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Malware Classification with BERT
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family. -
Self-Discriminative Learning for Unsupervised Document Embedding
Self-Discriminative Learning for Unsupervised Document Embedding Hong-You Chen∗1, Chin-Hua Hu∗1, Leila Wehbe2, Shou-De Lin1 1Department of Computer Science and Information Engineering, National Taiwan University 2Machine Learning Department, Carnegie Mellon University fb03902128, [email protected], [email protected], [email protected] Abstract ingful a document embedding as they do not con- sider inter-document relationships. Unsupervised document representation learn- Traditional document representation models ing is an important task providing pre-trained such as Bag-of-words (BoW) and TF-IDF show features for NLP applications. Unlike most competitive performance in some tasks (Wang and previous work which learn the embedding based on self-prediction of the surface of text, Manning, 2012). However, these models treat we explicitly exploit the inter-document infor- words as flat tokens which may neglect other use- mation and directly model the relations of doc- ful information such as word order and semantic uments in embedding space with a discrimi- distance. This in turn can limit the models effec- native network and a novel objective. Exten- tiveness on more complex tasks that require deeper sive experiments on both small and large pub- level of understanding. Further, BoW models suf- lic datasets show the competitiveness of the fer from high dimensionality and sparsity. This is proposed method. In evaluations on standard document classification, our model has errors likely to prevent them from being used as input that are relatively 5 to 13% lower than state-of- features for downstream NLP tasks. the-art unsupervised embedding models. The Continuous vector representations for docu- reduction in error is even more pronounced in ments are being developed. -
Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction -
Fun with Hyperplanes: Perceptrons, Svms, and Friends
Perceptrons, SVMs, and Friends: Some Discriminative Models for Classification Parallel to AIMA 18.1, 18.2, 18.6.3, 18.9 The Automatic Classification Problem Assign object/event or sequence of objects/events to one of a given finite set of categories. • Fraud detection for credit card transactions, telephone calls, etc. • Worm detection in network packets • Spam filtering in email • Recommending articles, books, movies, music • Medical diagnosis • Speech recognition • OCR of handwritten letters • Recognition of specific astronomical images • Recognition of specific DNA sequences • Financial investment Machine Learning methods provide one set of approaches to this problem CIS 391 - Intro to AI 2 Universal Machine Learning Diagram Feature Things to Magic Vector Classification be Classifier Represent- Decision classified Box ation CIS 391 - Intro to AI 3 Example: handwritten digit recognition Machine learning algorithms that Automatically cluster these images Use a training set of labeled images to learn to classify new images Discover how to account for variability in writing style CIS 391 - Intro to AI 4 A machine learning algorithm development pipeline: minimization Problem statement Given training vectors x1,…,xN and targets t1,…,tN, find… Mathematical description of a cost function Mathematical description of how to minimize/maximize the cost function Implementation r(i,k) = s(i,k) – maxj{s(i,j)+a(i,j)} … CIS 391 - Intro to AI 5 Universal Machine Learning Diagram Today: Perceptron, SVM and Friends Feature Things to Magic Vector -
Q-Learning in Continuous State and Action Spaces
-Learning in Continuous Q State and Action Spaces Chris Gaskett, David Wettergreen, and Alexander Zelinsky Robotic Systems Laboratory Department of Systems Engineering Research School of Information Sciences and Engineering The Australian National University Canberra, ACT 0200 Australia [cg dsw alex]@syseng.anu.edu.au j j Abstract. -learning can be used to learn a control policy that max- imises a scalarQ reward through interaction with the environment. - learning is commonly applied to problems with discrete states and ac-Q tions. We describe a method suitable for control tasks which require con- tinuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of -learning, is shown enhance learning speed and reliability for this task.Q 1 Introduction Reinforcement learning systems learn by trial-and-error which actions are most valuable in which situations (states) [1]. Feedback is provided in the form of a scalar reward signal which may be delayed. The reward signal is defined in relation to the task to be achieved; reward is given when the system is successfully achieving the task. The value is updated incrementally with experience and is defined as a discounted sum of expected future reward. The learning systems choice of actions in response to states is called its policy. Reinforcement learning lies between the extremes of supervised learning, where the policy is taught by an expert, and unsupervised learning, where no feedback is given and the task is to find structure in data. -
