Malware classification using machine learning

It's Never Too Late to Learn a New Skill! Learn to Code and Join Our 45+ Million Users. Enjoy Extra Quizzes & Projects and Exclusive Content. Practice with Our App. Enroll Today Machine Learning Problem, KPI and constraints We can map the business problem to a multi-class classification problem, where we need to predict the class for each given byte files among nine categories (Ramnit, Lollipop, Kelihos_ver3, Vundo, Simda,Tracur, Kelihos_ver1, Obfuscator.ACY, Gatak). KPI: multi-class log loss, confusion matri Malware Detection & Classification using Machine Learning Abstract: With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems

As a solution, we propose a classifier model based on machine learning that can detect malware activities. This research aims to study the existing malware classification algorithm's features, apply the existing algorithm, and evaluate the algorithm for malware classification. This algorithm can detect the malware with high accuracy up to 99.3% Malware Image Classification using Machine Learning with Local Binary Pattern An Abstract of A Thesis Presented to The Faculty of the Computer Science Department by Jhu-Sin Luo Bachelor of Science, National Kaohsiung First University of Science and Technology, 2014 In Partial Fulfillment of Requirements for the Degre

Malware Classification using classical Machine Learning and Deep Learning This repository is the official implementation of the research mentioned in the chapter An Empirical Analysis of Image-Based Learning Techniques for Malware Classification of the Book Malware Analysis Using Artificial Intelligence and Deep Learning Nowadays, there are countless types of malware attempting to damage companies' information systems. Thus, it is essential to detect and prevent them to avoid any risk. Malware classification is a widely used task that, as you probably know, can be accomplished by machine learning models quite efficiently Malware classification using Machine Learning Uses examples from book titled Malware Data Science to explain how AV companies use Machine learning to identify malware. Also, refers to open-source project Ember which provides a data set and python code to train and classify malware

Machine Learning In Machine Learning, classification is the problem of assigning an input sample into one of the target categories. For malware detection, the two categories are benign and.. Currently, machine learning techniques are becoming popular for classifying malware. However, most of the existing machine learning methods for malware classifying use shallow learning algorithms such as Support Vector Machine, decision trees, Random Forest, and Naive Bayes As a part of their overall strategy, Microsoft challenged the data science community to develop the machine learning systems which can determine or predict if the machine will be soon hit by a.. Machine Learning can be split into two major methods supervised learning and unsupervised learning the first means that the data we are going to work with is labeled the second means it is unlabeled, detecting malware can be attacked using both methods, but we will focus on the first one since our goal is to classify files

detection classification using machine learning algorithms and it is discussed about main important challenges that are facing in malware detection classification. Index Terms: Malware, Malware Analysis, Static Analysis, Dynamic Analysis, Classification, Machine learning, Data mining Techniques, Malicious Code Current state-of-the-art research shows that recently, researchers and anti-virus organizations started applying machine learning and deep learning methods for malware analysis and detection. We have used opcode frequency as a feature vector and applied unsupervised learning in addition to supervised learning for malware classification

unknown malware into recognized malware families using machine learning. To tackle this problem, researchers have suggested static and dynamic analysis techniques and procedures, which depend on the observation of the behavior of the malware pro-gram's activities for detection and classification. 1.2 Static Malware Analysi Whereas Machine Learning based Malware Classification for Android applications using Multimodal Image Representations [3] is a bit slow when it comes to data processing. Proposed Methodology: To analyze the signature, the signature is built from N-Gram techniques This paper talks about methods, problems and solutions to Malware Classification using Machine Learning. It is believed that the amount of malicious software being released might surpass the release of authoritative software. Since malware gets more sophisticated every year, this results in a need to shift from traditional methods and make the. Anti-malware companies turned to machine learning, an area of computer science that had been used successfully in image recognition, searching and decisionmaking, to augment their malware detection and classification. Today, machine learning boosts malware detection using various kinds of data o This work presents recommended methods for machine learning based malware classification and detection, as well as the guidelines for its implementation. Moreover, the study performed can be useful as a base for further research in the field of malware analysis with machine learning methods. Keywords malware, machine learning, classification.

