On the Effectiveness of Image Processing Based Malware Detection Techniques

2022 ◽  
pp. 1-26
Author(s):  
C. V. Bijitha ◽  
Hiran V. Nath
Keyword(s):  
Image Processing ◽  
Malware Detection ◽  
Detection Techniques

A Theoretical Feature-wise Study of Malware Detection Techniques

2018 ◽  
Vol 6 (12) ◽  
pp. 879-887
Author(s):  
Om Prakash Samantray ◽  
Satya Narayana Tripathy ◽  
Susant Kumar Das
Keyword(s):  
Malware Detection ◽  
Detection Techniques

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

Facial Image Processing in Computer Vision

2013 ◽  
pp. 1111-1123
Author(s):  
Moi Hoon Yap ◽  
Hassan Ugail
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Animation ◽  
Feature Detection ◽  
Research Field ◽  
Future Research ◽  
Expression Recognition ◽  
Facial Image ◽  
Detection Techniques ◽  
Future Challenges

The application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Deep Learning-Based Malware Detection and Classification

2021 ◽  
pp. 93-116
Author(s):  
Mirnalinee T. T. ◽  
Bhuvana J. ◽  
Arul Thileeban S. ◽  
Daniel Jeswin Nallathambi ◽  
Anirudh Muthukumar
Keyword(s):  
Neural Network ◽  
Cyber Security ◽  
Input Data ◽  
Malware Detection ◽  
Primary Objective ◽  
Malware Analysis ◽  
Detection Techniques ◽  
The Family ◽  
Threat Level ◽  
Malware Detection And Classification

Malware analysis is an important aspect of cyber security and is a key component in securing systems from attackers. New malware signatures are being created continuously and detection techniques need to keep pace with them. The primary objective is to propose a solution which detects malicious files in real time by evaluating each file. Other objectives are to assess the threat level of the malware and recognize the family of malicious file. Hence, to cover all the needs and to fulfill the motivation, a deep neural network is more suitable to detect and classify the malware. Convolutional neural network-based system MalNet-D is designed to detect the presence of malware, and subsequently, to classify the detected malware into the family in which it belongs, a variation of MalNet-D termed as MalNet-C is proposed. Images of the executable files, both malign and benign, are used as input data, which is trained by the respective MalNet. This is used to detect and classify malware into families. The system achieved 93% accuracy in malware detection and 96% accuracy in malware classification.

scholarly journals cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Afifa Maryam ◽  
Usman Ahmed ◽  
Muhammad Aleem ◽  
Jerry Chun-Wei Lin ◽  
Muhammad Arshad Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Edge Computing ◽  
Cloud Services ◽  
Hybrid Technique ◽  
Dynamic Features ◽  
Hybrid Features ◽  
Android Malware ◽  
Detection Techniques

Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

A Study on Glaucoma Disease Detection with Image Processing Methods

2020 ◽  
Vol 37 ◽  
pp. 25-35
Author(s):  
Shashilata Rawat ◽  
Uma Shankar Kurmi
Keyword(s):  
Image Processing ◽  
Optic Nerve ◽  
Imaging Techniques ◽  
Advanced Imaging ◽  
Detection Techniques ◽  
Glaucoma Detection ◽  
Mode Decomposition ◽  
Processing Techniques ◽  
The Brain ◽  
Aided Diagnosis

The glaucoma is a developing slow eye that effects optic nerve damage in its most common form. Once the optic nerve has been impaired, visual data is not passed to the brain and permanently visual impairment is caused. Glaucoma computer-aided diagnosis (CAD) is a rising area in which medical imaging is analyzed. The CAD is a more precise approach for glaucoma detection, inspired by recent advanced imaging techniques and high-velocity computers. Laser ophthalmoscope scanning, tomography with optical coherence, and retina tomography of Heidelberg have widely used imaging techniques for detecting glaucoma. In this paper, we provide a study of glaucoma disease with its types and detection techniques. Moreover, this paper tells about image processing techniques to detect glaucoma. Variational mode decomposition has also discussed here.

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