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Found 17 papers, 2 papers with code
Feature Analysis of Encrypted Malicious Traffic
In this paper, we apply three machine learning techniques to the problem of distinguishing malicious encrypted HTTP traffic from benign encrypted traffic and obtain results comparable to previous work.
Hidden Markov Models with Random Restarts vs Boosting for Malware Detection
One machine learning technique that has been used widely in the field of pattern matching in general-and malware detection in particular-is hidden Markov models (HMMs).
Creating Valid Adversarial Examples of Malware
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results.
Classification and Online Clustering of Zero-Day Malware
Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families.
Multifamily Malware Models
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset.
Hidden Markov Models with Momentum
In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models.
Generative Adversarial Networks and Image-Based Malware Classification
We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection.
Convolutional Neural Networks for Image Spam Detection
In this research, we consider the problem of image spam detection, based on image analysis.
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis.
Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication
Although prior research has shown that deep learning models are vulnerable to adversarial attacks, relatively little research has been done in the adversarial domain for behavioral biometrics.
Clickbait Detection in YouTube Videos
YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video.
Sentiment Analysis for Troll Detection on Weibo
We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies.
Malware Classification Using Long Short-Term Memory Models
We find that a model consisting of word embedding, biLSTMs, and CNN layers performs best in our malware classification experiments.
Malware Classification with Word Embedding Features
Malware classification is an important and challenging problem in information security.
Transfer Learning for Image-Based Malware Classification
In this paper, we consider the problem of malware detection and classification based on image analysis.
A Comparative Analysis of Android Malware
In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features.
Malware Detection Using Dynamic Birthmarks
In this paper, we explore the effectiveness of dynamic analysis techniques for identifying malware, using Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), both trained on sequences of API calls.
