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Full-Text Articles in Physical Sciences and Mathematics
Faking Sensor Noise Information, Justin Chang
Faking Sensor Noise Information, Justin Chang
Master's Projects
Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as …
Intelligent Log Analysis For Anomaly Detection, Steven Yen
Intelligent Log Analysis For Anomaly Detection, Steven Yen
Master's Projects
Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We …
Machine Learning Versus Deep Learning For Malware Detection, Parth Jain
Machine Learning Versus Deep Learning For Malware Detection, Parth Jain
Master's Projects
It is often claimed that the primary advantage of deep learning is that such models can continue to learn as more data is available, provided that sufficient computing power is available for training. In contrast, for other forms of machine learning it is claimed that models ‘‘saturate,’’ in the sense that no additional learning can occur beyond some point, regardless of the amount of data or computing power available. In this research, we compare the accuracy of deep learning to other forms of machine learning for malware detection, as a function of the training dataset size. We experiment with a …
Multifamily Malware Models, Samanvitha Basole
Multifamily Malware Models, Samanvitha Basole
Master's Projects
When training a machine learning model, there is likely to be a tradeoff between the accuracy of the model and the generality of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we obtain stronger results as compared to a case where we train a single model on multiple diverse families. During the detection phase, it would be more efficient to have a single model that could detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments to quantify the relationship between …
Image Spam Detection, Aneri Chavda
Image Spam Detection, Aneri Chavda
Master's Projects
Email is one of the most common forms of digital communication. Spam can be de ned as unsolicited bulk email, while image spam includes spam text embedded inside images. Image spam is used by spammers so as to evade text-based spam lters and hence it poses a threat to email based communication. In this research, we analyze image spam detection methods based on various combinations of image processing and machine learning techniques.
Malware Detection Using The Index Of Coincidence, Bhavna Gurnani
Malware Detection Using The Index Of Coincidence, Bhavna Gurnani
Master's Projects
In this research, we apply the Index of Coincidence (IC) to problems in malware analysis. The IC, which is often used in cryptanalysis of classic ciphers, is a technique for measuring the repeat rate in a string of symbols. A score based on the IC is applied to a variety of challenging malware families. We nd that this relatively simple IC score performs surprisingly well, with superior results in comparison to various machine learning based scores, at least in some cases.