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Almost Unsupervised Text to Speech and Automatic Speech Recognition Yi Ren * 1 Xu Tan * 2 Tao Qin 2 Sheng Zhao 3 Zhou Zhao 1 Tie-Yan Liu 2 Abstract 1. Introduction Text to speech (TTS) and automatic speech recognition (ASR) are two popular tasks in speech processing and have Text to speech (TTS) and automatic speech recog- attracted a lot of attention in recent years due to advances in nition (ASR) are two dual tasks in speech pro- deep learning. Nowadays, the state-of-the-art TTS and ASR cessing and both achieve impressive performance systems are mostly based on deep neural models and are all thanks to the recent advance in deep learning data-hungry, which brings challenges on many languages and large amount of aligned speech and text data. that are scarce of paired speech and text data. Therefore, However, the lack of aligned data poses a ma- a variety of techniques for low-resource and zero-resource jor practical problem for TTS and ASR on low- ASR and TTS have been proposed recently, including un- resource languages. In this paper, by leveraging supervised ASR (Yeh et al., 2019; Chen et al., 2018a; Liu the dual nature of the two tasks, we propose an et al., 2018; Chen et al., 2018b), low-resource ASR (Chuang- almost unsupervised learning method that only suwanich, 2016; Dalmia et al., 2018; Zhou et al., 2018), TTS leverages few hundreds of paired data and extra with minimal speaker data (Chen et al., 2019; Jia et al., 2018; unpaired data for TTS and ASR. -
Self-Supervised Learning
Self-Supervised Learning Andrew Zisserman Slides from: Carl Doersch, Ishan Misra, Andrew Owens, Carl Vondrick, Richard Zhang The ImageNet Challenge Story … 1000 categories • Training: 1000 images for each category • Testing: 100k images The ImageNet Challenge Story … strong supervision The ImageNet Challenge Story … outcomes Strong supervision: • Features from networks trained on ImageNet can be used for other visual tasks, e.g. detection, segmentation, action recognition, fine grained visual classification • To some extent, any visual task can be solved now by: 1. Construct a large-scale dataset labelled for that task 2. Specify a training loss and neural network architecture 3. Train the network and deploy • Are there alternatives to strong supervision for training? Self-Supervised learning …. Why Self-Supervision? 1. Expense of producing a new dataset for each new task 2. Some areas are supervision-starved, e.g. medical data, where it is hard to obtain annotation 3. Untapped/availability of vast numbers of unlabelled images/videos – Facebook: one billion images uploaded per day – 300 hours of video are uploaded to YouTube every minute 4. How infants may learn … Self-Supervised Learning The Scientist in the Crib: What Early Learning Tells Us About the Mind by Alison Gopnik, Andrew N. Meltzoff and Patricia K. Kuhl The Development of Embodied Cognition: Six Lessons from Babies by Linda Smith and Michael Gasser What is Self-Supervision? • A form of unsupervised learning where the data provides the supervision • In general, withhold some part of the data, and task the network with predicting it • The task defines a proxy loss, and the network is forced to learn what we really care about, e.g. -
Introduction to Machine Learning
Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial Neural Networks 3 Short History Progression (1943-1960) • First mathematical model of neurons ▪ Pitts & McCulloch (1943) • Beginning of artificial neural networks • Perceptron, Rosenblatt (1958) ▪ A single neuron for classification ▪ Perceptron learning rule ▪ Perceptron convergence theorem Degression (1960-1980) • Perceptron can’t even learn the XOR function • We don’t know how to train MLP • 1963 Backpropagation… but not much attention… Bryson, A.E.; W.F. Denham; S.E. Dreyfus. Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 (1963) 2544-2550 4 Short History Progression (1980-) • 1986 Backpropagation reinvented: ▪ Rumelhart, Hinton, Williams: Learning representations by back-propagating errors. Nature, 323, 533—536, 1986 • Successful applications: ▪ Character recognition, autonomous cars,… • Open questions: Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? • Hopfield nets (1982), Boltzmann machines,… 5 Short History Degression (1993-) • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. • SVM and Graphical models almost kill the ANN research. • Training deeper networks consistently yields poor results. • Exception: deep convolutional neural networks, Yann LeCun 1998. (discriminative model) 6 Short History Progression (2006-) Deep Belief Networks (DBN) • Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. • Generative graphical model • Based on restrictive Boltzmann machines • Can be trained efficiently Deep Autoencoder based networks Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. -
Survey on Reinforcement Learning for Language Processing
Survey on reinforcement learning for language processing V´ıctorUc-Cetina1, Nicol´asNavarro-Guerrero2, Anabel Martin-Gonzalez1, Cornelius Weber3, Stefan Wermter3 1 Universidad Aut´onomade Yucat´an- fuccetina, [email protected] 2 Aarhus University - [email protected] 3 Universit¨atHamburg - fweber, [email protected] February 2021 Abstract In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language process- ing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning. Keywords| reinforcement learning, natural language processing, conversational systems, pars- ing, translation, text generation 1 Introduction Machine learning algorithms have been very successful to solve problems in the natural language pro- arXiv:2104.05565v1 [cs.CL] 12 Apr 2021 cessing (NLP) domain for many years, especially supervised and unsupervised methods. However, this is not the case with reinforcement learning (RL), which is somewhat surprising since in other domains, reinforcement learning methods have experienced an increased level of success with some impressive results, for instance in board games such as AlphaGo Zero [106]. -
