Detecting Phishing Attacks using NLP

In present world phishing attacks are the most common and easily targeted attacks. In order to analyse texts and detect improper statements that show the phishing attacks, we have come with an idea which will use (NLP) Natural language processing techniques. Compared to previous work, our approach is different because the emphasis is on the text data found in the attack, which is semanticized in order to identify malicious intent. We have evaluated it using a huge phishing e-mail test data to illustrate the efficacy of our strategy.


Introduction
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Literature Survey
Many methods look at the URLs in the message [2]. Three methods for detecting phishing websites were explored in this paper suggested by Vaibhav Patil. The third solution is based on a visual appearances review to validate the validity of the site [2]. The downside to this technique is the identification of certain small false positives and false negative tests. In comparison, a third method includes evaluating the various features of URL.
G. Jaspher Willsie Kathrine evaluated several different detection approaches such as Heuristic based, and the technique used was decision tree algorithm and by his he helped in choosing the best approach to detect with least failure rate [1]. It consumes a lot of time as we need to check for each and every algorithm then come to final result.
Previous work of Ebubekir BUBER [4] has also employed syntactic parsing to infer malicious intent. It is seen as an algorithm which is better compared to previous ones and gives success rate of 89.9%. This Success rate can be improved further with a more efficient algorithm.
Muhammet Baykara [5] developed an Anti-phishing simulator is developed to detect the phishing attacks. It works on a simple logic on detecting attacks by comparing the mails and checking the URLs. [5] Major drawback of this system was that it works only to detect the URL induced mail type phishing attacks. Prasanta Kumar Sahoo [3] used Data mining algorithms to detect the fake E-mail using Naïve Bayesian classification. It was efficient because of its methodology in terms of complexity and overhead to detect phishing attacks. The drawback of this system was its incompatibility to work on different system and error rate.
In order to produce the best performance depending on the Sharma A. K criterion [6], a comparative study was performed of various spam filtering policies focused on specific roles in order to optimize the efficiency of the spam detection algorithms or various current data mining algorithms in its job.
Li et al [7] suggested an online learning approach to website identification. This article discusses website functionalities like site graphics and model database objects to refine the characteristics obtained from the site used as an evolutionary algorithm dependent on quantities. To define the website as valid or phishy, the configured functions are transferred through transductive help vector machine.

Proposed System
Our method carries out a semantical analysis of the attacker's text to validate the adequacy of each paragraph. Our method decides whether the sentence is a question or a order depending on the functions played by each word in the sentence. The potential topics of questions and commands will be collected by pairs. Every pair is then checked to see if it is included in a registry of malicious pairs. The program reads a text file one sentence at a time and returns true if a social manipulation attack is present in the record.
Identification of malicious questions and commands depends on the presence of a pair blacklist object (verbdirect object) which implies a malicious purpose to include in the application or order. We use machine learning to create a blacklist of the subject, constructing a decision tree that is designed for multiple distributed results. We also used Multinomial NB (the method implementing this algorithm) as the Scikit-Learn Python Library [17]. This algorithm produces a pre-setting label with any single pair that generates a predictive rating of trust. The size of the confidence levels is 0 and 1, with a score of 1 indicating certainty.

Module Description
Data assessment At Data Assessment, the data is analyzed one by one. Each variable in the data set is analyzed and then it is made as a proper data set for training purposes. This increases the accuracy and reliability of the data.
Pre-processing At Pre-Processing, an extensive evaluation is performed on standard benchmarks from text categorization and semantic analysis. This involves the steps of data cleaning, transformation and reduction.
Feature selection: At Feature selection, Decision Tree algorithm is performed and the emails are being classified into either 0 or 1 category based on the features of the text mail.
Prediction: At Prediction level, the classified mail is being checked with the listed dataset for the comparison and finally determined if the mail is malicious or not based on the threshold value percentage.  We used an email data collection to accurately test our approach to detect the positives and negatives. We have assembled a phishing email package that is publicly accessible. For the legal e-mail URL corpus we used Enron Corpus [8]. Some of the e-mails contained only pictures outside the images without text. We have missed phishing e-mails only with pictures and all 1780 remaining e-mails. We have evaluated the test corpus for comparison to the NLP algorithm, which only shows phish URL connections. In Python scripts our algorithm has been implemented and graphs are generated using R tools.

. Conclusion
The new framework helps internet users to browse safely and securely. This allows users to save valuable information which should not be leaked. It is much easier to remove our system if our proposed system is supplied in form of an extension to users. These findings demonstrate the efficiency that heuristic characteristics, visual characteristics and blacklist and white-list approach can achieve with a hybrid solution. We propose a method to detect phishing attacks on targeted emails. We are not dependent on metadata linked to emails but on text analysis. Our method is also successful in the detection of text-only phishing e-mails. Our findings on phishing e-mails provide substantially stronger warning that semantic knowledge is a good social manipulation predictor.