Machine Learning Basics - Start Investing in Yoursel

  1. In the context of malware analysis, a machine learning model is trained on a dataset of existing labeled malware examples, with the labeling either in terms of malicious or benign in the case of binary classification, or in terms of the type or family of malware for multi-class classification
  2. Malware classification This paper utilizes deep learning to classify the families of malware for Portable Executable 32 (PE32)
  3. The extracted data was used for the development of a novel type classification approach based on supervised machine learning. The proposed classification approach employs a novel combination of features that achieves a high classification rate with a weighted average AUC value of 0.98 using Random Forests classifier

Traditional machine learning approaches can be categorized into two primary groups, static and dynamic approaches, depending on the type of analysis. The main difference between them is that static approaches extract features from the static analysis of malware, while dynamic approaches extract features from the dynamic analysis We present the results of experiments performed on the Classification of Malware with PE headers (ClaMP) dataset. The best performance is achieved by an ensemble of five dense and CNN neural networks, and the ExtraTrees classifier as a meta-learner. Ensemble-Based Classification Using Neural Networks and Machine Learning Models for Windows. In the real world, the malware datasets are open-ended and dynamic, and new malware samples belonging to old classes and new classes are increasing continuously. This requires the malware classification method to enable incremental learning, which can efficiently learn the new knowledge In this paper, we demonstrate a classification technique of integrating both static and dynamic features to increase the accuracy of detection and classification of ransomware. We train supervised machine learning algorithms using a test set and use a confusion matrix to observe accuracy, enabling a systematic comparison of each algorithm

One of the most difficult parts of effectively using a machine learning algorithm for malware detection is converting the data to a format that can be used to build a machine learning model. This lab explores malware detection through a particular type of malicious script found in Microsoft Office files called macro malware Applying Deep Learning for PE-Malware Classification. 1. Share. Deep Learning & Computer vision techniques are making progress in every possible field. With growing computing powers many organizations use them to resolve or minimize many day-to-day problems. In a recent talk at AVAR 2018, Quick Heal AI team presented an approach of effectively.

Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study Dr. Jason Zhang, Sophos ABSTRACT Cybersecurity threats have been growing significantly in both volume and sophistication over the past decade. This poses great challenges to malware detection without considerable automation Summary of some research papers about machine learning applied in malware detection Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website In the past few years, researchers and anti-malware communities have reported using machine learning and deep learning based methods for designing malware analysis and detection system. According to the survey [1] conducted by AV-Test Institute, it registers that everyday 350,000 new malicious code and potentially unwanted applications

Malware Classification using Machine Learning by Arpan

Malware Classification Using Machine Learning. July 15, 2021July 15, 2021 by Asae. You are coaxing the reader to embrace a good outlook towards your merchandise or provider. Blog is a contraction of 'web log. ' Mainly, a weblog is a log of feelings, tips, practical one-way links, shots, movies, newest information or scandal Other than that, Machine Learning Aided Android Malware Classification had researched to detect and analyze malicious Android Apps by using two machine learning aided which are classification and clustering. The permission-based method was demonstrated and was able to classify malware from goodware in 89% of cases while the source code. Supervised learning: the machine has at its disposal both the inputs and outputs to learn. Unsupervised learning: the learning process is executed without any correct output avail-able. Reinforcement learning: given a certain input and consequent action, the latter is evaluated without the correct action being disclosed. 2.2 Time serie The exponential growth of malware has created a significant threat in our daily lives, which heavily rely on computers running all kinds of software. Malware writers create malicious software by creating new variants, new innovations, new infections and more obfuscated malware by using techniques such as packing and encrypting techniques. Malicious software classification and detection play an. success. Every time a new machine learning methodology is introduced for classifying malware, there is the potential for increasing the overall quality of malware classification in the field. Even new classifiers with the same accuracy as those used previously can be combined using one of a few different ensembl

