Malware classification using deep learning methods

Malware classification using deep learning methods

Deep learning models are shown to work much better in the analysis of long sequences of system calls. In this paper a shallow deep learning-based feature extraction method (word2vec) is used for representing any given malware based on its opcodes. Gradient Boosting algorithm is used for the classification task In this paper, we propose an original deep learning framework for malware classifying based on the malware behavior data. Currently, machine learning techniques are becoming popular for classifying malware This research presents a deep learning-based malware detection (DLMD) technique based on static methods for classifying different malware families. The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families Malware classification is a widely used task that, as you probably know, can be accomplished by machine learning models quite efficiently. In this article, I have decided to focus on an interesting malware classification method based on Convolutional Neural Networks

Malware Detection using Deep Learning Methods Nourin N.S, Sulphikar A Abstract: Rapid development of the internet leads the malware and classification of malware need to be improved to do the required prevention mechanism. Different kinds of software provide wealth resources to users which results in danger. As a result, malware.

(PDF) Android Malware Detection using Deep Learning on API

There are very few studies that have demonstrated the methods of classification according to the malware families. All operating system API calls made to act by any software show the overall direction of this program. Whether this program is a malware or not can be learned by examining these actions in-depth For example, the first class has a label of 0, the malware class is Worn, an the familly is Allaple.L For deep learning, we have created a model by taking the first 8 layers of the ResNet101 Convolutional Neural Network, to which we have concatenated a Flatten Layer, followed by a Fully Connected Neural Network

Abstract In this chapter, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) Malware analysts need to understand how the decision was made. There is no assurance that classification models built based on deep learning will perform in different conditions with new data that would not match previous training data Having a large amount of data i.e. images converted of both clean & malware files, we now apply Deep Learning algorithms on these samples. Deep Learning (DL) or Deep Neural Network (DNN) is a special class of Machine Learning (ML). Artificial Neural Networks (ANN) are building blocks of DNNs. ANNs take inspiration from biological nervous systems Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning. This takes too much time. By using deep learning techniques this step can be completely avoided. Recent research reported that many of them used biased dataset, which is completely ineffective in real-time situations. Hence this drives to create a new algorithm/architecture to detect malware using deep learning

Malware Classification by Using Deep Learning Framework

Whether the person has a virus or not is usually done by the PCR test. In addition to the PCR method, chest x-ray images can be classified with deep learning methods. Deep learning methods have become popular in academic studies by processing multi-layered images in one go and by defining manually entered parameters in machine learning 2.2 Methods Based on Deep Learning. Several studies on malware classification have been performed using CNN architectures. Cui et al. [6] detected code variants that are malicious after converting to grayscale images and using a simple CNN model We compare the performance of IMCFN algorithm with existing malware classification study, which used image-based malware classification techniques based on machine and deep learning methods. Firstly, these techniques extracted features from the malware images, and then applied machine learning or deep learning classifiers (e.g., Softmax, KNN. Existing reference-based and gene homology-based methods are not efficient in identifying unknown viruses or short viral sequences from metagenomic data. Here we developed a reference-free and alignment-free machine learning method, DeepVirFinder, for identifying viral sequences in metagenomic data using deep learning Malware classification. This paper utilizes deep learning to classify the families of malware for Portable Executable 32 (PE32). More on paper.doc

As a result, we built a dataset that contains the malware behavioral data at runtime and class labels to which the software was included. classification model is proposed, and this dataset created a model for malware detection using deep learning method LSTM Microsoft and Intel are Using Deep Learning to Track Malware Deep learning is a machine learning technique that combines artificial intelligence and image analysis to create highly effective means to detect malicious software. Data scientists from Microsoft and Intel have developed a method called STAMINA Over the last two years, FireEye has been experimenting with deep learning architectures for malware classification, as well as methods to evade them. Our experiments have demonstrated surprising levels of accuracy that are competitive with traditional ML-based solutions, while avoiding the costs of manual feature engineering 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 In case of behavior analysis of a malware, categorization of malicious files is an essential part after malware detection. Numerous static and dynamic techniques have been reported so far for categorizing malwares. This research work presents a deep learning based malware detection (DLMD) technique based on static methods for classifying different malware families

[1910.10958] Malware Classification using Deep Learning ..

