Spam/ham e-mail classification using machine learning methods based on bag of words technique

Sahin, Esra; Aydos, Murat; Orhan, Fatih

doi:10.1109/siu.2018.8404347

Cited by 15 publications

(7 citation statements)

References 4 publications

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“…Surface forms consider that each document is represented as a set of terms and their frequency in the document. These models make use of approaches, such as Bag Of Words (BOW), n-grams , or Part-of-Speech tagging (POS), and have worked remarkably well for many NLP problems [23,44]. However, they are not capable of explaining the word semantics.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Monitoring Hate Speech in Twitter

Pereira-Kohatsu

Quijano-Sánchez

Liberatore

et al. 2019

Sensors

144

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Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.

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Section: Related Workmentioning

confidence: 99%

Detecting and Monitoring Hate Speech in Twitter

Pereira-Kohatsu

Quijano-Sánchez

Liberatore

et al. 2019

Sensors

144

View full text Add to dashboard Cite

show abstract

“…They propose a new filtration technique to reduce false alarms. Sahın et al [23] evaluate the spam classification performance of different machine learning methods combined with the bag of words technique.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, these datasets can represent the typical inputs for machine learning classifiers in cybersecurity. The machine learning algorithms which are widely used in cybersecurity [1]- [3], [16]- [23] are employed to build up the target detection systems, i.e., LR, DT, MLP, NB, and RF. We regard these three scenarios as binary classification problems.…”

Section: Experiments and Analysismentioning

confidence: 99%

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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

2020

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Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples. This poses new threats to the security-critical applications in cybersecurity. Currently, there is still a short of study on adversarial examples in the domain of cybersecurity. In this paper, we propose a new method known as the brute-force attack method to better evaluate the robustness of the machine learning classifiers in cybersecurity against adversarial examples. The proposed method, which works in a black-box way and covers some shortages of the existing adversarial attack methods based on generative adversarial networks, is simple to implement and only needs the output of the target classifiers to generate adversarial examples. To have a comprehensive evaluation of the attack performance of the proposed method, we use our method to generate adversarial examples against the common machine learning based security systems in cybersecurity including host intrusion detection systems, Android malware detection systems, and network intrusion detection systems. We compare the attack performance of the proposed method against these security systems with that of state-of-the-art adversarial attack methods based on generative adversarial networks. The preliminary experimental results show that the proposed method, which is more efficient in computation and outperforms the state-of-the-art attack methods based on generative adversarial networks, can be used to evaluate the robustness of various machine learning based systems in cybersecurity against adversarial examples. INDEX TERMS Adversarial examples, machine learning, deep learning, intrusion detection, malware detection, neural networks, black-box method.

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“…Spam classification helps us to filter the unwanted emails from the email Inbox. There have been various attempts to classify the spam email based on using email header [20], [21], [5], [36], [37], [38], [13], [4], using email body [3], [41], [35], [29], [27], [30], [7], [31], [32], [33], [34] and also using both body and header [18], [23], [21], [15], [42] and statistical features [19], [25]. The email header classification is performed using techniques such as Naïve Bayes (NB), Decision Tree (DT) [40] [43], and Support Vector Machine (SVM) [23], [24], [20], [13], [26] Random Forest (RF) [4], [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of Header-based Features on Accuracy of Classifiers for Spam Email Classification

Kulkarni¹,

Jatinderkumar²,

Acharya³

2020

IJACSA

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Emails are an integral part of communication in today's world. But Spam emails are a hindrance, leading to reduction in efficiency, security threats and wastage of bandwidth. Hence, they need to be filtered at the first filtering station, so that employees are spared the drudgery of handling them. Most of the earlier approaches are mainly focused on building content-based filters using body of an email message. Use of selected header features to filter spam, is a better strategy, which was initiated by few researchers. In this context, our research intends to find out minimum number of features required to classify spam and ham emails. A set of experiments was conducted with three datasets and five Feature Selection techniques namely Chi-square, Correlation, Relief Feature Selection, Information Gain, and Wrapper. Five-classification algorithms-Naïve Bayes, Decision Tree, NBTree, Random Forest and Support Vector Machine were used. In most of the approaches, a trade-off exists between improper filtering and number of features. Hence arriving at an optimum set of features is a challenge. Our results show that in order to achieve the objective of satisfactory filtering, minimum 5 and maximum 14 features are required.

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Spam/ham e-mail classification using machine learning methods based on bag of words technique

Cited by 15 publications

References 4 publications

Detecting and Monitoring Hate Speech in Twitter

Detecting and Monitoring Hate Speech in Twitter

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

Effect of Header-based Features on Accuracy of Classifiers for Spam Email Classification

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