scholarly journals An Observation and Experimental Evaluation of Image Spam Detection

2019 ◽  
Vol 8 (3) ◽  
pp. 5892-5896
Keyword(s):  
Machine Learning ◽  
Experimental Evaluation ◽  
Empirical Investigation ◽  
Classification Systems ◽  
Spam Detection ◽  
Data Sets ◽  
Font Size ◽  
Image Spam ◽  
E Mail ◽  
Background Pattern

In belonging to other supports duel beside researchers of image spam detections, unsolicited mail have newly developed the image based spam dodge to construct the investigation of e-mails’ content of text unsuccessful. To avoid signature based recognition, it involves in implanting the unsolicited text or message into an appendage image, which is frequently arbitrarily customized. Identifying image based spam emails tries out to be an motivating illustration of the problem text embedded in images were subjected to noise such as background pattern, color, font variations and imperfections in a font size so as to eliminate the chances of being identified as unsolicited e-mail by classification techniques. In this research paper we spring a exhaustive review and categorization of machine learning and classification systems suggested so far in contradiction of image based spam email, and make an empirical investigation and correlation of few of them on real, widely accessible data sets.

Image Spam

2017 ◽  
pp. 1-27
Keyword(s):  
Digital Equipment Corporation ◽  
The Internet ◽  
Spam Detection ◽  
Marketing Manager ◽  
Detection Techniques ◽  
History Of ◽  
Image Spam ◽  
Two Sides ◽  
E Mail ◽  
Digital Equipment

In order to understand the never-ending fights between developers of anti-spam detection techniques and the spammers; it is important to have an insight of the history of spam mails. On May 3, 1978, Gary Thuerk, a marketing manager at Digital Equipment Corporation sent his first mass email to more than 400 customers over the Arpanet in order to promote and sell Digital's new T-Series of VAX systems (Streitfeld, 2003). In this regard, he said, “It's too much work to send everyone an e-mail. So we'll send one e-mail to everyone”. He said with pride, “I was the pioneer. I saw a new way of doing things.” As every coin has two sides, any technology too can be utilized for good and bad intention. At that time, Gary Thuerk would have never dreamt of this method of sending mails to emerge as an area of research in future. Gary Thuerk ended up getting crowned as the father of spam mails instead of the father of e-marketing. In the present scenario, the internet receives 2.5 billion pieces of spam a day by spiritual followers of Thuerk.

scholarly journals Weighted k-Nearest Neighbour for Image Spam Classification

2021 ◽  
pp. 1036-1045
Author(s):  
Ahmad M. Salih ◽  
Ban N. Nadim
Keyword(s):  
Optical Character Recognition ◽  
Data Exchange ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Image Features ◽  
Spam Detection ◽  
K Nearest Neighbor ◽  
Image Spam ◽  
E Mail

E-mail is an efficient and reliable data exchange service. Spams are undesired e-mail messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively.

scholarly journals A Comparitive Study of E-Mail Spam Detection using Various Machine Learning Techniques

2021 ◽  
Author(s):  
Simarjeet Kaur ◽  
Meenakshi Bansal ◽  
Ashok Kumar Bathla
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Spam Detection ◽  
Learning Techniques ◽  
E Mail ◽  
Email Spam

Due to the rise in the use of messaging and mailing services, spam detection tasks are of much greater importance than before. In such a set of communications, efficient classification is a comparatively onerous job. For an addressee or any email that the user does not want to have in his inbox, spam can be defined as redundant or trash email. After pre-processing and feature extraction, various machine learning algorithms were applied to a Spam base dataset from the UCI Machine Learning repository in order to classify incoming emails into two categories: spam and non-spam. The outcomes of various algorithms have been compared. This paper used random forest, naive bayes, support vector machine (SVM), logistic regression, and the k nearest (KNN) machine learning algorithm to successfully classify email spam messages. The main goal of this study is to improve the prediction accuracy of spam email filters.

scholarly journals Twitter Spam Detection using Pre-trained Model

2019 ◽  
Vol 8 (4) ◽  
pp. 10620-10623
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Spam Detection ◽  
Social Media Platform ◽  
Machine Learning Model ◽  
The Social ◽  
Media Platform ◽  
E Mail ◽  
Day By Day

In the age of technology social media platform is becoming a great companion for expressing the thoughts, information, and opinion. It became the powerful tool for every person who wants to expand their networks of people beyond the physical boundation. We are living at that age where various categories of social media platform available according to work needed, it may be Facebook, LinkedIn, WhatsApp or Twitter. We are focusing our work on Twitter, It is also known as microblogging site which provides service to express the opinion in limited words. As the popularity of twitter is growing day by day users are joining the platform very fast, as it happens another side many spammers are also taking undue advantages of this platform, for any social media platform it is very important to maintain the secure, safer and trustworthy environment for their legitimate users. Twitter spams are more harmful than e-mail spam because of their higher clickthrough rate, as in the social network if someone trusted some spam a genuine post than it is higher chance that the persons in the network might also trust on that spam post and may click on it. There are plenty of methods available to handle the task of twitter spam detection problem, we are solving this problem of twitter spam at tweet level.Pre-trained models are some breakthrough in the journey of machine learning and natural language processing after their advancement they are of great help. Here we are using Bidirectional Encoder Representation from Transformer (BERT) model to solve the problem as our task is to solve the problem of imbalance dataset as well as the multilingual dataset, BERT makes a clear distinction in this type of task, the main advantage of this type’s model is that we don’t have to collect millions of data for better performance of the machine learning model.

scholarly journals Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets

2021 ◽  
Vol 34 (2) ◽  
pp. 541-549 ◽  
Author(s):  
Leihong Wu ◽  
Ruili Huang ◽  
Igor V. Tetko ◽  
Zhonghua Xia ◽  
Joshua Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Sets ◽  
Predictive Toxicology ◽  
Trade Off

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Sign in / Sign up

Export Citation Format

Share Document