Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Feature Engineering vs Feature Selection vs Hyperparameter Optimization in the Spotify Song Popularity Dataset

Research in Featuring Engineering has been part of the data pre-processing phase of machine learning projects for many years. It can be challenging for new people working with machine learning to understand its importance along with various approaches to find an optimized model. This work uses the Spotify Song Popularity dataset to compare and evaluate Feature Engineering, Feature Selection and Hyperparameter Optimization. The result of this work will demonstrate Feature Engineering has a greater effect on model efficiency when compared to the alternative approaches

COMPARISON OF MACHINE LEARNING TECHNIQUES IN SPAM E-MAIL CLASSIFICATION

E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails is an important problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques in classification of spam e-mails. Random Forest (RF), C4.5 decision tree and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset from HP Labs, indicating that ensemble methods may have an edge in spam detectio

A Competent Approach for Type of Phishing Attack Detection Using Multi-Layer Neural Network

With the enlargement of contemporary technologies and the large-scale global computer networks web-attacks are escalating because of emergent curiosity of people and lawful institutions towards internet. Phishing is one of web-attack carried out by attacker using both social and technical engineering. Generally on web more attacks are launched every month with seek of crafting web addict to consider that they are contacting with a legalized entity for the intention of embezzle identity information, logon records and account details. The phishing attack detection and classification methods are utilized for the prevention and in-depth analysis of the attacks. In this paper, the proposed model has been designed with the multi-directional feature analysis along with the Back-Propagation Probabilistic neural network (BP-PNN) classification. The proposed model has performed better in the terms of the accuracy in all of the domains based upon the attack detection and classification

A hybrid semantic similarity feature-based to support multiple ontologies

Pembelajaran Berasaskan Kerja (PBK) merupakan satu kaedah pembelajaran yang menggabungkan pembelajaran teori dan amali secara serentak dalam lapangan kerja sebenar, dengan tujuan untuk melahirkan graduan yang memiliki nilai kebolehkerjaan. Walaupun kaedah ini telah lama dilaksanakan di negara maju seperti Amerika Syarikat dan United Kingdom, tetapi di Malaysia ianya baru dilaksanakan pada tahun 2007 dan hanya melibatkan beberapa buah kolej komuniti pada peringkat awal. Walau bagaimanapun pada tahun 2010, pelaksanaan PBK telah dihentikan di kolej komuniti, dan dipindahkan di politeknik. Antara isu yang berlaku dalam pelaksanaan PBK politeknik semasa dalam industri ialah konsep pelaksanaan PBK, gaya pengajaran dan pembelajaran, kaedah penilaian, hubungan politeknik dengan industri, keseragaman konsep pelaksanaan PBK, isu dan cabaran dalam pelaksanaan PBK, dan perbezaan kaedah pelaksanaan PBK antara politeknik dengan kolej komuniti. Oleh itu, tujuan kajian ini dijalankan ialah untuk meneroka, memahami dan menjelaskan pelaksanaan PBK politeknik bersama industri. Kajian ini dijalankan menggunakan metodologi kajian kes kualitatif. Proses pengumpulan data di lapangan kajian dilaksanakan selama setahun menggunakan tek:nik temubual, pemerhatian dan analisis dokumen. Strategi persampelan variasi maksima, teknik persampelan snowball dan jenis persampelan bertujuan digunakan. Peserta kajian adalah daripada kalangan pengurusan dan pensyarah penyelaras PBK, penyelia industri dan pelajar yang terlibat dengan PBK. Dapatan kajian menunjukkan bahawa pelaksanaan PBK politeknik bersama industri berlaku banyak penambahbaikan dalam pelaksanaannya jika dibandingkan dengan pelaksanaan PBK di kolej komuniti sebelum ini, namun terdapat beberapa isu yang wujud, iaitu melibatkan kurikulum PBK yang tidak selari dengan dasar industri dan kelemahan penyelia industri dalam pengajaran dan pembelajaran

A Review on Malicious URL Detection using Machine Learning Systems

Malicious web sites pretendsignificant danger to desktop security and privacy.These links become instrumental in giving partial or full system control to the attackers. This results in victim systems, which get easily infected and, attackers can utilize systems for various cyber-crimes such as stealing credentials, spamming, phishing, denial-of-service and many more such attack. Detection of such website is difficult because of thephishing campaigns and the efforts to avoid blacklists.To look for malicious URLs, the first step is usually to gather URLs that are liveon the Internet. There are various stages to detect this URLs such as collection of dataset, extracting feature using different feature extraction techniques and Classification of extracted feature. This paper focus on comparative analysis of malicious URL detection techniques