A Spam Discrimination Based on Mail Header Feature and SVM

Miao, Yongwei; Tang, Tao; Fan-jin, Mai; Cheng, Xiaohui

doi:10.1109/wicom.2008.1139

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…Wang et al [16] also proposed the similar spam classification technique by using features of the sender and receiver address (To, CC, BCC), mail user agent and message-ID to train the system. Similar research by Hu et al [17], Wu et al [14], Ye et al [15] and Sheu [13] also classified spam email by using different features information such as the size of an email, time, length of sender-destination field, sender IP or email subject and others with machine learning.…”

Section: Related Researchmentioning

confidence: 83%

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Phomkeona

Okamura

2020

Journal of Information Processing

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Cyber hackers use email as a tool to trick, inject or drop malicious software into the recipient's device. Everyday users have to face off against, phishing or malicious emails and it would be a huge problem for whole organizations even if only one user clicked on a single link from this malicious email. The difficult issue is how to classify and detect those malicious emails from ordinary, especially spear phishing emails, which are designed for a particular target, or zero-day malicious emails that no one has ever found until now. In this paper, we introduce a way to classify and detect zero-day malicious emails by using deep-learning with data investigated from the email header and body itself, combined with dynamic analysis information as a group of features. Four different language email datasets can be used to train and test the system to simulate real-world diversity and zero-day malicious email attack situations. We succeeded in obtaining a satisfactory accuracy rate for detection results for both zero-day malicious email types and normal spam.

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Section: Related Researchmentioning

confidence: 83%

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Phomkeona

Okamura

2020

Journal of Information Processing

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“…Ye et al [16] offered a statistical analysis of junk and legitimate email header session messages, as well as the feasibility of using these messages to conduct spam filtering. The content present in 10024 emails of trash in the mail system was obtained from the database of spam archives and statistically analyzed.…”

Section: Related Workmentioning

confidence: 99%

“…The convolutional neural network (CNN) architecture was developed to solve the problems of the classic cost-related artificial neural network (ANN), time, number of parameters, and selected features. The most important benefits of the CNN model are: extracting the most relevant features, minimizing the number of parameters [18], training massive data, and decreasing the computation in the network [16]. The CNN model has achieved high performance in a several fields, such as image recognition.…”

Section: The Cnn Model For Image Datamentioning

confidence: 99%

Feature Extraction and Classification of Email Spam Detection Using IMTF-IDF+Skip-Thought Vectors

Mallampati¹,

Hedge²

2022

ISI

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Spam is a major concern in present emails, and there are several reasons for sending spam emails. The two most common ones are advertising and fraud. If supported by suitable preprocessing approaches, the detection algorithm for spam email or spam classifier will function effectively (removal of noise, removal of stop words, stemming, lemmatization, term frequency). Spam that combines both text and image components is referred to as hybrid spam. Compared to spam emails with images and text, it is more unsafe and complex. To distinguish spam or ham, we must use an effective and smart approach in order to have a strong representation of emails and improve classification performance. In this paper, we propose a multi-modal architecture relying on a feature model (MMA-FM) that concatenates two embedding vectors. The text and image sections of the similar emails were separated using a hybrid model (IMTF-IDF+Skip-thoughts) and the convolutional neural network (CNN) as a feature extraction technique. The extracted features are concatenated and given to Naïve Bayes (NB) and Support Vector Machine (SVM) models to classify hybrid email as either spam or ham. In this paper we used two hybrid datasets: Enron, Dredze, and TREC 2007, which are publicly accessible corpora. Our results show that the SVM model provides an accuracy of 99.16%, which is higher when compared to the Naïve Bayes method.

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“…There are four commonly used techniques for spam classification namely, a) Use of blacklist [14] b) Protocol-based approach c) Use of keywords or content filtering d) Header based [20], [28], [21], [5], [36], [13] In the first case, a list of email the network administrator maintains addresses or domain name databases. The classifier matches new record with blacklisted database and simply rejects some mails and puts them onto the spam folder.…”

Section: Literature Reviewmentioning

confidence: 99%

“…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]. When these techniques were adopted by the researchers using various features and datasets, Random Forest showed better performance than the other techniques.…”

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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A Spam Discrimination Based on Mail Header Feature and SVM

Cited by 12 publications

References 4 publications

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Zero-day Malicious Email Investigation and Detection Using Features with Deep-learning Approach

Feature Extraction and Classification of Email Spam Detection Using IMTF-IDF+Skip-Thought Vectors

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

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