scholarly journals Multi-Language Spam/Phishing Classification by Email Body Text: Toward Automated Security Incident Investigation

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 668
Author(s):  
Justinas Rastenis ◽  
Simona Ramanauskaitė ◽  
Ivan Suzdalev ◽  
Kornelija Tunaitytė ◽  
Justinas Janulevičius ◽  
...  
Keyword(s):  
Body Text ◽  
Automated Classification ◽  
Potential Threat ◽  
Security Incident ◽  
Incident Investigation ◽  
Scientific Papers ◽  
Email Message ◽  
Public Dataset ◽  
Email Classification

Spamming and phishing are two types of emailing that are annoying and unwanted, differing by the potential threat and impact to the user. Automated classification of these categories can increase the users’ awareness as well as to be used for incident investigation prioritization or automated fact gathering. However, currently there are no scientific papers focusing on email classification concerning these two categories of spam and phishing emails. Therefore this paper presents a solution, based on email message body text automated classification into spam and phishing emails. We apply the proposed solution for email classification, written in three languages: English, Russian, and Lithuanian. As most public email datasets almost exclusively collect English emails, we investigate the suitability of automated dataset translation to adapt it to email classification, written in other languages. Experiments on public dataset usage limitations for a specific organization are executed in this paper to evaluate the need of dataset updates for more accurate classification results.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Automated classification of copy number variants based on 2019 ACMG standards

2021 ◽  
Vol 132 ◽  
pp. S287-S288
Author(s):  
Jianling Ji ◽  
Ryan Schmidt ◽  
Westley Sherman ◽  
Ryan Peralta ◽  
Megan Roytman ◽  
...  
Keyword(s):  
Copy Number ◽  
Copy Number Variants ◽  
Automated Classification

scholarly journals Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images

2021 ◽  
pp. 102656
Author(s):  
Amira S. Ashour ◽  
Merihan M. Eissa ◽  
Maram A. Wahba ◽  
Radwa A. Elsawy ◽  
Hamada Fathy Elgnainy ◽  
...  
Keyword(s):  
Automated Classification ◽  
Bag Of Features

Hierarchical Scheme for Vehicle Make and Model Recognition

2021 ◽  
pp. 036119812110197
Author(s):  
Chaoqing Wang ◽  
Junlong Cheng ◽  
Yuefei Wang ◽  
Yurong Qian
Keyword(s):  
Intelligent Transportation Systems ◽  
Recognition Accuracy ◽  
Transportation Systems ◽  
Feature Descriptor ◽  
Vehicle Recognition ◽  
Public Dataset ◽  
Classification Of Images ◽  
Model Recognition ◽  
Hierarchical Scheme

A vehicle make and model recognition (VMMR) system is a common requirement in the field of intelligent transportation systems (ITS). However, it is a challenging task because of the subtle differences between vehicle categories. In this paper, we propose a hierarchical scheme for VMMR. Specifically, the scheme consists of (1) a feature extraction framework called weighted mask hierarchical bilinear pooling (WMHBP) based on hierarchical bilinear pooling (HBP) which weakens the influence of invalid background regions by generating a weighted mask while extracting features from discriminative regions to form a more robust feature descriptor; (2) a hierarchical loss function that can learn the appearance differences between vehicle brands, and enhance vehicle recognition accuracy; (3) collection of vehicle images from the Internet and classification of images with hierarchical labels to augment data for solving the problem of insufficient data and low picture resolution and improving the model’s generalization ability and robustness. We evaluate the proposed framework for accuracy and real-time performance and the experiment results indicate a recognition accuracy of 95.1% and an FPS (frames per second) of 107 for the framework for the Stanford Cars public dataset, which demonstrates the superiority of the method and its availability for ITS.

scholarly journals Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Song-Quan Ong ◽  
Hamdan Ahmad ◽  
Gomesh Nair ◽  
Pradeep Isawasan ◽  
Abdul Hafiz Ab Majid
Keyword(s):  
Deep Learning ◽  
Aedes Aegypti ◽  
Real Time ◽  
Aedes Albopictus ◽  
Automated Classification ◽  
Expert Performance ◽  
Deep Convolutional Neural Networks ◽  
Significant Difference ◽  
Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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