scholarly journals HinPhish: An Effective Phishing Detection Approach Based on Heterogeneous Information Networks

2021 ◽  
Vol 11 (20) ◽  
pp. 9733
Author(s):  
Bingyang Guo ◽  
Yunyi Zhang ◽  
Chengxi Xu  ◽  
Fan Shi ◽  
Yuwei Li  ◽  
...  
Keyword(s):  
Sensitive Information ◽  
Target Domain ◽  
Bank Account ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Internet Users ◽  
Detection Approach ◽  
Long Time ◽  
Website Content ◽  
Phishing Detection

Internet users have suffered from phishing attacks for a long time. Attackers deceive users through malicious constructed phishing websites to steal sensitive information, such as bank account numbers, website usernames, and passwords. In recent years, many phishing detection solutions have been proposed, which mainly leverage whitelists or blacklists, website content, or side channel-based techniques. However, with the continuous improvement of phishing technology, current methods have difficulty in achieving effective detection. Hence, in this paper, we propose an effective phishing website detection approach, which we call HinPhish. HinPhish extracts various link relationships from webpages and uses domains and resource objects to construct a heterogeneous information network. HinPhish applies a modified algorithm to leverage the characteristics of different link types in order to calculate the phish-score of the target domain on the webpage. Moreover, HinPhish not only improves the accuracy of detection, but also can increase the phishing cost for attackers. Extensive experimental results demonstrate that HinPhish can achieve an accuracy of 0.9856 and F1-score of 0.9858 .

scholarly journals Feature based Phishing Website Detection using Random Forest Classifier

2021 ◽  
Vol 9 (VI) ◽  
pp. 1902-1906
Author(s):  
Sonali Kadam
Keyword(s):  
Machine Learning ◽  
Random Forest Classifier ◽  
The Internet ◽  
Classification Algorithms ◽  
Sensitive Information ◽  
Security Threat ◽  
Uniform Resource Locator ◽  
Internet Users ◽  
Feature Based ◽  
Phishing Detection

In today’s world, one of the most vulnerable security threat which poses a problem to the internet users is phishing. Phishing is an attack made to steal the sensitive information of the users such as password, PIN, card details etc., In a phishing attack, the attacker creates a fake website to make the users click it and steal the sensitive information of users. . In this paper, we propose a feature-based phishing detection technique that uses uniform resource locator (URL) features. This paper focuses on the extracting the features which are then classified based on their effect within a website. The feature groups include address- bar related features, abnormal- based features, HTML – JavaScript based features and domain based features. We plan to use machine learning and implement some classification algorithms and compare the performance of these algorithms on our dataset.

scholarly journals PDRCNN: Precise Phishing Detection with Recurrent Convolutional Neural Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Weiping Wang ◽  
Feng Zhang ◽  
Xi Luo ◽  
Shigeng Zhang
Keyword(s):  
Third Party ◽  
Detection Accuracy ◽  
Web Pages ◽  
Sensitive Information ◽  
Global Features ◽  
Detection Approach ◽  
Low Efficiency ◽  
Lstm Network ◽  
Deep Learning Neural Network ◽  
Phishing Detection

Through well-designed counterfeit websites, phishing induces online users to visit forged web pages to obtain their private sensitive information, e.g., account number and password. Existing antiphishing approaches are mostly based on page-related features, which require to crawl content of web pages as well as accessing third-party search engines or DNS services. This not only leads to their low efficiency in detecting phishing but also makes them rely on network environment and third-party services heavily. In this paper, we propose a fast phishing website detection approach called PDRCNN that relies only on the URL of the website. PDRCNN neither needs to retrieve content of the target website nor uses any third-party services as previous approaches do. It encodes the information of an URL into a two-dimensional tensor and feeds the tensor into a novelly designed deep learning neural network to classify the original URL. We first use a bidirectional LSTM network to extract global features of the constructed tensor and give all string information to each character in the URL. After that, we use a CNN to automatically judge which characters play key roles in phishing detection, capture the key components of the URL, and compress the extracted features into a fixed length vector space. By combining the two types of networks, PDRCNN achieves better performance than just using either one of them. We built a dataset containing nearly 500,000 URLs which are obtained through Alexa and PhishTank. Experimental results show that PDRCNN achieves a detection accuracy of 97% and an AUC value of 99%, which is much better than state-of-the-art approaches. Furthermore, the recognition process is very fast: on the trained PDRCNN model, the average per URL detection time only cost 0.4 ms.

scholarly journals A Case-Based Reasoning Approach for Automatic Adaptation of Classifiers in Mobile Phishing Detection

