Suspicious URL Detection using Dynamic Learning Model with Machine Learning
نویسندگان
چکیده
منابع مشابه
Malicious URL Detection using Machine Learning: A Survey
Malicious URL, a.k.a. malicious website, is a common and serious threat to cybersecurity. Malicious URLs host unsolicited content (spam, phishing, drive-by exploits, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such th...
متن کاملA Review on Phishing URL Detection using Machine Learning Systems
Seeking sensitive user data in the form of online banking user-id and passwords or credit card information, which may then be used by ‘phishers’ for their own personal gain is the primary objective of the phishing e-mails. With the increase in the online trading activities, there has been a phenomenal increase in the phishing scams which have now started achieving monstrous proportions. This pa...
متن کاملEmotion Detection in Persian Text; A Machine Learning Model
This study aimed to develop a computational model for recognition of emotion in Persian text as a supervised machine learning problem. We considered Pluthchik emotion model as supervised learning criteria and Support Vector Machine (SVM) as baseline classifier. We also used NRC lexicon and contextual features as training data and components of the model. One hundred selected texts including pol...
متن کاملURLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly generated malicious URLs. To address this, recent years have witnessed several efforts to perform Malicious URL Detection using Machine Learning. The mo...
متن کاملMelanoma detection with a deep learning model
Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions. Methods: In this analytic s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal for Research in Applied Science and Engineering Technology
سال: 2019
ISSN: 2321-9653
DOI: 10.22214/ijraset.2019.7214