Differential evolution detection models for SMS spam

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods

Image Spam Analysis

Image spam is unsolicited bulk email, where the message is embedded in an image. This technique is used to evade text-based spam lters. In this research, we analyze and compare two novel approaches for detecting spam images. Our rst approach focuses on the extraction of a broad set of image features and selection of an optimal subset using a Support Vector Machine (SVM). Our second approach is based on Principal Component Analysis (PCA), where we determine eigenvectors for a set of spam images and compute scores by projecting images onto the resulting eigenspace. Both approaches provide high accuracy with low computational complexity. Further, we develop a new spam image dataset that should prove valuable for improving image spam detection capabilities

A Review on mobile SMS Spam filtering techniques

Under short messaging service (SMS) spam is understood the unsolicited or undesired messages received on mobile phones. These SMS spams constitute a veritable nuisance to the mobile subscribers. This marketing practice also worries service providers in view of the fact that it upsets their clients or even causes them lose subscribers. By way of mitigating this practice, researchers have proposed several solutions for the detection and filtering of SMS spams. In this paper, we present a review of the currently available methods, challenges, and future research directions on spam detection techniques, filtering, and mitigation of mobile SMS spams. The existing research literature is critically reviewed and analyzed. The most popular techniques for SMS spam detection, filtering, and mitigation are compared, including the used data sets, their findings, and limitations, and the future research directions are discussed. This review is designed to assist expert researchers to identify open areas that need further improvement

Content based hybrid sms spam filtering system

World has changed. Everybody is connected. Almost each and everyone have a mobile phone. Millions of SMSs are going around the world over mobile networks in every second. But about 113 of them are spam. SMS spam has become a crucial problem with the increase of mobile penetration around the world. SMS spam filtering is a relatively new task which inherits many issues and solutions from email spam filtering. However it poses its own specific challenges. Server based approaches and Mobile application based approaches are accommodate content based and content less mechanism to do the SMS spam filtering. Though there are approaches, still there is a lack of a hybrid solution which can do general filtering at server level while user specific filtering can be done on mobile level. This paper presents a hybrid solution for SMS spam filtering where both feature phone users as well as smart phone users get benefited. Feature phone users can experience the general filter while smart phone users can configure and filter SMSs based on their own preferences rather than sticking in to a general filter. Server level solution consists of a neural network along with a Bayesian filter and device level filter consists of a Bayesian filter. We have evaluated the accuracy of neural network using spam huge dataset along with some randomly used personal SMSs

Antyscam – practical web spam classifier

To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.

SMS Spam Detection in a Real-World Platform using Machine Learning

Spam detection techniques have made our lives easier by unclogging our inboxes and keeping unsafe messages from being opened. With the automation of text messaging solutions and the increase in telecommunication companies and message providers, the volume of text messages has been on the rise. With this growth came along malicious traffic which users had little control over. In this thesis, we present an implementation of a spam detection system in a real-world text messaging platform. Using well-established machine learning algorithms, we make an in-depth analysis on the performance of the models using two different datasets: one publicly available (N=5,574) and the other gathered from actual traffic of the platform (N=1,477). Making use of the empirical results, we outline the models and hyperparameters which can be used in the platform and in which scenarios they produce optimal performance. The results indicate that our dataset poses a great challenge at accurate classification, most likely due to the small sample size and unbalanced dataset, along with nuances in the dataset. Nevertheless, there were models that were found to have a good all-around performance and they can be trained and used in the platform