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
San Kyaw Zaw ◽  
Sangsuree Vasupongayya
Keyword(s):  
Concept Drift ◽  
Case Based Reasoning ◽  
Sensitive Information ◽  
Design Decision ◽  
Detection Techniques ◽  
Android Apps ◽  
Detection Approach ◽  
Proposed Model ◽  
Phishing Detection ◽  
Case Based

Currently, the smartphone contains lots of sensitive information. The increasing number of smartphone usage makes it more interesting for phishers. Existing phishing detection techniques are performed on their specific features with selected classifiers to get their best accuracy. An effective phishing detection approach is required to adapt the concept drift of mobile phishing and prevent degradation in accuracy. In this work, an adaptive phishing detection approach based on case-based reasoning technique is proposed to handle the concept drift challenge in phishing apps. Several experiments are conducted in order to demonstrate the design decision of our proposed model. The proposed model is evaluated with a large feature set containing 1,065 features from 10 different categories. These features are extracted from more than 10,000 android applications. Five combinations of features are created in order to mimic new real-world Android apps to evaluate our experiments. Moreover, a reduced feature set is also studied in this work in order to improve the efficiency of the proposed model. Both accuracy and efficiency of the proposed model are evaluated. The experimental results show that our proposed model achieves acceptable accuracy and efficiency for the phishing detection.

Penerapan Strategi Social Media Marketing Untuk Meningkatkan Penjualan Pada Dealer Mobil Toyota, Daihatsu dan Honda Bandung

Infoman s ◽  
2018 ◽  
Vol 12 (2) ◽  
pp. 115-124
Author(s):  
Yopi Hidayatul Akbar ◽  
Muhammad Agreindra Helmiawan
Keyword(s):  
Social Media ◽  
Search Engine ◽  
Social Media Marketing ◽  
Public Attention ◽  
Media Companies ◽  
Social Media Networks ◽  
Internet Users ◽  
Marketing Activities ◽  
The University ◽  
Website Content

Social media is one of the information media that is currently widely used by several companies and personally to convey information, with the presence of social media companies no longer need to spread offers through print media, they can use information technology tools in this case social media to submit offers the products they sell to users globally through social media. This social media marketing technique is the process of reaching visits by internet users to certain sites or public attention through social media sites. Marketing activities using social media are usually centered on the efforts of a company to create content that attracts attention, thus encouraging readers to share the content through their social media networks. The application of the QMS method is certainly not only submitted through search engine webmasters, but also on a website keywords must be applied that relate to the contents of the website content, because with the keyword it will automatically attract visitors to the university website based on keyword phrases that they type in the search engine. With Search Media Marketing Technique (SMM) is one of the techniques that must be applied in conducting sales promotions, especially in car dealers in Bandung, it is considered important because each product requires price, feature and convenience socialization through social media so that sales traffic can increase. Each dealer should be able to apply the techniques of Social Media Marketing (SMM) well so that car sales can reach the expected target and provide profits for sales as car sellers in the field.

scholarly journals Measuring diversity in heterogeneous information networks

2021 ◽  
Vol 859 ◽  
pp. 80-115
Author(s):  
Pedro Ramaciotti Morales ◽  
Robin Lamarche-Perrin ◽  
Raphaël Fournier-S'niehotta ◽  
Rémy Poulain ◽  
Lionel Tabourier ◽  
...  
Keyword(s):  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Measuring Diversity

scholarly journals A Comparative Analysis of Arabic Text Steganography

2021 ◽  
Vol 11 (15) ◽  
pp. 6851
Author(s):  
Reema Thabit ◽  
Nur Izura Udzir ◽  
Sharifah Md Yasin ◽  
Aziah Asmawi ◽  
Nuur Alifah Roslan ◽  
...  
Keyword(s):  
Evaluation Criteria ◽  
Arabic Language ◽  
Text Messages ◽  
Secret Message ◽  
Sensitive Information ◽  
Arabic Text ◽  
Ideal Object ◽  
Internet Users ◽  
Text Steganography ◽  
Text Content

Protecting sensitive information transmitted via public channels is a significant issue faced by governments, militaries, organizations, and individuals. Steganography protects the secret information by concealing it in a transferred object such as video, audio, image, text, network, or DNA. As text uses low bandwidth, it is commonly used by Internet users in their daily activities, resulting a vast amount of text messages sent daily as social media posts and documents. Accordingly, text is the ideal object to be used in steganography, since hiding a secret message in a text makes it difficult for the attacker to detect the hidden message among the massive text content on the Internet. Language’s characteristics are utilized in text steganography. Despite the richness of the Arabic language in linguistic characteristics, only a few studies have been conducted in Arabic text steganography. To draw further attention to Arabic text steganography prospects, this paper reviews the classifications of these methods from its inception. For analysis, this paper presents a comprehensive study based on the key evaluation criteria (i.e., capacity, invisibility, robustness, and security). It opens new areas for further research based on the trends in this field.

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