An Opinion Spam Detection Method Based on Multi-Filters Convolutional Neural Network

With the continuous development of e-commerce,consumers show increasing interest in posting comments on consumption experience and quality of commodities.Meanwhile,people make purchasing decisions relying on other comments much more than ever before.So the reliability of commodity comments has a significant impact on ensuring consumers’equity and building a fair internet-trade-environment.However,some unscrupulous online-sellers write fake praiseful reviews for themselves and malicious comments for their business counterparts to maximize their profits.Those improper ways of self-profiting have severely ruined the entire online shopping industry.Aiming to detect and prevent these deceptive comments effectively,we construct a model of Multi-Filters Convolutional Neural Network(MFCNN)for opinion spam detection.MFCNN is designed with a fixed-length sequence input and an improved activation function to avoid the gradient vanishing problem in spam opinion detection.Moreover,convolution filters with different widths are used in MFCNN to represent the sentences and documents.Our experimental results show that MFCNN outperforms current state-of-the-art methods on standard spam detection benchmarks.

Real-Time Spammers Detection Based on Metadata Features with Machine Learning

Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).

Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

Phishing websites present a severe cybersecurity risk since they can lead to financial losses,data breaches,and user privacy violations.This study uses machine learning approaches to solve the problem of phishing website detection.Using artificial intelligence,the project aims to provide efficient techniques for locating and thwarting these dangerous websites.The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification.Logistic Regression,K-Nearest Neighbors,Decision Trees,Random Forests,Support Vector Classifiers,Linear Support Vector Classifiers,and Naive Bayes were all used in the inquiry.This research covers the benefits and drawbacks of several Machine Learning approaches,illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions.The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections.The study evaluates and compares accuracy,precision and recalls of several machine learning models in detecting phishing website URL’s detection.

E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks

Spam has turned into a big predicament these days,due to the increase in the number of spam emails,as the recipient regularly receives piles of emails.Not only is spam wasting users’time and bandwidth.In addition,it limits the storage space of the email box as well as the disk space.Thus,spam detection is a challenge for individuals and organizations alike.To advance spam email detection,this work proposes a new spam detection approach,using the grasshopper optimization algorithm(GOA)in training a multilayer perceptron(MLP)classifier for categorizing emails as ham and spam.Hence,MLP and GOA produce an artificial neural network(ANN)model,referred to(GOAMLP).Two corpora are applied Spam Base and UK-2011Web spam for this approach.Finally,the finding represents evidence that the proposed spam detection approach has achieved a better level in spam detection than the status of the art.

Spam Short Messages Detection via Mining Social Networks

Short message service （SMS） is now becoming an indispensable way of social communication, and the problem of mobile spam is getting increasingly serious. We propose a novel approach for spare messages detection. Instead of conventional methods that focus on keywords or flow rate filtering, our system is based on mining under a more robust structure： the social network constructed with SMS. Several features, including static features, dynamic features and graph features, are proposed for describing activities of nodes in the network in various ways. Experimental results operated on real dataset prove the validity of our approach.

Email Filtering Using Hybrid Feature Selection Model

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.