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

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.

A Survey on Bio-Inspired Method for Detection of Spamming

2017 ◽  
Vol 8 (3) ◽  
pp. 1-19
Author(s):  
Mebarka Yahlali
Keyword(s):  
Data Mining ◽  
Biological Model ◽  
It Security ◽  
Spam Filtering ◽  
Short Introduction ◽  
Social Bees ◽  
N Gram ◽  
Artificial Model

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.

A New Meta-Heuristics for Intrusion Detection System by Scenario Inspired From the Protection System of Social Bees

2018 ◽  
pp. 26-37
Author(s):  
Ahmed Chaouki Lokbani ◽  
Mohamed Amine Boudia
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Scientific Paper ◽  
Protection System ◽  
Software Performance ◽  
Social Bees ◽  
Multi Agent ◽  
Insight Into ◽  
Artificial Model

In this paper, the authors propose a meta-heuristic for intrusion detection system by scenario, inspired by the protection system of social bees to their hive. This approach is based on a specialized multi-agent system where the authors give limited responsibility to each guard bee agent: to secure only one port. This specialization aims to better exploit the training set and the hardware and software performance. 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: “Our Approach,” where they explain the natural model. Finally, they simplify their model in a modelling table to share their vision and philosophy to switch from natural model to artificial model.

Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

2018 ◽  
Vol 9 (1) ◽  
pp. 15-39 ◽  
Author(s):  
Mohamed Amine Boudia ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Comparative Study ◽  
Objective Function ◽  
Swarm Intelligence ◽  
The Other ◽  
Original Text ◽  
Simple Majority ◽  
Social Bees ◽  
The Third ◽  
Automatic Text ◽  
Previous Step

This article is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization use the bio-inspired approach to performs the results of the previous step. Its objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate; this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final step concerned in choosing a best summary from all candidates summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.

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

2013 ◽  
Vol 4 (3) ◽  
pp. 15-33 ◽  
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.

Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

2018 ◽  
pp. 276-302
Author(s):  
Mohamed Amine Boudia
Keyword(s):  
Comparative Study ◽  
Objective Function ◽  
Swarm Intelligence ◽  
The Other ◽  
Original Text ◽  
Simple Majority ◽  
Social Bees ◽  
The Third ◽  
Automatic Text ◽  
Previous Step

This chapter is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization uses the bio-inspired approach to perform the results of the previous step, the objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text and minimize the sum of scores in order to increase the summarization rate. This optimization will also give a candidate's summary where the order of the phrases changes compared to the original text. For the third and final step concerning choosing a best summary from all candidate summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.

Ant Colonies and Data Mining

2011 ◽  
pp. 405-421
Author(s):  
Miroslav Bursa ◽  
Lenka Lhotska
Keyword(s):  
Data Mining ◽  
Swarm Intelligence ◽  
Data Clustering ◽  
Medical Data ◽  
Cluster Validation ◽  
Deep Insight ◽  
Future Directions ◽  
Ant Colonies ◽  
Present Discussion ◽  
Insight Into

The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a constantly growing area of current research. Medicine, market, trade, and meteorology belong to the numerous fields that benefit of its techniques. First an introduction into data mining and cluster validation techniques is presented, followed by a review of ant-inspired concepts and applications. The chapter provides a reasonably deep insight into the most successful ant colony and swarm intelligence concepts, their paradigms and application. The authors present discussion, evaluation and comparison of these techniques. Important applications and results recently achieved are provided. Finally, new and prospective future directions in this area are outlined and discussed.

A New Meta-Heuristics for Intrusion Detection System Inspired from the Protection System of Social Bees

2017 ◽  
Vol 11 (1) ◽  
pp. 18-34 ◽  
Author(s):  
Mohamed Amine Boudia ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Scientific Paper ◽  
Protection System ◽  
Software Performance ◽  
Social Bees ◽  
Multi Agent ◽  
Future Work ◽  
Artificial Model

In this paper, the authors will propose a meta-heuristic for intrusion detection system by scenario, inspired from the protection system of social bees to their hive. This approach is based on a specialized multi agent system where the authors will give a limited responsibility to each guard bee agent: to secure only one port, this specialization aims to better exploit the training set and the hardware and software performance. The authors will start this paper by a short introduction where they will show the importance of IT security especially today, then they will give a little insight into the state of the art, before starting the essential part of a scientific paper: “our approach” where the authors will explain the natural model, and then they'll simplify their model in a modelling table to share their vision and philosophy to switch from natural model to artificial model, and then they will detail the artificial model they are going to experience in the next chapter, they will discuss the results and make comparison in the two following chapter to get out with a conclusion and perspective of their future work.

An Insight into Educational Data Mining

2019 ◽  
Vol 7 (2) ◽  
pp. 83-90
Author(s):  
Balwinder Kaur ◽  
Anu Gupta ◽  
R.K.Singla .
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Insight Into
Sign in / Sign up

Export Citation Format

Share Document