Malware Detection & Classification using Machine Learning

analysis of malware behaviour using machine learning. They suggest an incremental method for behaviour analysis using clustering and classification. Another way for malware classification is extracting malicious API sequences. This is done using voting expert's algorithm over API calls, or b In this paper, a new learning machine approach is used effectively where significance is given to data analysis, feature engineering, and modeling. This way, it helps us quickly differentiate actual file and malware type based on the characteristics before entering into the Cloud Environment Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification. AFAgarap/malware-classification • • 31 Dec 2017. We envision an intelligent anti-malware system that utilizes the power of deep learning (DL) models ANDROID MALWARE CLASSIFICATION USING PARALLELIZED MACHINE LEARNING METHODS by Lifan Xu Approved: Kathleen F. McCoy, Ph.D. Chair of the Department of Computer and Information Sciences Approved: Babatunde A. Ogunnaike, Ph.D. Dean of the College of Engineering Approved: Ann L. Ardis, Ph.D. Senior Vice Provost for Graduate and Professional Educatio

Malware Classification Using Machine Learning Algorithm

Malware Image Classification using Machine Learning with

  1. Machine learning tech-niques have been used in malware analysis as well. For example, Nataraj et al. (2011) represented malware in grayscale images and utilized pattern recog-nition approaches used in image processing in order to detect malware. Also other approaches using standard machine learning algorithms such as percep
  2. Analysis of Malware behavior: Type classification using machine learning. Malicious software has become a major threat to modern society, not only due to the increased complexity of the malware itself but also due to the exponential increase of new malware each day. This study tackles the problem of analyzing and classifying a high amount of.
  3. Analysis of Malware behavior: Type classification using machine learning. In 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). 1--7. 2015.7166115 Google Scholar Cross Re
  4. In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive image datasets. We carry out various experiments with this technique and.
  5. Machine Learning : Naïve Bayes Rule for Malware Detection and Classification. March 29, 2013 by Victor Marak. Share: ABSTRACT: This paper presents statistics and machine learning principles as an exercise while analyzing malware. Conditional probability or Bayes' probability is what we will use to gain insight into the data gleaned from a.
  6. Each machine learning algorithm was subsequently optimised using optimisation algorithms, including the use of bio-inspired optimisation algorithms such as Particle Swarm Optimisation, Artificial Bee Colony optimisation (ABC), Firefly optimisation and Genetic algorithm. The prototype framework was tested and evaluated using three datasets

DOI: 10.1109/CyberSA.2015.7166128 Corpus ID: 206600254. Analysis of malware behavior: Type classification using machine learning @article{Pirscoveanu2015AnalysisOM, title={Analysis of malware behavior: Type classification using machine learning}, author={Radu S. Pirscoveanu and Steven S. Hansen and Thor M. T. Larsen and M. Stevanovic and J. Pedersen and A. Czech}, journal={2015 International. This work presents recommended methods for machine learning based malware classification and detection, as well as the guidelines for its implementation. Moreover, the study performed can be useful as a base for further research in the field of malware analysis with machine learning methods in the classification process using two machine learning tools namely Knime and Orange. In their experiments, the authors compare the results of malware classification using the Decision Tree classification method, Naïve Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. The results exhibit a comparison of th

A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones. 02/12/2021 ∙ by Sai Vishwanath Venkatesh, et al. ∙ IEEE ∙ 0 ∙ share . Smartphones contain information that is more sensitive and personal than those found on computers and laptops Classification of Malware: using reverse engineering and machine learning techniques [Ravula, Ravindar Reddy] on Amazon.com. *FREE* shipping on qualifying offers. Classification of Malware: using reverse engineering and machine learning technique Keywords: Malware classification, Hu man encoding, malware abstraction, feature construction, compression, machine learning 1. Introduction Developing adequate identification and eradication schemes to combat malware is a major challenge nowadays, due to the ever-increasing complexities of such malicious programs. Fro Machine learning to tackle attacks. an attacker can use it for sending malware with the intent of gathering sensitive data. One example of a classification algorithm is Support Vector. note = Chowdhury, M., Rahman, A. and Islam, R. (2017). Malware analysis and detection using data mining and machine learning classification, In Proceedings of the 2017 International Conference on Applications and Techniques in Cyber Intelligence (ATCI) on the Springer's {}Advances in Intelligent Systems and Computing{} book series, Ningbo (June 16 - 18); International Conference on.

