical imaging visualization image classification image segmentation Biophysics image processing algorithms and systems Medical diagnostics Physiology Tissues

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VARIATIONS ON PROJECTION-PURSUIT FOR MULTIPARAMETER image visualization

VARIATIONS ON PROJECTION-PURSUIT FOR MULTIPARAMETER IMAGE VI...

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CONF ON BIOMEDICAL image PROCESSING AND BIOMEDICAL visualization

作者： HARIKUMAR, G BRESLER, Y Univ. of Illinois/Urbana-Champaign (United States)

ISBN: (纸本)0819411388

This paper addresses the effective display of multi-parameter medical diagnostic data, such as arises in MRI or in multimodality image-fusion. In such data, also known as a vector field, a vector value, rather than a scalar, is associated with each pixel. Each component of the vector corresponds to a different imaging modality, or a different combination of imaging parameters, and may provide different levels of contrast sensitivity between different tissues. While each of the different images may be misleading (as illustrated later by an example), in combination they may contain the correct information. Unfortunately, a human observer is not likely to be able to extract this information when presented with a parallel display of the distinct images. The development of a display technology that overcomes this difficulty by synthesizing a display method matched to the capabilities of the human observer is the subject of this paper.

关键词： image segmentation image visualization Tissues image fusion image processing Statistical analysis visualization Contrast sensitivity Display technology Medical diagnostics

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Hierarchical tone mapping for high dynamic range image visualization

Hierarchical tone mapping for high dynamic range image visua...

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Conference on Visual Communications and image Processing 2005

作者： Qiu, GP Duan, J Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England

ISBN: (纸本)0819459763

In this paper, we present a computationally efficient, practically easy to use tone mapping techniques for the visualization of high dynamic range (HDR) images in low dynamic range (LDR) reproduction devices. The new method, termed hierarchical nonlinear linear (HNL) tone-mapping operator maps the pixels in two hierarchical steps. The first step allocates appropriate numbers of LDR display levels to different HDR intensity intervals according to the pixel densities of the intervals. The second step linearly maps the HDR intensity intervals to theirs allocated LDR display levels. In the developed HNL scheme, the assignment of LDR display levels to HDR intensity intervals is controlled by a very simple and flexible formula with a single adjustable parameter. We also show that our new operators can be used for the effective enhancement of ordinary images.

关键词： high dynamic range image image visualization tone mapping image rendering

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