families of known malware. Several machine learning methods have been proposed for solving the malware classification problem. However, these techniques rely on hand-engineered features extracted from malware data which may not be effective for classifying new malware. Deep learning models have show A classical machine learning method for static analysis will extract there is now growing interest in applying deep learning to malware classification as evidenced by several publications from. Deep learning has been recently achieving a great performance for malware classification task. Several research studies such as that of converting malware into gray-scale images have helped to improve the task of classification in the sense that it is easier to use an image as input to a model that uses Deep Learning's Convolutional Neural. The main aim of this paper is to present a robust Malware Detection method to detect unknown malwares using deep learning. With the ascent in the shadow Internet economy, malware has formed into one of the significant dangers to PCs and data frameworks all through the world. Deep learning works on multiple processing layers which makes it more.

Ni et al, Malware identification using visualization algorithm (MCSC), extracting the opcode sequences from images and deep learning, Computers & Security, malware and encoding them to equal lengths to convert to Elsevier, pp. 1-15, 2018. gray images and classify using CNN with an average [10] Given the challenges and ubiquity of modern malware, many researchers have attempted to devise automated methods for detection [1]. For over a decade, researchers and anti-virus companies have begun employing machine learning algo-rithms to address this problem as noted in Section 2. In the academic community, researchers have proposed using Ransomware Traffic Classification Using Deep Learning Models: Ransomware Traffic Classification: 10.4018/IJWP.2020010101: Ransomware is a malware which affects the systems data with modern encryption techniques, and the data is recovered once a ransom amount is paid. In thi Preliminary work in deep learning as it applies to Android malware detection was presented in Ref. [23]. In this study, we first extracted a total of 192 features from static and dynamic app analyses and then applied the deep learning technique to distinguish malware from benign apps. Our premise is that deep learning with a deep architecture. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and benign applications

Malware Classification using Deep Learning - Tutorial

  1. the machine learning classification algorithm were used such as Naive baiyes, KNM, SMO which bring less Accuracy on Malware detection. From the above Problems are identified that Deep learning techniques are necessary for malware classification with highest accuracy. Thus there is a necessary study on Deep learning on Data from IoT device
  2. **Malware Classification** is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or.
  3. analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, significantly reducing the time of detection with promising results due to the deep learning technologies used
  4. Block diagram of malware detection using deep learning hybrid detection category. 2. Deep Learning Algorithms The purpose of this article is to present a The idea of deep learning evolved from timely review of deep learning techniques in the neural networks. Other popular machine learning techniques as well as latest modified methods.
  5. learning methods like Local Outlier Factor, One-class SVM and Isolation Forest [15], while another focused on the exploration of dimensionality reduction and methods like Decision Tree and k-Nearest Neighbors classification [16]
  6. Malware classification using Deep Learning based on static, raw malware executable data Data driven approach -Allow the model to learn the features from perspecti ve, our deep learning methods require that this size is constrained so as to k eep the model training process practica

Detecting and Classifying Malware: The number and variety of malware attacks are continually increasing, making it more difficult to defend against them using standard methods. DL provides an opportunity to build generalizable models to detect and classify malware autonomously. There are a number of ways to detect malware The study of Schmidt et al. 39 in a multimodal deep learning method for android malware detection; using various feature sets proposed a framework that used a similarity-based system to extract and represent android malicious application features. The binary vectors and feature set were calculated and represented as similarity features by. Malware classification using XGboost-Gradient Boosted Decision Tree. Article history: Received: 31 July, 2020 Accepted: 06 September, 2020 Online: 26 September, 2020 In this industry 4.0 and digital era, we are more dependent on the use of communication and various transaction such as financial, exchange of information by various means Vasan et al. proposed a hybrid deep learning model (called 'IMCFN') based on visualization, which uses a fine-tuned CNN architecture for malware detection and classification. Data augmentation, as well as conversion of malware binaries into color images are used to optimize the performance of the IMCFN algorithm and to cope with imbalanced.

