scholarly journals Phishing Detection: Analysis of Visual Similarity Based Approaches

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Ankit Kumar Jain ◽  
B. B. Gupta
Keyword(s):  
Solution Space ◽  
Threshold Value ◽  
Visual Similarity ◽  
Future Research ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Phishing Attacks ◽  
Text Content ◽  
Phishing Detection ◽  
Financial Losses

Phishing is one of the major problems faced by cyber-world and leads to financial losses for both industries and individuals. Detection of phishing attack with high accuracy has always been a challenging issue. At present, visual similarities based techniques are very useful for detecting phishing websites efficiently. Phishing website looks very similar in appearance to its corresponding legitimate website to deceive users into believing that they are browsing the correct website. Visual similarity based phishing detection techniques utilise the feature set like text content, text format, HTML tags, Cascading Style Sheet (CSS), image, and so forth, to make the decision. These approaches compare the suspicious website with the corresponding legitimate website by using various features and if the similarity is greater than the predefined threshold value then it is declared phishing. This paper presents a comprehensive analysis of phishing attacks, their exploitation, some of the recent visual similarity based approaches for phishing detection, and its comparative study. Our survey provides a better understanding of the problem, current solution space, and scope of future research to deal with phishing attacks efficiently using visual similarity based approaches.

scholarly journals Classification of Phishing Email Using Random Forest Machine Learning Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
False Negative ◽  
Machine Learning Algorithm ◽  
Detection Techniques ◽  
Phishing Attacks ◽  
Learning Technique ◽  
Phishing Detection

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates.

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.

scholarly journals Phishing for Long Tails: Examining Organizational Repeat Clickers and Protective Stewards

SAGE Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 215824402199065
Author(s):  
Matthew Canham ◽  
Clay Posey ◽  
Delainey Strickland ◽  
Michael Constantino
Keyword(s):  
Research Effort ◽  
Time Frame ◽  
Future Research ◽  
Negative Behaviors ◽  
Computer Abuse ◽  
Positive Behaviors ◽  
Phishing Attacks ◽  
Long Tails ◽  
Motivated Behaviors ◽  
External Actors

Organizational cybersecurity efforts depend largely on the employees who reside within organizational walls. These individuals are central to the effectiveness of organizational actions to protect sensitive assets, and research has shown that they can be detrimental (e.g., sabotage and computer abuse) as well as beneficial (e.g., protective motivated behaviors) to their organizations. One major context where employees affect their organizations is phishing via email systems, which is a common attack vector used by external actors to penetrate organizational networks, steal employee credentials, and create other forms of harm. In analyzing the behavior of more than 6,000 employees at a large university in the Southeast United States during 20 mock phishing campaigns over a 19-month period, this research effort makes several contributions. First, employees’ negative behaviors like clicking links and then entering data are evaluated alongside the positive behaviors of reporting the suspected phishing attempts to the proper organizational representatives. The analysis displays evidence of both repeat clicker and repeat reporter phenomena and their frequency and Pareto distributions across the study time frame. Second, we find that employees can be categorized according to one of the four unique clusters with respect to their behavioral responses to phishing attacks—“Gaffes,” “Beacons,” “Spectators,” and “Gushers.” While each of the clusters exhibits some level of phishing failures and reports, significant variation exists among the employee classifications. Our findings are helpful in driving a new and more holistic stream of research in the realm of all forms of employee responses to phishing attacks, and we provide avenues for such future research.

scholarly journals Evaluating the Potentiality of Sentinel-2 for Change Detection Analysis Associated to LULUCF in Wallonia, Belgium

Land ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 55
Author(s):  
Odile Close ◽  
Sophie Petit ◽  
Benjamin Beaumont ◽  
Eric Hallot
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Change Detection ◽  
Signal To Noise Ratio ◽  
Land Conversion ◽  
Sustainable Resources ◽  
Detection Techniques ◽  
Detection Analysis ◽  
Conversion Rates ◽  
Sentinel 2

Land Use/Cover changes are crucial for the use of sustainable resources and the delivery of ecosystem services. They play an important contribution in the climate change mitigation due to their ability to emit and remove greenhouse gas from the atmosphere. These emissions/removals are subject to an inventory which must be reported annually under the United Nations Framework Convention on Climate Change. This study investigates the use of Sentinel-2 data for analysing lands conversion associated to Land Use, Land Use Change and Forestry sector in the Wallonia region (southern Belgium). This region is characterized by one of the lowest conversion rates across European countries, which constitutes a particular challenge in identifying land changes. The proposed research tests the most commonly used change detection techniques on a bi-temporal and multi-temporal set of mosaics of Sentinel-2 data from the years 2016 and 2018. Our results reveal that land conversion is a very rare phenomenon in Wallonia. All the change detection techniques tested have been found to substantially overestimate the changes. In spite of this moderate results our study has demonstrated the potential of Sentinel-2 regarding land conversion. However, in this specific context of very low magnitude of land conversion in Wallonia, change detection techniques appear to be not sufficient to exceed the signal to noise ratio.

