A Survey on Bio-Inspired Method for Detection of Spamming

The objective of this work is to show the importance of bi-inspiration SPAM filtering. To achieve this goal, the author compared two methods: Social bees vs inspiration from the Human Renal. The inspiration is taken from a biological model. Messages are indexed and represented by the n-gram words and characters independent of languages (because message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining. The author starts this article with a short introduction where the readers will see the importance of IT security—especially today. The author then explains and experiments on a two original meta-heuristics and explains the natural model and then the artificial model.

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Comparative Study Between a Swarm Intelligence for Detection and Filtering of SPAM

Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management - Advances in Web Technologies and Engineering ◽

10.4018/978-1-5225-3004-6.ch003 ◽

2018 ◽

pp. 38-65

Author(s):

Mohamed Amine Boudia ◽

Mohamed Elhadi Rahmani ◽

Amine Rahmani

Keyword(s):

Data Mining ◽

Comparative Study ◽

Swarm Intelligence ◽

Scientific Paper ◽

It Security ◽

Short Introduction ◽

Social Bees ◽

N Gram ◽

Insight Into ◽

Artificial Model

This chapter is a comparative study between two bio-inspired approaches based on swarm intelligence for detection and filtering of SPAM: social bees vs. inspiration from the human renal. The authors took inspiration from biological model and use two meta-heuristics because the effects allow the authors to detect the characteristics of unwanted data. Messages are indexed and represented by the n-gram words and characters independent of languages (because a message can be received in any language). The results are promising and provide an important way to use this model for solving other problems in data mining. The authors start this paper with a short introduction where they show the importance of IT security. Then they give a little insight into the state of the art, before starting the essential part of a scientific paper, where they explain and experiment with two original meta-heuristics, and explain the natural model. Then they detail the artificial model.

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A New Meta-Heuristic Based on Social Bees for Detection and Filtering of Spam

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2013070102 ◽

2013 ◽

Vol 4 (3) ◽

pp. 15-33 ◽

Cited By ~ 14

Author(s):

Reda Mohamed Hamou ◽

Abdelmalek Amine ◽

Amine Boudia

Keyword(s):

Data Mining ◽

Collective Intelligence ◽

Biological Model ◽

The Internet ◽

Social Bees ◽

N Gram

Spam is now seized the Internet in phenomenal proportions since a high percentage of total emails exchanged on the Internet. In the fight against spam, the authors are interested in this article experiencing a meta-heuristic based on social bees. The authors took inspiration from biological model of social bees and especially, their organization in the workplace, and collective intelligence. The authors chose this meta-heuristic because it presents effects allow the authors to detect the characteristics of unwanted data. Messages are indexed and represented by the n-gram words and characters independent of languages ??(because a message can be received in any language). The results are promising and provide an important way for the use of this model for solving other problems in data mining.

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Business Intelligence and Data Mining

Research and Development in E-Business through Service-Oriented Solutions - Advances in E-Business Research ◽

10.4018/978-1-4666-4181-5.ch003 ◽

2013 ◽

pp. 57-70

Author(s):

Zsolt T. Kardkovács

Keyword(s):

Data Mining ◽

Decision Support ◽

Strategic Planning ◽

Decision Support Systems ◽

Business Intelligence ◽

Business Processes ◽

Support Systems ◽

Decision Makers ◽

Key Factors ◽

Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

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Building an Intrusion Detection System for IT Security Based on Data Mining Techniques

Scientific Journal of Riga Technical University Computer Sciences ◽

10.2478/v10143-011-0040-3 ◽

2011 ◽

Vol 45 (1) ◽

Author(s):

Pjotrs Dorogovs ◽

Arkady Borisov ◽

Andrejs Romanovs

Keyword(s):

Data Mining ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

It Security ◽

Data Mining Techniques

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A Novel Approach to Find the Saturation Point of n-Gram Encoding Method for Protein Sequence Classification Involving Data Mining

International Conference on Innovative Computing and Communications - Lecture Notes in Networks and Systems ◽

10.1007/978-981-13-2354-6_12 ◽

2018 ◽

pp. 101-108 ◽

Cited By ~ 2

Author(s):

Suprativ Saha ◽

Tanmay Bhattacharya

Keyword(s):

Data Mining ◽

Protein Sequence ◽

Sequence Classification ◽

Saturation Point ◽

Novel Approach ◽

Protein Sequence Classification ◽

N Gram ◽

Encoding Method

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Improving Spam Email Filtering Systems Using Data Mining Techniques

Implementing Computational Intelligence Techniques for Security Systems Design - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-2418-3.ch003 ◽

2020 ◽

pp. 43-72

Author(s):

Wasan Shaker Awad ◽

Wafa M. Rafiq

Keyword(s):

Machine Learning ◽

Data Mining ◽

Genetic Algorithm ◽

Low Cost ◽

High Accuracy ◽

False Positives ◽

Spam Filtering ◽

Spam Filter ◽

Using Data ◽

Email Spam

Email is the most popular choice of communication due to its low-cost and easy accessibility, which makes email spam a major issue. Emails can be incorrectly marked by a spam filter and legitimate emails can get lost in the spam folder or the spam emails can deluge the users' inboxes. Therefore, various methods based on statistics and machine learning have been developed to classify emails accurately. In this chapter, the existing spam filtering methods were studied comprehensively, and a spam email classifier based on the genetic algorithm was proposed. The proposed algorithm was successful in achieving high accuracy by reducing the rate of false positives, but at the same time, it also maintained an acceptable rate of false negatives. The proposed algorithm was tested on 2000 emails from the two popular spam datasets, Enron and LingSpam, and the accuracy was found to be nearly 90%. The results showed that the genetic algorithm is an effective method for spam classification and with further enhancements that will provide a more robust spam filter.

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