Physical Sciences and Mathematics Commons^™

Spam Comments Detection In Youtube Videos, Priyusha Kotta

Master's Projects

This paper suggests an innovative way for finding spam or ham comments on the video- sharing website YouTube. Comments that are contextually irrelevant for a particular video or have a commercial motive constitute as spam. In the past few years, with the advent of advertisements spreading to new arenas such as the social media has created a lucrative platform for many. Today, it is being widely used by everyone. But this innovation comes with its own impediments. We can see how malicious users have taken over these platforms with the aid of automated bots that can deploy a well-coordinated spam …

Malware Classification Based On Hidden Markov Model And Word2vec Features, Aparna Sunil Kale

Master's Projects

Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained on a wide variety of features, including opcode sequences, API calls, and byte ��-grams, among many others. In this research, we implement hybrid machine learning techniques, where we train hidden Markov models (HMM) and compute Word2Vec encodings based on opcode sequences. The resulting trained HMMs and Word2Vec embedding vectors are then used as features for classification algorithms. Specifically, we consider support vector machine (SVM), ��-nearest neighbor

(��-NN), random forest (RF), and deep neural network (DNN) classifiers. …

Classification Of Malware Models, Akriti Sethi

Master's Projects

Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset. The resulting models are then compared in two ways. First, we treat the HMM matrices as images and experiment with convolutional neural networks (CNN) for image classification. Second, we apply support vector machines (SVM) to classify the HMMs. We analyze the results and discuss the relative advantages and disadvantages of each approach.

Javascript Metamorphic Malware Detection Using Machine Learning Techniques, Aakash Wadhwani

Master's Projects

Various factors like defects in the operating system, email attachments from unknown sources, downloading and installing a software from non-trusted sites make computers vulnerable to malware attacks. Current antivirus techniques lack the ability to detect metamorphic viruses, which vary the internal structure of the original malware code across various versions, but still have the exact same behavior throughout. Antivirus software typically relies on signature detection for identifying a virus, but code morphing evades signature detection quite effectively.

JavaScript is used to generate metamorphic malware by changing the code’s Abstract Syntax Tree without changing the actual functionality, making it very difficult …