CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing
TLDR
The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.Abstract:
Email spam consumes a lot of network resources and threatens many systems because of its unwanted or malicious content. Most existing spam filters only target complete-spam but ignore semispam. This paper proposes a novel and comprehensive CPSFS scheme: Credible Personalized Spam Filtering Scheme, which classifies spam into two categories: complete-spam and semispam, and targets filtering both kinds of spam. Complete-spam is always spam for all users; semispam is an email identified as spam by some users and as regular email by other users. Most existing spam filters target complete-spam but ignore semispam. In CPSFS, Bayesian filtering is deployed at email servers to identify complete-spam, while semispam is identified at client side by crowdsourcing. An email user client can distinguish junk from legitimate emails according to spam reports from credible contacts with the similar interests. Social trust and interest similarity between users and their contacts are calculated so that spam reports are more accurately targeted to similar users. The experimental results show that the proposed CPSFS can improve the accuracy rate of distinguishing spam from legitimate emails compared with that of Bayesian filter alone.read more
Citations
More filters
Journal ArticleDOI
A lifelong spam emails classification model
TL;DR: An enhanced model is proposed for ensuring lifelong spam classification model and the overall performance of the suggested model is contrasted against various other stream mining classification techniques to prove the success of the proposed model as a lifelong spam emails classification method.
Journal ArticleDOI
Design and Simulation of MIMO Antennas for Mobile Communication
D. S. Bhargava,T. V. Padmavathy,Yadhamuri Vinitha Reddy,Neelapareddy Kavitha,Vuppalapati Hema +4 more
Journal ArticleDOI
Blockchain-Based Crowdsourcing Makes Training Dataset of Machine Learning No Longer Be in Short Supply
TL;DR: This paper reviews studies applying mobile crowdsourcing to training dataset collection and annotation and proposes a new possible combination of machine learning and crowdsourcing systems.
Journal ArticleDOI
SentiFilter: A Personalized Filtering Model for Arabic Semi-Spam Content based on Sentimental and Behavioral Analysis
TL;DR: The proposed SentiFilter model is a hybrid model that combines both sentimental and behavioral factors to detect unwanted content for each user towards pre-defined topics and is expected to provide an effective automated solution for filtering semi-spam content in favor of personalized preferences.
Journal ArticleDOI
Hyperparameter Optimization of Ensemble Models for Spam Email Detection
TL;DR: In this paper , the authors developed baseline models of random forest and extreme gradient boost (XGBoost) ensemble algorithms for the detection and classification of spam emails using the Enron1 dataset.
References
More filters
Journal ArticleDOI
Neural networks for deceptive opinion spam detection
Yafeng Ren,Donghong Ji +1 more
TL;DR: This work empirically explore a neural network model to learn document-level representation for detecting deceptive opinion spam and shows that the proposed method outperforms state-of-the-art methods.
Journal ArticleDOI
Key-Aggregate Searchable Encryption (KASE) for Group Data Sharing via Cloud Storage
TL;DR: This paper proposes the novel concept of key-aggregate searchable encryption and instantiates the concept through a concrete KASE scheme, in which a data owner only needs to distribute a single key to a user for sharing a large number of documents, and the user only need to submit a single trapdoor to the cloud for querying the shared documents.
Journal ArticleDOI
CAIS: A Copy Adjustable Incentive Scheme in Community-Based Socially Aware Networking
TL;DR: This paper proposes a copy adjustable incentive scheme (CAIS), which adopts the virtual credit concept to stimulate selfish nodes to cooperate in data forwarding and demonstrates that CAIS copes well with node selfishness in community-based networks and outperforms other benchmark protocols with high data delivery ratio, low communication overhead, and short data delivery latency.
Journal ArticleDOI
Trust, distrust and lack of confidence of users in online social media-sharing communities
TL;DR: This work adopts the concepts of 'trust', 'distrust', and 'lack of confidence' in social relationships and proposes a novel unifying framework to predict trust and distrust as well as to distinguish the confidently-made decisions (trust or distrust) from lack of confidence without a web of trust.
Journal ArticleDOI
Social-Oriented Adaptive Transmission in Opportunistic Internet of Smartphones
TL;DR: A social-oriented smartphone-based adaptive transmission mechanism to improve the network connectivity and throughput in Internet of Things (IoTs) for smart cities and a firefly-algorithm-based scheme is investigated, by which the formulated NP-complete problem can be solved effectively.