IoT Malware Network Traffic Classification using Visual Representation and Deep Learning. 10/04/2020 ∙ by Gueltoum Bendiab, et al. ∙ 0 ∙ share . With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication Naser Peiravian and Xingquan Zhu. 2013. Machine learning for android malware detection using permission and api calls. In 2013 IEEE 25th international conference on tools with artificial intelligence. IEEE, 300--305. Google Scholar Digital Library; Juan Ramos et almbox. 2003. Using tf-idf to determine word relevance in document queries This section will provide an overview about analysis of malware, and the need for machine learning based classification. 1.1. Malware Detection and Classification Malware detection is the process of scanning files to find/detect malware. Malware classification is the task of classifying different types of malware into their respective families

Malware Classification using classical Machine Learning

The natural variation in the physical properties of virus particles had previously hindered implementation of this approach, however, using machine learning, the team built a classification. Conclusion. This paper shows that neural networks are capable of learning to discriminate benign and malicious Windows executables without costly and unreliable feature engineering. This avoids a number of issues with commonly used anti-virus and malware detection systems while achieving higher classification AUC The traditional non-machine learning malware classification methods are mostly heuristic and signature-based. There are two types of traditional malware analysis methods: the static methods and dynamic methods. The static methods extract the malware features from the static malware bytecodes, suc Microsoft Malware Prediction | Kaggle. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using Kaggle, you agree to our use of cookies. Got it. Learn more. We use cookies on Kaggle to deliver our services, analyze web traffic, and improve your experience on the site. By using. CSE 4003 - Cyber SecurityJ Component - Review 2Team Members:V Manoj Kumar - 17BCE0835K Nithiya Soundari - 17BCE2244 (Team Leader)Vaishnavi S - 19BCE241

Malware Classification using Deep Learning - Tutorial

Assignment: Improvements to malware detection and classification Machine learning is not all about autonomous vehicles and terminator robots. Techniques such as principle component analysis (PCA) can be combined with other data exploration techniques to help us gain a deeper understanding of the world around us The results certainly encourage the use of deep transfer learning for the purpose of malware classification, said Jugal Parikh and Marc Marino, the two Microsoft researchers who participated in. In this paper, we provide an overview of the performance of machine learning (ML) techniques to detect malware on Android, without using privileged access. The ML-classifiers use device information such as the CPU usage, battery usage, and memory usage for the detection of 10 subtypes of Mobile Trojans on the Android Operating System (OS) This paper demonstrates how machine learning using intrinsic genomic signatures can provide rapid alignment-free taxonomic classification of novel pathogens. Our method delivers accurate classifications of the COVID-19 virus without a priori biological knowledge, by a simultaneous processing of the geometric space of all relevant viral genomes. [2] B. Anderson and D. McGrew. Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity. In ACM SIGKDD International Conference on Knowledge. Discovery and Data Mining (KDD), 2017 (To Appear). [3] B. Anderson, S. Paul, and D. McGrew. Deciphering Malware's use of TLS (without Decryption.

Malware Classification - SerializingMe

Malware classification using Machine Learnin

  1. Therefore we propose a machine learning model to classify the malware to their corresponding families using the properties of the malware. In this paper, we present a Review of Mansour Ahmadi et al.'s Feature fusion for effective Malware Family Classification system, Liu et al.'s Automatic Malware classification and detection system.
  2. Malware Analysis and Detection using Data Mining and Machine Learning Classification Mozammel Chowdhury, Azizur Rahman and Rafiqul Islam School of Computing and Mathematics, Charles Sturt University, Australia {mochowdhury,azrahman,mislam}@csu.edu.au Abstract. Exfiltration of sensitive data by malicious software or malware is
  3. g increasingly common and can cause millions of dollars of damage annually. A large area of research focus is the automated detection and removal of such.
  4. First, malware images are reorganized into 3 by 3 grids which is mainly used to extract LBP feature. Second, the LBP is implemented on the malware images to extract features in that it is useful in pattern or texture classification. Finally, Tensorflow, a library for machine learning, is applied to classify malware images with the LBP feature
  5. the use of machine learning for malware detection. Many notable works have already been done for malware classification/detection with impressive detection rates that target the classification of PE. Nevertheless, these works have their own set of limitations that introduces the gap of study. For instance, Kruczkowski, an

