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Found 42 papers, 4 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.
Social Media Bot Detection using Dropout-GAN
Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime.
On the Steganographic Capacity of Selected Learning Models
In this research, we consider the general question of the steganographic capacity of learning models.
A Comparison of Adversarial Learning Techniques for Malware Detection
Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks.
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).
A Natural Language Processing Approach to Malware Classification
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification.
Keystroke Dynamics for User Identification
In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data.
Classifying World War II Era Ciphers with Machine Learning
We also find that ciphers that are more similar in design are somewhat more challenging to distinguish, but not as difficult as might be expected.
Steganographic Capacity of Deep Learning Models
We find that the steganographic capacity of the learning models tested is surprisingly high, and that in each case, there is a clear threshold after which model performance rapidly degrades.
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.
An Empirical Analysis of the Shift and Scale Parameters in BatchNorm
To conduct our experiments, we implement two new optimizers in PyTorch, namely, a version of BatchNorm that we refer to as AffineLayer, which includes the re-parameterization step without normalization, and a version with just the normalization step, that we call BatchNorm-minus.
A Comparison of Graph Neural Networks for Malware Classification
Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge.
Predicting Pedestrian Crosswalk Behavior Using Convolutional Neural Networks
After extensive testing of our system in real-world environments, we conclude that it is feasible as a back-up system that can compliment existing crosswalk buttons, and thereby improve the overall safety of crossing the street.
Multifamily Malware Models
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset.
Darknet Traffic Classification and Adversarial Attacks
Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal activities.
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.
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.
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.
Computer-Aided Diagnosis of Low Grade Endometrial Stromal Sarcoma (LGESS)
Low grade endometrial stromal sarcoma (LGESS) is rare form of cancer, accounting for about 0. 2% of all uterine cancer cases.
Auxiliary-Classifier GAN for Malware Analysis
In this research, we generate fake malware images using auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images.
Machine Learning for Malware Evolution Detection
Malware evolves over time and antivirus must adapt to such evolution.
Free-Text Keystroke Dynamics for User Authentication
In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text.
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics
Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input.
Machine Learning-Based Analysis of Free-Text Keystroke Dynamics
Keystroke dynamics can be used to analyze the way that a user types based on various keyboard input.
CNN vs ELM for Image-Based Malware Classification
In this paper, we conduct experiments to train and evaluate machine learning models for malware classification, based on features that can be obtained without disassembly or execution of code.
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
In this paper, we consider malware classification using deep learning techniques and image-based features.
Word Embedding Techniques for Malware Evolution Detection
Malware detection is a critical aspect of information security.
Cluster Analysis of Malware Family Relationships
We perform clustering based on pairs of families and use the results to determine relationships between families.
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification
We show that we can obtain better classification accuracy based on these feature embeddings, as compared to HMM experiments that directly use the opcode sequences, and serve to establish a baseline.
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.
Universal Adversarial Perturbations and Image Spam Classifiers
In this chapter, we evaluate numerous adversarial techniques for the purpose of attacking deep learning-based image spam classifiers.
On Ensemble Learning
Our common framework and empirical results are an effort to bring some sense of order to the chaos that is evident in the evolving field of ensemble learning -- both within the narrow confines of the malware analysis problem, and in the larger realm of machine learning in general.
Malware Classification with Word Embedding Features
Malware classification is an important and challenging problem in information security.
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 GMM-HMM Models
Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems.
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.
