The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail.

http://journal.portalgaruda.org/index.php/EEI/article/download/1922/1381

Review of the machine learning methods in the classification of phishing attack

Phishing is a serious cybersecurity problem, which is widely available through multimedia, such as e-mail and Short Messaging Service (SMS) to collect the personal information of the individual. However, the rapid growth of the unsolicited and unwanted information needs to be addressed, raising the necessity of the technology to develop any effective anti-phishing methods.,The primary intention of this research is to design and develop an approach for preventing phishing by proposing an optimization algorithm. The proposed approach involves four steps, namely preprocessing, feature extraction, feature selection and classification, for dealing with phishing e-mails. Initially, the input data set is subjected to the preprocessing, which removes stop words and stemming in the data and the preprocessed output is given to the feature extraction process. By extracting keyword frequency from the preprocessed, the important words are selected as the features. Then, the feature selection process is carried out using the Bhattacharya distance such that only the significant features that can aid the classification are selected. Using the selected features, the classification is done using the deep belief network (DBN) that is trained using the proposed fractional-earthworm optimization algorithm (EWA). The proposed fractional-EWA is designed by the integration of EWA and fractional calculus to determine the weights in the DBN optimally.,The accuracy of the methods, naive Bayes (NB), DBN, neural network (NN), EWA-DBN and fractional EWA-DBN is 0.5333, 0.5455, 0.5556, 0.5714 and 0.8571, respectively. The sensitivity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.4558, 0.5631, 0.7035, 0.7045 and 0.8182, respectively. Likewise, the specificity of the methods, NB, DBN, NN, EWA-DBN and fractional EWA-DBN is 0.5052, 0.5631, 0.7028, 0.7040 and 0.8800, respectively. It is clear from the comparative table that the proposed method acquired the maximal accuracy, sensitivity and specificity compared with the existing methods.,The e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion of the historical data in the detection process enhances the accuracy of detection.

An optimization-based deep belief network for the detection of phishing e-mails

From the previous work related to web phishing, the researchers overlook the imbalanced class problem on the dataset. theoretically, the majority of classification methods would assume that the nature of the class distribution is balanced. It caused the classification’s performance of the method will be declining. Therefore, the mechanism of imbalanced class handling is severely needed. In our study, One SidedSelection and Synthetic Minority Over-Sampling Technique are used to handle the imbalanced class condition. Those algorithms work to balancing the class distribution of the dataset so that the accuracy and the gmean score of the classification will be enhanced. Based on the result, the combination of those methods (OSS and SMOTE) can enhance the classification’s result significantly either on binary type class and multiclass type dataset. Hence, the combination of OSS and SMOTE can be a plausible option to handle the imbalanced class problem on the web phishing classification either on binary class and multiclass datasets.

Hybrid Resampling for Imbalanced Class Handling on Web Phishing Classification Dataset

Now-a-days, social engineering is considered to be one of the most overwhelming threats in the field of cyber security. Social engineers, who deceive people by using their personal appeal through cunning communication, do not rely on finding the vulnerabilities to break into the cyberspace as traditional hackers. Instead, they make shifty communication with the victims that often enable them to gain confidential information like their credentials to compromise cyber security. Phishing attack has become one of the most commonly used social engineering methods in daily life. Since the attacker does not rely on technical vulnerabilities, social engineering, especially phishing attacks cannot be tackled using cyber security tools like firewalls, IDSs (Intrusion Detection Systems), etc. What is more, the increased popularity of the social media has further complicated the problem by availing abundance of information that can be used against the victims. The objective of this paper is to propose a new framework that characterizes the behavior of the phishing attack, and a comprehensive model for describing awareness, measurement and defense of phishing based attacks. To be specific, we propose a hybrid multi-layer model using Natural Language Processing (NLP) techniques for defending against phishing attacks. The model enables a new prospect in detection of a potential attacker trying to manipulate the victim for revealing confidential information.

https://www.ijcnis.org/index.php/ijcnis/article/download/2991/259

Innovations of Phishing Defense: The Mechanism, Measurement and Defense Strategies

Web phishing is one of the many crimes that occur in cyberspace and often threatens internet users around the world. Web phishing works by tricking the victim into a website page that has been designed to resemble the original page and then directing the target to submit the important information they have. Web phishing detection system needs to be developed to minimize attacks and theft of information using the website. Research related to web phishing detection system has been carried out by many researchers, one of them using data mining techniques, but still uses a single classification algorithm. Therefore, the addition of meta-algorithm is proposed to support the improvement of classification performance for the development of various web phishing detection systems. From the testing phase that conducted using Web Phishing dataset from UCI Machine Learning Repository, an increase in accuracy value of 97.1% is obtained by the addition of the bagging process, 97.3% by using the boosting process, and 97.5% by the addition of the stacking process. With the resulting improved performance, it is hoped that the model can be used as a reference in perfecting the development of various phishing web detection systems.

Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process

Phishing is a criminal scheme to steal the user's personal data and other credential information. It is a fraud that acquires victim's confidential information such as password, bank account detail, credit card number, financial username and password etc. and later it can be misuse by attacker. We aim to use fundamental visual features of a web page's appearance as the basis of detecting page similarities. We propose a novel solution, to efficiently detect phishing web pages. Note that page layouts and contents are fundamental feature of web pages' appearance. Since the standard way to specify page layouts is through the style sheet (CSS), we develop an algorithm to detect similarities in key elements related to CSS. In this paper, we proposed a system that uses SVM technique along with map-reduce paradigm to achieve a higher accuracy in detection of the spam email. By using the map-reduce technique we also try to overcome the two hurdles of the SVM.

Detecting spam and phishing mails using SVM and obfuscation URL detection algorithm

Spam Email, also known as junk email , is a subset of electronic spam involving nearly identical messages sent to numerous recipients by email. The messages may contain disguised links that appear to be for familiar websites but in fact lead to phishing web sites or sites that are hosting malware. Spam email may also include malware as scripts or other executable file attachments. Spam is any unwanted and harmful mail. Separation of spam from normal mails is essential. This paper surveys different spam email filtering techniques. The different techniques are Machine learning based, list based, content based and hybrid or other. Machine learning based, is mostly used because of high accuracy and mathematical support. Keywords—Spam filtering techniques, Machine learning based ,content based, word based.

Rashmi A. Rane

Papers

Detecting spam and phishing mails using SVM and obfuscation URL detection algorithm

Survey on Spam Filtering Techniques and Mapreduce