Malware Classification using Deep Learning based Feature

  1. High-Performance Virus Detection System by using Deep Learning Ying-Feng Hsu1, Makiko Ito2, Takumi Maruyama3, Morito Matsuoka1, Nicolas Jung4, Yuki Matsumoto4, Daisuke Motooka4, Shota Nakamura4 1 Cybermedia Center, Osaka University, Osaka, Japan Email: {yf.hsu, matsuoka}@cmc.osaka -u.ac.jp 2 Fujitsu Laboratories Ltd., Kanagawa, Japan Email: maki-ito@jp.fujitsu.co
  2. The code in this repository accompanies a talk by Hyrum Anderson, and has some great feature extraction and deep learning code for malware classification of Windows PE binaries. In particular, the PEFeatureExtractor class extracts a comprehensive set of static features, including raw features that don't require the parsing of the PE.
  3. Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges
  4. method for deep learning based security applications. Using a mixture regression model enhanced by fused lasso, LEMNA generates high-fidelity explanation results for a range of deep learning models including RNN. •We evaluate LEMNA using two popular security applications, including PDF malware classification and function start de
  5. To fill the gap in the literature, this paper, first, evaluates the classical MLAs and deep learning architectures for malware detection, classification, and categorization using different public and private datasets. Second, we remove all the dataset bias removed in the experimental analysis by having different splits of the public and private.
  6. Deep learning method plays a better generalization performance of the classification and can learn more about cell cases of complex function. As it illustrates in Fig. 11 with different samples of malicious code or benign, when we use deep belief network combined with different feature, it is clearly to see the gap between the use of image.

Many new solutions use syntactic features and machine learning techniques to classify Android malware. It has been known that analysis of the Function Call Graph (FCG) can capture behavioral features of malware well. This paper presents a new approach to classifying Android malware based on deep learning and OpCode-level FCG To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware)

JOURNAL OF IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING, VOL. X, NO.X, MONTH 2017 3 in a sandbox and then converts logs to a binary vector. They used deep belief network for classification and reportedly achieved 98.6% accuracy. In another study, Pascanu et al. [39] proposed a method to model malware execution using natural language modeling. They extracted relevant features using recurrent. In this paper, we propose DroidVecDeep, an Android malware detection method using deep learning based on word2vec embeddings. In this work, we firstly extracted a total of 240 features from 4 main static feature types of Android apps, and then use word embeddings for characterization DOI: 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094 Corpus ID: 226850347. Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning @article{Mahdavifar2020DynamicAM, title={Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning}, author={S. Mahdavifar and A. A. Kadir and Rasool Fatemi and Dima Alhadidi and A. Ghorbani}, journal.


Deep Learning and LSTM based Malware Classification by

This book will address the question of how deep learning and artificial intelligence methods can be used to advance the fields of malware detection and analysis. It addresses the challenges of applying AI and DL algorithms to particularly challenging cases, such as obfuscated malware In the recent years, the application of malware detection mechanisms utilize through data mining techniques through have increased using machine learning to recognize malicious files [1, 2].Machine learning methods can take in hidden examples from a given preparing set which includes both malware and benign examples Some ensemble learning methods [60, 61] and deep learning methods have a good effect on the classification. In the following research, we will consider these learning methods. Thirdly, identifier renaming, string encryption, Java reflection, packing and control flow obfuscation technology are widely used in Android apps . The obfuscation of.


Comparison Deep Learning Method to Traditional Methods Using for Network Intrusion Detection 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. deep-seated network for classification and allegedly achieved 98.6 percent accuracy. In another study, Pascanu et al. suggested a method for modeling malware execution using natural language processing. They extracted the relevant features using a recurrent neural network to predict future API calls

(PDF) Deep Learning Based Malware Classification Using

Malware Classification using classical Machine Learning

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI In this paper, we propose CDGDroid, an effective approach for Android malware detection based on deep learning. We use the semantics graph representations, that is, control flow graph, data flow graph, and their possible combinations, as the features to characterise Android applications Deep learning can be used for various real world applications including speech recognition, malware detection and classification, natural language processing, bioinformatics, computer vision and many others. TensorFlow: A Python based open source software library for deep learning TensorFlow (tensorflow.org) is a powerful open source software. 2.1. Deep Learning. In 2006, Hinton and Salakhutdinov published an article in the Science journal that was a gateway to the age of DL. They showed that a neural network with hidden layers played a key role in increasing the learning power of features Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector

(PDF) OpCode-Level Function Call Graph Based Android

Using deep learning methods to detect malware in Android

Based on command and control (C2) traffic from malware, such as Sality and Emotet, this blog analyzes how deep learning models are further able to identify modified and incomplete C2 traffic packets. This analysis illustrates that the usage of machine learning techniques in IPS can discover yet unseen variants of C2 traffic and can help detect. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems Deep learning is a class of machine learning algorithms that (pp199-200) uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.. Overview. Most modern deep learning models are based on. In recent years, deep convolution neural networks (DCNNs) have won many contests in machine learning, object detection, and pattern recognition. Furthermore, deep learning techniques achieved exceptional performance in image classification, reaching accuracy levels beyond human capability. Malware variants from similar categories often contain similarities due to code reuse

Applying Deep Learning for PE-Malware Classificatio

Applying Deep Learning for PE-Malware Classification. 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 using Deep. protocols traffic classification, using supervised learning methods. We offer a solution that can detect previously unknown malware, based on previously learned ones. Our solution is dynamically adaptive, always remaining one step ahead of attackers. These traits enable us to discover maliciou Deep learning methods are impressively outperforming traditional methods on such tasks as image and text classification. With these developments, there's great potential for building novel threat detection methods using deep learning. Machine learning algorithms work with numbers,. About Jon Barker Jon Barker is a Senior Research Scientist in the Applied Deep Learning Research team at NVIDIA. Jon joined NVIDIA in 2015 and has worked on a broad range of applications of deep learning including object detection and segmentation in satellite imagery, optical inspection of manufactured GPUs, malware detection, resumé ranking and audio denoising

the identification of malware data into their respective classes, whereas Liu et al. [3] use a clustering algorithm to discover new malware families as part of a ML-based malware analysis system. Others have expanded upon the traditional ML approaches using deep learning techniques. For example, Kalash et al. [4] propose Malicious software, called malware, can perform harmful actions on computer systems, which may cause economic damage and information leakage. Therefore, malware classification is meaningful and required to prevent malware attacks. Application programming interface (API) call sequences are easily observed and are good choices as features for malware classification D. Yuxin and Z. Siyi, Malware detection based on deep learning algorithm, Neural Comput. Appl. 31(2) (2019) 461-472. Crossref, ISI, Google Scholar; 22. S. Tobiyama et al., Malware detection with deep neural network using process behavior, in 2016 IEEE 40th Annual Computer Software and Applications Conf., 2016, pp. 577-582 Malware developers have developed methods for sandboxing and analysis environments by performing various checks to see if there is an actual user Deep Instinct is revolutionizing cybersecurity with its unique Deep learning Software - harnessing the power of deep learning architecture and yielding unprecedented prediction models, designed to.

Microsoft, Intel use Deep Learning to determine malware

Malware Classification with Deep Convolutional Neural

harm the running system. Therefore, unsupervised methods are investigated, having the positive properties of performing the same task, but not using labeled input data. Recently, deep learning techniques are increasingly inves­ tigated because of their success in range of domains. In that direction, Malhotra et al. [25] used stacked recurren of machine learning and deep learning in cybersecurity issues. Machine learning techniques have been applied for major challenges in cybersecurity issues like intrusion detection, malware classification and detection, spam detection and phishing detection. Although machine learning cannot automat Microsoft, Intel Combine Deep Learning and Pixels to Nix Malware. By John P. Mello Jr. May 13, 2020 10:11 AM PT. Microsoft and Intel researchers have found a way to combine artificial intelligence. diseases, disease detection and its classification using traditional methods, machine learning and deep learning. The survey revealed that the adoption of traditional methods, machine learning techniques are still inefficient. While deep learning methods delivered superior results for disease identification an experts is slow and time-consuming, this research project aims to develop a deep learning-based method for automatic virus detection. There are four virus species in this thesis, they are SARS, MERS, HIV, and COVID-19. This study is based on classification and bounding box regression

title = Malware detection using deep transferred generative adversarial networks, abstract = Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic The manuscript presents three-class classification namely person is wearing a mask, or improperly worn masks or no mask detected. Using our deep learning method called Facemasknet, we got an accuracy of 98.6 %. The Facemasknet can work with still images and also works with a live video stream