Reinforcement Learning in Supervised Problem Domains
Technische Universität München Fakultät für Informatik Lehrstuhl VI – Echtzeitsysteme und Robotik reinforcement learning in supervised problem domains Thomas F. Rückstieß Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation 1. Univ.-Prof. Dr. Patrick van der Smagt 2. Univ.-Prof. Dr. Hans Jürgen Schmidhuber Die Dissertation wurde am 30. 06. 2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 18. 09. 2015 angenommen. Thomas Rückstieß: Reinforcement Learning in Supervised Problem Domains © 2015 email: [email protected] ABSTRACT Despite continuous advances in computing technology, today’s brute for- ce data processing approaches may not provide the necessary advantage to win the race against the ever-growing amount of data that can be wit- nessed over the last decades. In this thesis, we discuss novel methods and algorithms that are capable of directing attention to relevant details and analysing it in sequence to overcome the processing bottleneck and to keep up with this data explosion. In the first of three parts, a novel exploration technique for Policy Gradi- ent Reinforcement Learning is presented which replaces traditional ad- ditive random exploration with state-dependent exploration, exploring on a higher, more strategic level. We will show how this new exploration method converges faster and finds better global solutions than random exploration can. The second part of this thesis will introduce the concept of “data con- sumption” and discuss means to minimise it in supervised learning tasks by deriving classification as a sequential decision process and ma- king it accessible to Reinforcement Learning methods. -
Audio Event Classification Using Deep Learning in an End-To-End Approach
Audio Event Classification using Deep Learning in an End-to-End Approach Master thesis Jose Luis Diez Antich Aalborg University Copenhagen A. C. Meyers Vænge 15 2450 Copenhagen SV Denmark Title: Abstract: Audio Event Classification using Deep Learning in an End-to-End Approach The goal of the master thesis is to study the task of Sound Event Classification Participant(s): using Deep Neural Networks in an end- Jose Luis Diez Antich to-end approach. Sound Event Classifi- cation it is a multi-label classification problem of sound sources originated Supervisor(s): from everyday environments. An auto- Hendrik Purwins matic system for it would many applica- tions, for example, it could help users of hearing devices to understand their sur- Page Numbers: 38 roundings or enhance robot navigation systems. The end-to-end approach con- Date of Completion: sists in systems that learn directly from June 16, 2017 data, not from features, and it has been recently applied to audio and its results are remarkable. Even though the re- sults do not show an improvement over standard approaches, the contribution of this thesis is an exploration of deep learning architectures which can be use- ful to understand how networks process audio. The content of this report is freely available, but publication (with reference) may only be pursued due to agreement with the author. Contents 1 Introduction1 1.1 Scope of this work.............................2 2 Deep Learning3 2.1 Overview..................................3 2.2 Multilayer Perceptron...........................4 -
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018)
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018) Chika Yinka-Banjo, PhD Ayorkor Korsah, PhD University of Lagos Ashesi University Nigeria Ghana Outline • Introduction to Machine learning • Reinforcement learning definitions • Example reinforcement learning problems • The Markov decision process • The optimal policy • Value function & Q-value function • Bellman Equation • Q-learning • Building a simple Q-learning agent (coding) • Recap • Where to go from here? Introduction to Machine learning • Artificial Intelligence (AI) is the study and design of Intelligent agents. • An Intelligent agent can perceive its environment through sensors and it can act on its environment through actuators. • E.g. Agent: Humanoid robot • Environment: Earth? • Sensors: Camera, tactile sensor etc. • Actuators: Motors, grippers etc. • Machine learning is a subfield of Artificial Intelligence Branches of AI Introduction to Machine learning • Machine learning techniques learn from data without being explicitly programmed to do so. • Machine learning models enable the agent to learn from its own experience by extracting useful information from feedback from its environment. • Three types of learning feedback: • Supervised learning • Unsupervised learning • Reinforcement learning Branches of Machine learning Supervised learning • Supervised learning: the machine learning model is trained on many labelled examples of input-output pairs. • Such that when presented with a novel input, the model can estimate accurately what the correct output should be. • Data(x, y): x is input data, y is label Supervised learning task in the form of classification • Goal: learn a function to map x -> y • Examples include; Classification, regression object detection, image captioning etc. Unsupervised learning • Unsupervised learning: here the model extract useful information from unlabeled and unstructured data.