FS2RNN: Feature Selection Scheme for Web Spam Detection Using Recurrent Neural Networks

The recent increase in hacks and computer network attacks around the world has intensified the need to develop better intrusion detection and prevention systems. The intrusion detection system (IDS) plays a vital role in detecting anomalies and attacks on the network which have become larger and more pervasive in nature. However, most anomaly-based intrusion detection systems are plagued by high false positives. Furthermore, Remote-to-Local (R2L) and User-to-Root (U2R) are two kinds of attack which have low predicted accuracy scores in advance IDS methods. Therefore, this paper proposes a novel IDS framework to overcome these IDS problems. The proposed framework including three main parts. The first part is to build SFSDT model which is the feature selection model. SFSDT is to generate the best feature subset from the original feature set. This model is a hybrid Sequence Forward Selection (SFS) algorithm and Decision Tree (DT) model. The second part is to build various IDS models to train on the best-selected feature subset. The various Recurrent Neural Networks (RNN) are traditional RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Two IDS datasets are used for the learned models in experiments including NSL-KDD in 2010 and ISCX in 2012. The final part is to evaluate the proposed model by comparing the proposed models to other IDS models. The experimental results show the proposed models achieve significantly improved accuracy detection rate as well as attack types classification. Furthermore, this approach can reduce the computation time by memory profilers measurement.

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Web Spam Detection Using Transductive(Inductive Graph Neural Networks

Advances in Neural Networks: Computational and Theoretical Issues - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-18164-6_9 ◽

2015 ◽

pp. 83-91 ◽

Cited By ~ 2

Author(s):

Anas Belahcen ◽

Monica Bianchini ◽

Franco Scarselli

Keyword(s):

Neural Networks ◽

Spam Detection ◽

Web Spam ◽

Graph Neural Networks

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Spam Detection using Recurrent Neural Networks

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0305 ◽

2020 ◽

pp. 313-318

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Recurrent Neural Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Spam Detection ◽

Network Bandwidth ◽

Junk Mail ◽

Email Address

Spam is well defined as the unsolicited bulk messages or junk mail will send to email address or phone number that are generally marketable in nature and also carry malicious documents. The main issue of spam is that it can download malicious files which can attack the computers, smart phones and networks, utilize network bandwidth and storage space, degrades email servers and can cause attacks in our devices like spyware, phishing and ransomware. In the existing approach, an exploratory analysis of supervised machine learning algorithms has done and the performance has been evaluated. The drawback of existing approach is that the performance of supervised machine learning algorithms decreases as we increase the size of the dataset. In order to overcome such drawbacks, an efficient spam detection using recurrent neural networks using the BiGRU model has been proposed. By implementing this, it has been achieved with better accuracy of 99.07%. From this, it is concluded that BiGRU model has better performance than existing approaches.

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