Towards secure message systems

Message systems, which transfer information from sender to recipient via communication networks, are indispensable to our modern society. The enormous user base of message systems and their critical role in information delivery make it the top priority to secure message systems. This dissertation focuses on securing the two most representative and dominant messages systems---e-mail and instant messaging (IM)---from two complementary aspects: defending against unwanted messages and ensuring reliable delivery of wanted messages.;To curtail unwanted messages and protect e-mail and instant messaging users, this dissertation proposes two mechanisms DBSpam and HoneyIM, which can effectively thwart e-mail spam laundering and foil malicious instant message spreading, respectively. DBSpam exploits the distinct characteristics of connection correlation and packet symmetry embedded in the behavior of spam laundering and utilizes a simple statistical method, Sequential Probability Ratio Test, to detect and break spam laundering activities inside a customer network in a timely manner. The experimental results demonstrate that DBSpam is effective in quickly and accurately capturing and suppressing e-mail spam laundering activities and is capable of coping with high speed network traffic. HoneyIM leverages the inherent characteristic of spreading of IM malware and applies the honey-pot technology to the detection of malicious instant messages. More specifically, HoneyIM uses decoy accounts in normal users\u27 contact lists as honey-pots to capture malicious messages sent by IM malware and suppresses the spread of malicious instant messages by performing network-wide blocking. The efficacy of HoneyIM has been validated through both simulations and real experiments.;To improve e-mail reliability, that is, prevent losses of wanted e-mail, this dissertation proposes a collaboration-based autonomous e-mail reputation system called CARE. CARE introduces inter-domain collaboration without central authority or third party and enables each e-mail service provider to independently build its reputation database, including frequently contacted and unacquainted sending domains, based on the local e-mail history and the information exchanged with other collaborating domains. The effectiveness of CARE on improving e-mail reliability has been validated through a number of experiments, including a comparison of two large e-mail log traces from two universities, a real experiment of DNS snooping on more than 36,000 domains, and extensive simulation experiments in a large-scale environment

Novel graph analytics for enhancing data insight

Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the graph mining algorithms, but also producing effective graph visualizations in order to enhance human perception. The current thesis deals with the investigation of novel methods for graph analytics, in order to enhance data insight. Towards this direction, the current thesis proposes two methods so as to perform graph mining and visualization. Based on previous works related to graph mining, the current thesis suggests a set of novel graph features that are particularly efficient in identifying the behavioral patterns of the nodes on the graph. The specific features proposed, are able to capture the interaction of the neighborhoods with other nodes on the graph. Moreover, unlike previous approaches, the graph features introduced herein, include information from multiple node neighborhood sizes, thus capture long-range correlations between the nodes, and are able to depict the behavioral aspects of each node with high accuracy. Experimental evaluation on multiple datasets, shows that the use of the proposed graph features for the graph mining procedure, provides better results than the use of other state-of-the-art graph features. Thereafter, the focus is laid on the improvement of graph visualization methods towards enhanced human insight. In order to achieve this, the current thesis uses non-linear deformations so as to reduce visual clutter. Non-linear deformations have been previously used to magnify significant/cluttered regions in data or images for reducing clutter and enhancing the perception of patterns. Extending previous approaches, this work introduces a hierarchical approach for non-linear deformation that aims to reduce visual clutter by magnifying significant regions, and leading to enhanced visualizations of one/two/three-dimensional datasets. In this context, an energy function is utilized, which aims to determine the optimal deformation for every local region in the data, taking the information from multiple single-layer significance maps into consideration. The problem is subsequently transformed into an optimization problem for the minimization of the energy function under specific spatial constraints. Extended experimental evaluation provides evidence that the proposed hierarchical approach for the generation of the significance map surpasses current methods, and manages to effectively identify significant regions and deliver better results. The thesis is concluded with a discussion outlining the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces

Detection of suspicious URLs in online social networks using supervised machine learning algorithms

This thesis proposes the use of several supervised machine learning classification models that were built to detect the distribution of malicious content in OSNs. The main focus was on ensemble learning algorithms such as Random Forest, gradient boosting trees, extra trees, and XGBoost. Features were used to identify social network posts that contain malicious URLs derived from several sources, such as domain WHOIS record, web page content, URL lexical and redirection data, and Twitter metadata. The thesis describes a systematic analysis of the hyper-parameters of tree-based models. The impact of key parameters, such as the number of trees, depth of trees and minimum size of leaf nodes on classification performance, was assessed. The results show that controlling the complexity of Random Forest classifiers applied to social media spam is essential to avoid overfitting and optimise performance. The model complexity could be reduced by removing uninformative features, as the complexity they add to the model is greater than the advantages they give to the model to make decisions. Moreover, model-combining methods were tested, which are the voting and stacking methods. Both show advantages and disadvantages; however, in general, they appear to provide a statistically significant improvement in comparison to the highest singular model. The critical benefit of applying the stacking method to automate the model selection process is that it is effective in giving more weight to more topperforming models and less affected by weak ones. Finally, 'SuspectRate', an online malicious URL detection system, was built to offer a service to give a suspicious probability of tweets with attached URLs. A key feature of this system is that it can dynamically retrain and expand current models