Deep Learning for Malware Classification by Simant Dube

trained using the prediction outputs of the individual models as input features) improved overall classification performance to an accuracy of 98.9%, a meaningful improvement over the most effective individual model, suggesting both that machine learning is an effective tool for classification of potentially malicious compiled binaries, and furthermore that performance improvements can be. Malware detection using machine learning. Rn I am working on my final project and I am a little bit stuck. I need to create a data set to train my machine learning algorithms. I am using Cuckoo sandbox to generate reports of different types of malware and non malware files. The cuckoo reports are in .json format so I need to convert them in. Using image representation of raw data as a feature helped the team say NOOOOO to overfittttting win the Microsoft Malware Classification Challenge. The team came with the following approach — Malwares can be visualized as gray­scale images using the byte file. Each byte is from 0 to 255 so it can be easily translated into pixel intensity

Malware Classification by Using Deep Learning Framework

Using virus whole genome sequencing to catalogue all nucleotide variants occurring at >1% machine learning approaches are explored to determine whether classification of HBeAg status could be. Generally, I see the correct application of AI in the supervised machine learning camp where there is a lot of labeled data available: malware detection (telling benign binaries from malware.

Microsoft Malware Prediction Using Classical Machine

• [Xin-2018] Machine Learning and Deep Learning Methods for Cybersecurity . 3.2.2 Mal]aXe • [Apruzzesse-2018] On the Effectiveness of Machine and Deep Learning for Cyber Security • [Avira-2018a, Avira-2018b] Malware detection and classification, Avira NightVision (an AI platform Three classifiers namely KNN, Linear Discriminant Analysis and Gradient Boosting Machine (GBM) were used for this task. The best model achieved an accuracy of 94% on malware classification. This research also developed an early-stage Deep learning based detection model For the first part of the collaboration, the researchers built on Intel's prior work on deep transfer learning for static malware classification and used a real-world dataset from Microsoft to ascertain the practical value of approaching the malware classification problem as a computer vision task. The basis for this study is the observation.

Machine Learning for Malware Detection - Infosec Resource

machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection tech-niques based on machine learning algorithms focused on the Android OS. Introduction According to a 2014 research study (RiskIQ. The application of Machine Learning for botnet detection has been widely researched. [stevanovic2014efficient] developed a flow-based botnet detection system using supervised machine learning. [santana2018we] explored a couple of Machine Learning models to characterize their capabilities, performance and limitations for botnet attacks It is suitable for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques

Features and malware classification neural network. We built a simple but well-functioning neural network similar to our product model for the task of malware detection. The model is based on static analysis of executable files (PE files). Malware classification neural network. The neural network model works with the following types of features We have developed a distributed malware testing environment by extending Cuckoo Sandbox that was used to test an extensive number of malware samples and trace their behavioral data. The extracted data was used for the development of a novel type classification approach based on supervised machine learning We use a variety of machine learning models that use different algorithms to predict whether a certain file is malware. Some of these algorithms are binary classifiers that give a strict clean-or-malware verdict (0 or 1), while others are multi-class classifiers that provide a probability for each classification (malware, clean, potentially.

