Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

https://www.mdpi.com/2078-2489/11/6/312/pdf

Malicious Text Identification: Deep Learning from Public Comments and Emails

Istenmeyen elektronik postalar aliciya rizasi disinda gonderilen ve genellikle kotu niyetli veya tanitim amacli olan kisilerin basvurdugu bir yontemdir. Elektronik postalar, kullaniminin kolayligi, maliyetlerinin ucuz olmasindan dolayi propaganda, reklam, oltalama yapmak isteyen kisi veya topluluklar tarafindan etkin bir bicimde kullanilmaktadir. Amaclarini gerceklestirmek isteyen kisi veya topluluklar hic tanimadiklari e-posta hesaplarina gereksiz ve istenmeyen postalar gonderirler. Bu calismada, istenmeyen elektronik postalarin filtrelenmesi icin literaturde bulunan yontemler incelenmistir. Bu istenmeyen e-posta filtreleme yontemleri temel olarak yapay zekâ tabanli olmayan ve yapay zekâ tabanli olan seklinde iki ana baslik altinda incelenmistir. Yapay zekâ tabanli olmayan yontemlerin istenmeyen e-posta tespitinde etkili sonuclar verdigi ancak literaturde bu yontemleri atlayabilen tekniklerin oldugu gorulmektedir. Istenmeyen e-posta tespitinde yapay zekâ tabanli makine ogrenmesi algoritmalari kullanan sistemlerin popularitesinin arttigi ve arastirmalarin bu yonde ivme kazandigi gorulmektedir. Ozellikle derin ogrenme yontemleri yuksek performanslari nedeniyle spam tespitinde tercih edilmeye baslamistir. Literaturde klasik makine ogrenme yontemlerinden olan Bayes, Destek Vektor Makinesi, Yapay Sinir Agi, Rastgele Orman, Cok Katmanli Algilayici, K-En Yakin Komsu gibi algoritmalarinin kullanildigi spam tespit yontemlerinde yuksek basarim sagladigi gorulmektedir. Uzun Kisa Sureli Bellek ve Evrisimsel Sinir Agi algoritmalarini kullanan derin ogrenme temelli spam tespit yontemlerinin basarim oranlarini daha da artirdigi farkli veri kumeleri kullanilarak gosterilmistir. Ayrica spam tespit sistemlerinde bulunan acik problemler ve Turkce ozelinde bu calismalarin hangi asamada oldugu da bu calismada irdelenmistir ve cesitli oneriler yapilmistir.

/pdf/istenmeyen-epostalarin-tespiti-icin-kullanilan-yontemlerin-4o1nh84wy8.pdf

İstenmeyen Epostaların Tespiti için Kullanılan Yöntemlerin İncelenmesi

基于深度神经网络的胎儿体重预测 (Estimation of Fetal Weight Based on Deep Neural Network).

1Harbin Engineering University, Harbin , China, 150001 2Harbin Engineering University, Harbin , China, 150001 3Northeast Forestry University, Harbin , China, 150001 4Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China,100093 5Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China,100093 6Institute of Information Engineering,Chinese Academy of Sciences,Beijing,China,100093 a569093011@qq.com,b26407166@qq.com, c stdong0451@163.com, d2443532866@qq.com, e330227346@qq.com, f272523869@qq.com

/pdf/research-of-a-spam-filter-based-on-improved-naive-bayes-2ez2n5hzaf.pdf

Research of a Spam Filter based on Improved Naive Bayes

As one of the most pervasive current modes of communication, email needs to be fast and reliable. However, spammers and attackers use it as a primary channel to conduct illegal activities. Although many approaches have been developed and evaluated for spam detection, they do not provide sufficient accuracy. This deficiency results in significant economic losses for organizations. In this article, we first propose a framework for creating novel spam filters using Keras to combine a Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) classification models. We then use this framework to introduce a specific solution applicable to realistic scenarios involving dynamic incoming email data in real-time. This solution takes the form of a real-time content-based spam classifier. We evaluate its performance concerning accuracy, precision, recall, false-positive, and false-negative rates. Our experimental results show that our approach can significantly outperform existing solutions for real-time spam detection.

A Novel Real-time Anti-spam Framework

The effective distinction between normal email and spam, so as to maximize the possible of filtering spam has become a research hotspot currently. Naive bayes algorithm is a kind of frequently-used email classification and it is a statistical-based classification algorithm. It assumes that the attributes are independent of each other when given the target value. This hypothesis is apparently impossible in the email classification, so the accuracy of email classification based on naive bayes algorithm is low. In allusion to the problem of poor accuracy of email classification based on naive bayes algorithm, scholars have proposed some new email classification algorithms. The email classification algorithm based on deep neural network is one kind of them. The deep neural network is an artificial neural network with full connection between layer and layer. The algorithm extracted the email feature from the training email samples and constructed a DNN with multiple hidden layers, the DNN classifier was generated by training samples, and finally the testing emails were classified, and they were marked whether they were spam or not. In order to verify the effect of the email classification algorithm based on DNN, in this paper we constructed a DNN with 2 hidden layers. The number of nodes in each hidden layer was 30. When the training set was trained, we set up 2000 batches, and each batch has 3 trained data. We used the famous Spam Base dataset as the data set. The experiment result showed that DNN was higher than naive Bayes in the accuracy of email classification when the proportion of the training set was 10%, 20%, 30%, 40% and 50% respectively, and DNN showed a good classification effect. With the development of science and technology, spam manifests in many forms and the damage of it is more serious, this puts forward higher requirements for the accuracy of spam recognition. The focus of next research will be combining various algorithms to further improve the effect of email classification. Keywords-Deep Neural Networks; Spam Email; Classification; Naive Bayes; SpamBase Data Set

/pdf/research-of-email-classification-based-on-deep-neural-1027a2n30k.pdf

Research of Email Classification based on Deep Neural Network

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Research of Email Classification based on Deep Neural Network