Malicious Domain Detection Based on Machine Learning
نویسندگان
چکیده
منابع مشابه
Malicious JavaScript detection using machine learning
JavaScript has become a ubiquitous Web technology that enables interactive and dynamic Web sites. The widespread adoption, along with some of its properties allowing authors to easily obfuscate their code, make JavaScript an interesting venue for malware authors. In this survey paper, we discuss some of the difficulties in dealing with malicious JavaScript code, and go through some recent appro...
متن کاملMalicious 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 کاملEarly detection of malicious web content with applied machine learning
This thesis explores the use of applied machine learning techniques to augment traditional methods of identifying and preventing web-based attacks. Several factors complicate the identification of web-based attacks. The first is the scale of the web. The amount of data on the web and the heterogeneous nature of this data complicate efforts to distinguish between benign sites and attack sites. S...
متن کاملA Hybrid Malicious Code Detection Method based on Deep Learning
In this paper, we propose a hybrid malicious code detection scheme based on AutoEncoder and DBN (Deep Belief Networks). Firstly, we use the AutoEncoder deep learning method to reduce the dimensionality of data. This could convert complicated high-dimensional data into low dimensional codes with the nonlinear mapping, thereby reducing the dimensionality of data, extracting the main features of t...
متن کاملBeam Detection Based on Machine Learning Algorithms
The positions of free electron laser beams on screens are precisely determined by a sequence of machine learning models. Transfer training is conducted in a selfconstructed convolutional neural network based on VGG16 model. Output of intermediate layers are passed as features to a support vector regression model. With this sequence, 85.8% correct prediction is achieved on test data.
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: DEStech Transactions on Computer Science and Engineering
سال: 2018
ISSN: 2475-8841
DOI: 10.12783/dtcse/iceit2017/19866