Malware Classification using Attention-based Transductive Learning Network Liting Deng, Hui Wen, Mingfeng Xin, Yue Sun, Limin Sun, Hongsong Zhu. LaaCan: A Lightweight Authentication Architecture for Vehicle Controller Area Network Syed Akib Anwar Hridoy, Mohammad Zulkernine. The Maestro Attack: Orchestrating Malicious Flows With BG Malware Classification. Intrusion Detection. Deep learning is a subtype of Machine Learning (ML) and belongs to the broader category of artificial intelligence. Deep learning uses Artificial Neural Networks (ANNs), which are designed to mimic the functionality and connectivity of neurons in the human brain Learning to Identify Known and Unknown Classes: A Case Study in Open World Malware Classification. [longer version] M. Hassen and P. Chan Proc. Intl. FLAIRS Conf., pp. 26-31, 2018. Using a Personalized Anomaly Detection Approach with Machine Learning to Detect Stolen Phones. H. Hu and P. Chan Proc. Intl. FLAIRS Conf., pp. 323-328, 2018 Use of deep learning in Android malware detection. Currently available machine learning has several weaknesses and some open issues related to the use of DL in Android malware detection include: Deep learning lacks transparency to provide an interpretation of the decision created by its methods. Malware analysts need to understand how the. From this point, the field expanded to study the potential for adversarial examples in other realms, like machine-learning-based image and malware classification. The increased usage of deep learning techniques for object recognition led to a surge in interest around 2014, when Szegedy et al. showed that deep convolutional neural networks were.

Deep Learning for Classification of Malware System Call Sequences; Deep Learning for Zero-day Flash Malware Detection (Short Paper) Deep Learning is a Good Steganalysis Tool When Embedding Key is Reused for Different Images, even if there is a cover source mismatch Machine Learning, Big Data. retweets are not endorsements The present system includes a computer-networked system that allows mobile subscribers, and others, to submit mobile applications to be analyzed for anomalous and malicious behavior using data acquired during the execution of the application within a highly instrumented and controlled environment for which the analysis relies on per-execution as well as comparative aggregate data across many. Building machine learning models of malware behavior is widely accepted as a panacea towards effective malware classification. A crucial requirement for building sustainable learning models, though, is to train on a wide variety of malware samples. Unfortunately, malware evolves rapidly and it thus becomes hard—if not impossible—to.

Feature selection and machine learning classification forMalware Detection in Self-Driving Vehicles Using Machine

Using Machine Learning Techniques to Identify Botnet Traffic. Some studies in machine learning using the game of checkers. II—Recent progress: Machine learning methods for classifying human physical activity from on-body accelerometers: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow. Dr. Ajit Kumar is an Assistant Professor at Sri Sri University. He has completed his Ph.D. from Department of Computer Science, Pondicherry University in 2018. His Ph.D. thesis titled 'A Framework for Malware Detection with Static Features using Machine Learning Algorithms' focused on Malware detection using machine learning Email Spam detection with Machine Learning. Aman Kharwal. May 17, 2020. Machine Learning. 13. Email spam, are also called as junk emails, are unsolicited messages sent in bulk by email (spamming). In this Data Science Project I will show you how to detect email spam using Machine Learning technique called Natural Language Processing and Python

Malware analysis, detection, classification, and attribution; Vulnerability discovery using machine learning; ML applications for cloud infrastructure and IoT security; Network attack detection, classification, and analysis; Spam, phishing, online scam detection; Malicious behaviors in online social networks; Sequence analysis for system. Effective and efficient mitigation of malware is a long-time endeavor in the information security community. The development of an anti-malware system that can counteract an unknown malware is a prolific activity that may benefit several sectors. We envision an intelligent anti-malware system that utilizes the power of deep learning (DL) models. Using such models would enable the detection of. Xu, Lifan. (University of Delaware), Android malware classification using parallelized machine learning methods (2016) Advisor: Cavazos, John Android is the most popular mobile operating system with a market share of over 80%. Due to its popularity and also its open source nature, Android is now the platform most targeted by malware, creating an urgent need for effectiv In the following sections, we introduce several malicious C2 traffic types, which we use as samples to show how an advanced machine learning system can detect such traffic. The discussed malware serves as examples to illustrate the effectiveness of our machine learning AI in the detection of C2 traffic. The detection capabilities of our AI are. Machine-learning based patient classification using Hepatitis B virus full-length genome quasispecies from Asian and European cohorts Sci Rep . 2019 Dec 11;9(1):18892. doi: 10.1038/s41598-019-55445-8

AI and Machine Learning in Cyber Security | byMalware Analysis Datasets: Raw PE as Image | IEEE DataPort(PDF) Malware Analysis and Classification: A Survey