Machine learning methods in Cyber Security (Malware analysis, network flows) Uncertainty methods in deep learning Classification of metamorphic malware with deep learning. A.F. Yazi Sensor Based Cyber Attack Detections in Critical Infrastructures using Deep Learning Algorithms. M. Yılmaz; 2018: Classification and static detection of. Methods: In this study, a Deep Learning (DL) method for COVID-19 diagnostic and prognostic analysis using Computed Tomography (CT) scans is studied. Based on the COVID-19 CT images, it is aimed to diagnose COVID- 19 at an early stage. Thus, it may take place before a clinical diagnosis before pathogenic testing

(PDF) Generative Adversarial Network for Improving DeepDeepSign: Deep Learning for Automatic Malware Signature

A Novel Automatic Method for Cassava Disease Classification Using Deep Learning . Isaman Sangbamrung1, Panchalee Praneetpholkrang2, and Sarunya Kanjanawattana1 . 1 Computer Engineering, Institute of Engineering, Suranaree University of Technology, Nakhonratchasima, Thailand . 2 School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, Japa Deep learning methods are widely used in a variety of studies such as image classification, segmentation, and skin disease detection of medical statistics [24, 25]. The study proposed a state-of-the-art deep learning image classifier, namely COV-MCNet (Multi-classification network) based o Deep Learning methods work better if you have more data. A Novel Approach For Encrypted Traffic Classification Using Deep Learning; Deep Learning at the shallow end: Malware classification. Image Category Classification Using Deep Learning. This example uses: You can easily extract features from one of the deeper layers using the activations method. Selecting which of the deep layers to choose is a design choice, but typically starting with the layer right before the classification layer is a good place to start..

Deep Learning and LSTM based Malware Classification | byDeep Learning At The Shallow End: Malware Classification

Overall, this paper paves way for an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. AB - Security breaches due to attacks by malicious software (malware) continue to escalate posing a major security concern in this digital age optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescenc To thwart attempts at having their malware analyzed and then detected, malware authors will use anti-virtual machine (ant-VM) techniques. By adopting the technique the malware is designed to detect whether it is running inside a virtual machine, if a virtual machine is detected the malware will then act differently or just not run at all Furthermore, deep learning using CNN is considered one of the best choices in medical imaging applications 20, especially classification. CNNs are more appropriate for large datasets. CNNs are. It was natural for anti-malware companies to start augmenting their malware detection and classification with machine learning, a computer science area that has shown great success in image recognition, searching and decision- making. Today, machine learning augments malware detection using various kinds of data on host, network and cloud-based.

Methods based on traditional machine learning often require a lot of time and resources in sample labeling, which results in a sufficient inventory of unlabeled samples but not directly usable. In view of these issues, this paper proposes an effective malware classification framework based on malware visualization and semi-supervised learning 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 Plant Disease Classification Using SOFT COMPUTING Supervised Machine Learning: 31: Secure and Hassle-Free EVM Through Deep Learning Based Face Recognition: 32: Credit Risk Prediction Based on Machine Learning Methods: 33: Survey on Machine Learning and Deep Learning Algorithms used in Internet of Things (IoT) Healthcare: 3 Deep Learning Papers on Security. A Deep Learning Approach for Network Intrusion Detection System. A Hybrid Malicious Code Detection Method based on Deep Learning. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. A Multi-task Learning Model for Malware Classification with Useful.

Classification Diagram of Machine Learning Based Malware

Deep learning for classification of malware system call sequences. In Australasian Joint Conference on Artificial Intelligence, pages 137-149. Springer, 2016. Shun Tobiyama, Yukiko Yamaguchi, Hajime Shimada, Tomonori Ikuse, and Takeshi Yagi. Malware detection with deep neural network using process behavior Using this threshold, we detected approximately 60% of the malware samples from our test collection. Adversarial attack algorithm. To attack the neural network, we use the gradient method described in Practical black-box attacks against machine learning. For a malware file we want to change the score of the classifier to avoid detection Categorization of Deep Learning algorithms and their use cases in the Telecom Industry. Advantages of Deep Learning in Mobile and Wireless Networking. The Telecom industry acknowledges several benefits of employing Deep Learning to address network engineering problems: Traditional ML algorithms require feature engineering, which is expensive Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning where pre-trained models are used as the starting point on computer vision and natural language processing tasks given the vast compute and time resources required t