Email Categorization Using Multi-stage Classification Technique

Ren, Kaijun; Gu, Naijie

doi:10.1109/pdcat.2007.71

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…This multi-stage approach aims to mitigate false positive issues, effectively reducing the number of incorrectly classified non-spam emails as spam. Consequently, the overall accuracy of the spam detection system is enhanced, leading to improved performance in identifying spam emails [65]. Wang et al designed seamlessly integrated system with existing anti-spam technologies.…”

Section: Related Workmentioning

confidence: 99%

"A Multi-Dimensional Strategy for Spam Email Classification: Leveraging Origin, Text, and Image Features in a Hybrid Model"

Ghogare,

Patil

2023

Preprint

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Spam emails represent a formidable cybersecurity threat, necessitating precise classification methods to mitigate associated risks and reduce the influx of unwanted messages. This research delves into the quest for improved spam email classification accuracy by leveraging ensemble machine learning techniques, specifically focusing on the utilization of origin, content, and image features within emails. Our study involved the training and testing of a random forest classifier, assessing individual features and integrated features using metrics such as accuracy, recall, precision, and F1 score. The results revealed that the hybrid framework, which combines these features, outperforms individual feature-based approaches. In the context of the ever-evolving landscape of spam technology and the emergence of novel message types challenging traditional methods, we introduce an integrated approach. This approach integrates feature results from various sub models to achieve superior classification accuracy. Our findings demonstrate the outstanding performance of the hybrid approach, achieving the highest accuracy rate (97.6%), recall rate (95.9%), precision rate (98.9%), and F1 score (97.4%) among the tested techniques. The research presents an innovative amalgamation of features that significantly enhances classification accuracy, making a notable contribution to the existing body of knowledge. It underscores the importance of feature integration technique in the field of spam email classification.

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Section: Related Workmentioning

confidence: 99%

"A Multi-Dimensional Strategy for Spam Email Classification: Leveraging Origin, Text, and Image Features in a Hybrid Model"

Ghogare,

Patil

2023

Preprint

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Spam filtering for network traffic security on a multi‐core environment

Islam¹,

Zhou²,

Xiang³

et al. 2009

Concurrency and Computation

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SUMMARYThis paper presents an innovative fusion-based multi-classifier e-mail classification on a ubiquitous multicore architecture. Many previous approaches used text-based single classifiers to identify spam messages from a large e-mail corpus with some amount of false positive tradeoffs. Researchers are trying to prevent false positive in their filtering methods, but so far none of the current research has claimed zero false positive results. In e-mail classification false positive can potentially cause serious problems for the user. In this paper, we use fusion-based multi-classifier classification technique in a multi-core framework. By running each classifier process in parallel within their dedicated core, we greatly improve the performance of our multi-classifier-based filtering system in terms of running time, false positive rate, and filtering accuracy. Our proposed architecture also provides a safeguard of user mailbox from different malicious attacks. Our experimental results show that we achieved an average of 30% speedup at an average cost of 1.4 ms. We also reduced the instances of false positives, which are one of the key challenges in a spam filtering system, and increases e-mail classification accuracy substantially compared with single classification techniques.

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Liu

et al. 2009

Concurrency and Computation

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Email Categorization Using Multi-stage Classification Technique

Cited by 6 publications

References 19 publications

"A Multi-Dimensional Strategy for Spam Email Classification: Leveraging Origin, Text, and Image Features in a Hybrid Model"

"A Multi-Dimensional Strategy for Spam Email Classification: Leveraging Origin, Text, and Image Features in a Hybrid Model"

Spam filtering for network traffic security on a multi‐core environment

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