Improved Bayesian-Based Spam Filtering Approach

2013 ◽  
Vol 401-403 ◽  
pp. 1885-1891
Author(s):  
Xue Jiang ◽  
Jun Kai Yi
Keyword(s):  
Beneficial Effect ◽  
Experimental Result ◽  
Bayesian Filtering ◽  
Actual Situation ◽  
Spam Filtering ◽  
Bayesian Filter ◽  
Filtering Algorithm ◽  
Spam Filter ◽  
Filter Approach ◽  
Filtering Approach

Bayesian filtering approach is widely used in the field of anti-spam now. However, the two assumptions of this algorithm are significantly different with the actual situation so as to reduce the accuracy of the algorithm. This paper proposes a detailed improvement on researching of Bayesian Filtering Algorithm principle and implement method. It changes the priori probability of spam from constant figure to the actual probability, improves selection and selection rules of the token, and also adds URL and pictures to the detection content. Finally it designs a spam filter based on improved Bayesian filter approach. The experimental result of this improved Bayesian Filter approach indicates that it has a beneficial effect in the spam filter application.

scholarly journals CPSFS: A Credible Personalized Spam Filtering Scheme by Crowdsourcing

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xin Liu ◽  
Pingjun Zou ◽  
Weishan Zhang ◽  
Jiehan Zhou ◽  
Changying Dai ◽  
...  
Keyword(s):  
Social Trust ◽  
Experimental Results ◽  
Bayesian Filtering ◽  
Spam Filtering ◽  
Network Resources ◽  
Accuracy Rate ◽  
Spam Filters ◽  
Interest Similarity ◽  
Client Side ◽  
Email Spam

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.

Bayesian Spam Filter Based on Distributed Architecture

2010 ◽  
Vol 108-111 ◽  
pp. 1415-1420
Author(s):  
Liang Ye ◽  
Ying Hong Liang ◽  
Peng Liu
Keyword(s):  
Learning Outcomes ◽  
Bayesian Learning ◽  
Distributed Architecture ◽  
Bayesian Filter ◽  
Identification Process ◽  
Spam Filter ◽  
Bayes Algorithm

The flood of spam promotes the development of anti-spam technology. In this paper, we bring forward the Bayesian filter technology based on the distributed architecture, which can realize the sharing of the Bayesian learning outcomes among servers within the system, so as to increase the accuracy of spam recognition. We, in the paper, discuss the sharing model of information with spam features under the distributed architecture and the spam identification process; analyze the Bayes algorithm and carry out the relevant improvements; design the Bayes Filter based on distributed architecture on the above basis and verify the effect of the filter by experiments.

scholarly journals Comparison of speed-accuracy tradeoff between linear and nonlinear filtering algorithms for myocontrol

2018 ◽  
Vol 119 (6) ◽  
pp. 2030-2035 ◽  
Author(s):  
Cassie N. Borish ◽  
Adam Feinman ◽  
Matteo Bertucco ◽  
Natalie G. Ramsy ◽  
Terence D. Sanger
Keyword(s):  
Movement Time ◽  
Control Input ◽  
Bayesian Filtering ◽  
Success Rates ◽  
Bayesian Algorithm ◽  
Signal Processing Algorithm ◽  
Bayesian Filter ◽  
Fitts Law ◽  
Communication Devices ◽  
Speed Accuracy

Nonlinear Bayesian filtering of surface electromyography (EMG) can provide a stable output signal with little delay and the ability to change rapidly, making it a potential control input for prosthetic or communication devices. We hypothesized that myocontrol follows Fitts’ Law, and that Bayesian filtered EMG would improve movement times and success rates when compared with linearly filtered EMG. We tested the two filters using a Fitts’ Law speed-accuracy paradigm in a one-muscle myocontrol task with EMG captured from the dominant first dorsal interosseous muscle. Cursor position in one dimension was proportional to EMG. Six indices of difficulty were tested, varying the target size and distance. We examined two performance measures: movement time (MT) and success rate. The filter had a significant effect on both MT and success. MT followed Fitts’ Law and the speed-accuracy relationship exhibited a significantly higher channel capacity when using the Bayesian filter. Subjects seemed to be less cautious using the Bayesian filter due to its lower error rate and smoother control. These findings suggest that Bayesian filtering may be a useful component for myoelectrically controlled prosthetics or communication devices. NEW & NOTEWORTHY Whereas previous work has focused on assessing the Bayesian algorithm as a signal processing algorithm for EMG, this study assesses the use of the Bayesian algorithm for online EMG control. In other words, the subjects see the output of the filter and can adapt their own behavior to use the filter optimally as a tool. This study compares how subjects adapt EMG behavior using the Bayesian algorithm vs. a linear algorithm.

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