Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Gibson, Simran; Issac, Biju; Zhang, Li; Jacob, Seibu Mary

doi:10.1109/access.2020.3030751

Cited by 57 publications

(42 citation statements)

References 17 publications

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“…The article [228] explores bio-inspired methods to analyze email datasets. Multiple ML models including Naive Bayes, SVM, Random Forest,Decision Trees, and Multilayer Perceptron were considered in [228] to evaluate these datasets.…”

Section: Bio-inspired Aimentioning

confidence: 99%

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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The threat hunting lifecycle is a complex atmosphere that requires special attention from professionals to maintain security. This paper is a collection of recent work that gives a holistic view of the threat hunting ecosystem, identifies challenges, and discusses the future with the integration of artificial intelligence (AI). We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting.

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Section: Bio-inspired Aimentioning

confidence: 99%

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Hillier¹,

Karroubi²

2022

Preprint

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“…x t+1 ir 1 = x t r 1 (11) The E3GOA algorithm is the third algorithm proposed in this work and is shown in Algorithm 4. This algorithm is similar to the previous proposed algorithms but with a slight modification in the mutation operator.…”

Section: B the Enhanced Goa Algorithmmentioning

confidence: 99%

“…All the above techniques are based mainly on two basic approaches: a Machine Learning approach and a knowledge engineering approach [11]. The knowledge engineering approach is called rule-based filtering, which creates a set of rules by using rule-based spam filtering tools or by some other authority [12].…”

Section: Introductionmentioning

confidence: 99%

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

et al. 2021

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A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.

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“…The performed research showed that the NB gave the best results, but expressed a limitation due to classconditional independence. Gibson et al [19] analyzed machine-learning algorithms that are optimized with bio-inspired methods. They implemented Multinominal Naïve Bayes (MNB), SVM, RF, DT, and Multilayer Perceptron algorithms which were tested on seven different e-mail datasets: Lingspam, PUA, PU1, PU2, PU3, Enron, and SpamAssassin.…”

Section: Related Workmentioning

confidence: 99%

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

2021

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The paper elaborates on how text analysis influences classification—a key part of the spam-filtering process. The authors propose a multistage meta-algorithm for checking classifier performance. As a result, the algorithm allows for the fast selection of the best-performing classifiers as well as for the analysis of higher-dimensionality data. The last aspect is especially important when analyzing large datasets. The approach of cross-validation between different datasets for supervised learning is applied in the meta-algorithm. Three machine-learning methods allowing a user to classify e-mails as desirable (ham) or potentially harmful (spam) messages were compared in the paper to illustrate the operation of the meta-algorithm. The used methods are simple, but as the results showed, they are powerful enough. We use the following classifiers: k-nearest neighbours (k-NNs), support vector machines (SVM), and the naïve Bayes classifier (NB). The conducted research gave us the conclusion that multinomial naïve Bayes classifier can be an excellent weapon in the fight against the constantly increasing amount of spam messages. It was also confirmed that the proposed solution gives very accurate results.

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Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Cited by 57 publications

References 17 publications

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI

Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System

A Method for Fast Selection of Machine-Learning Classifiers for Spam Filtering

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