Deepfake videos: synthesis and detection techniques –a survey

2021 ◽  
pp. 1-21
Author(s):  
Shahela Saif ◽  
Samabia Tehseen
Keyword(s):  
Social Systems ◽  
Generative Models ◽  
Detection Methods ◽  
Future Research ◽  
Fake News ◽  
Research Directions ◽  
Detection Techniques ◽  
Future Research Directions ◽  
Video Creation ◽  
The One

Deep learning has been used in computer vision to accomplish many tasks that were previously considered too complex or resource-intensive to be feasible. One remarkable application is the creation of deepfakes. Deepfake images change or manipulate a person’s face to give a different expression or identity by using generative models. Deepfakes applied to videos can change the facial expressions in a manner to associate a different speech with a person than the one originally given. Deepfake videos pose a serious threat to legal, political, and social systems as they can destroy the integrity of a person. Research solutions are being designed for the detection of such deepfake content to preserve privacy and combat fake news. This study details the existing deepfake video creation techniques and provides an overview of the deepfake datasets that are publicly available. More importantly, we provide an overview of the deepfake detection methods, along with a discussion on the issues, challenges, and future research directions. The study aims to present an all-inclusive overview of deepfakes by providing insights into the deepfake creation techniques and the latest detection methods, facilitating the development of a robust and effective deepfake detection solution.

scholarly journals Persuading end users to act cautiously online: a fear appeals study on phishing

2018 ◽  
Vol 26 (3) ◽  
pp. 264-276 ◽  
Author(s):  
Jurjen Jansen ◽  
Paul van Schaik
Keyword(s):  
Information Sharing ◽  
Fear Appeals ◽  
Future Research ◽  
Protection Motivation ◽  
Online Information ◽  
Fear Appeal ◽  
Content Type ◽  
Phishing Attacks ◽  
Internet Users ◽  
Post Test

Purpose The purpose of this paper is to test the protection motivation theory (PMT) in the context of fear appeal interventions to reduce the threat of phishing attacks. In addition, it was tested to what extent the model relations are equivalent across fear appeal conditions and across time. Design/methodology/approach A pre-test post-test design was used. In the pre-test, 1,201 internet users filled out an online survey and were presented with one of three fear appeal conditions: strong fear appeal, weak fear appeal and control condition. Arguments regarding vulnerability of phishing attacks and response efficacy of vigilant online information-sharing behaviour were manipulated in the fear appeals. In the post-test, data were collected from 786 internet users and analysed with partial least squares path modelling. Findings The study found that PMT model relations hold in the domain of phishing. Self-efficacy and fear were the most important predictors of protection motivation. In general, the model results were equivalent across conditions and across time. Practical Implications It is important to consider online information-sharing behaviour because it facilitates the occurrence and success of phishing attacks. The results give practitioners more insight into important factors to address in the design of preventative measures to reduce the success of phishing attacks. Future research is needed to test how fear appeals work in real-world settings and over longer periods. Originality/value This paper is a substantial adaptation of a previous conference paper (Jansen and Van Schaik, 2017a, b).

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

Development and validation of the Mental-Physical Verb Norms (MPVN): A Text Analysis Measure of Mental State Attribution

2021 ◽  
Author(s):  
Ram Isaac Orr ◽  
michael gilead
Keyword(s):  
Text Analysis ◽  
Human Life ◽  
Mental States ◽  
Future Research ◽  
Intentional Stance ◽  
Unique Contribution ◽  
Mental State Attribution ◽  
Text Content ◽  
Normative Ratings ◽  
Development And Validation

Attribution of mental states to self and others, i.e., mentalizing, is central to human life. Current measures are lacking in ability to directly gauge the extent of individuals engage in spontaneous mentalizing. Focusing on natural language use as an expression of inner psychological processes, we developed the Mental-Physical Verb Norms (MPVN). These norms are participant-derived ratings of the extent to which common verbs reflect mental (opposite physical) activities and occurrences, covering ~80% of all verbs appearing within a given English text. Content validity was assessed against existing expert-compiled dictionaries of mental states and cognitive processes, as well as against normative ratings of verb concreteness. Criterion Validity was assessed through natural text analysis of internet comments relating to mental health vs. physical health. Results showcase the unique contribution of the MPVN ratings as a measure of the degree to which individuals adopt the intentional stance in describing targets, by describing both self and others in mental, opposite physical terms. We discuss potential uses for future research across various psychological and neurocognitive disciplines.

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.

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