A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques

This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficulty of reversing a ransomware infection, and how important it is to detect it before infecting a system. Machine learning is coming to the forefront of combatting ransomware, so we attempted to identify weaknesses in machine learning approaches and how they can be strengthened. The threat posed by ransomware is exceptionally high, with new variants and families continually being found on the internet and dark web. Recovering from ransomware infections is difficult, given the nature of the encryption schemes used by them. The increase in the use of artificial intelligence also coincides with this boom in ransomware. The exploration into machine learning and deep learning approaches when it comes to detecting ransomware poses high interest because machine learning and deep learning can detect zero-day threats. These techniques can generate predictive models that can learn the behaviour of ransomware and use this knowledge to detect variants and families which have not yet been seen. In this survey, we review prominent research studies which all showcase a machine learning or deep learning approach when detecting ransomware malware. These studies were chosen based on the number of citations they had by other research. We carried out experiments to investigate how the discussed research studies are impacted by malware evolution. We also explored the new directions of ransomware and how we expect it to evolve in the coming years, such as expansion into IoT (Internet of Things), with IoT being integrated more into infrastructures and into homes.

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Deep Learning Approaches for Sentiment Analysis Challenges and Future Issues

10.4018/978-1-7998-8161-2.ch003 ◽

2022 ◽

pp. 27-50

Author(s):

Rajalaxmi Prabhu B. ◽

Seema S.

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Model Building ◽

Large Data ◽

Machine Learning Algorithms ◽

Large Data Sets ◽

Data Sets ◽

Learning Approaches ◽

Learning Techniques ◽

Important Challenge

A lot of user-generated data is available these days from huge platforms, blogs, websites, and other review sites. These data are usually unstructured. Analyzing sentiments from these data automatically is considered an important challenge. Several machine learning algorithms are implemented to check the opinions from large data sets. A lot of research has been undergone in understanding machine learning approaches to analyze sentiments. Machine learning mainly depends on the data required for model building, and hence, suitable feature exactions techniques also need to be carried. In this chapter, several deep learning approaches, its challenges, and future issues will be addressed. Deep learning techniques are considered important in predicting the sentiments of users. This chapter aims to analyze the deep-learning techniques for predicting sentiments and understanding the importance of several approaches for mining opinions and determining sentiment polarity.

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Emotion analysis of Arabic tweets using deep learning approach

Journal Of Big Data ◽

10.1186/s40537-019-0252-x ◽

2019 ◽

Vol 6 (1) ◽

Author(s):

Massa Baali ◽

Nada Ghneim

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Media Analysis ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Learning Approaches ◽

Social Media Analysis ◽

Deep Convolutional Neural Networks ◽

Emotion Analysis ◽

Classification Tasks

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

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Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Sensors ◽

10.3390/s19235213 ◽

2019 ◽

Vol 19 (23) ◽

pp. 5213 ◽

Cited By ~ 4

Author(s):

Donato Impedovo ◽

Fabrizio Balducci ◽

Vincenzo Dentamaro ◽

Giuseppe Pirlo

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Traffic Flow ◽

Traffic Congestion ◽

Machine Learning Techniques ◽

Visual Features ◽

Learning Approaches ◽

Traffic State ◽

State Classification ◽

Learning Techniques

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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Iterative machine learning applied to annotation of text datasets

10.5753/eniac.2021.18268 ◽

2021 ◽

Author(s):

Thiago Abdo ◽

Fabiano Silva

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithms ◽

Computational Cost ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Learning Techniques ◽

The Creation ◽

The Impact ◽

High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

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Deep Learning Approaches for Protein Structure Prediction

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.5.20037 ◽

2018 ◽

Vol 7 (4.5) ◽

pp. 168

Author(s):

Khatri Chandni ◽

Prof. Mrudang Pandya ◽

Dr. Sunil Jardosh

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Protein Structure ◽

Protein Structure Prediction ◽

Structure Prediction ◽

Machine Learning Techniques ◽

Great Promise ◽

Learning Approaches ◽

Learning Networks ◽

Learning Techniques

In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks and Machine Learning. Deep Learning solves severa problems in bioinformatics. Protein Structure Prediction is one of the most important fields that can be solving using Deep Learning approaches.These protein are categorized on basis of occurrence of amino acid patterns occur to extract the feature. In these paper aimed to review work based on protein structure prediction solve using Deep Learning Networks. Objective is to review motivate and facilitatethese deep learn the network for predicting protein sequences using Deep Learning.

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Extraction of drug–drug interaction using neural embedding

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720018400279 ◽

2018 ◽

Vol 16 (06) ◽

pp. 1840027 ◽

Cited By ~ 2

Author(s):

Wen Juan Hou ◽

Bamfa Ceesay

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Drug Interaction ◽

Relevant Information ◽

Learning System ◽

Machine Learning Techniques ◽

Future Research ◽

Learning Approach ◽

Learning Techniques ◽

Drug Drug Interaction

Information on changes in a drug’s effect when taken in combination with a second drug, known as drug–drug interaction (DDI), is relevant in the pharmaceutical industry. DDIs can delay, decrease, or enhance absorption of either drug and thus decrease or increase their action or cause adverse effects. Information Extraction (IE) can be of great benefit in allowing identification and extraction of relevant information on DDIs. We here propose an approach for the extraction of DDI from text using neural word embedding to train a machine learning system. Results show that our system is competitive against other systems for the task of extracting DDIs, and that significant improvements can be achieved by learning from word features and using a deep-learning approach. Our study demonstrates that machine learning techniques such as neural networks and deep learning methods can efficiently aid in IE from text. Our proposed approach is well suited to play a significant role in future research.

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A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽

10.1155/2019/1306039 ◽

2019 ◽

Vol 2019 ◽

pp. 1-22 ◽

Cited By ~ 15

Author(s):

Antonio Hernández-Blanco ◽

Boris Herrera-Flores ◽

David Tomás ◽

Borja Navarro-Colorado

Keyword(s):

Machine Learning ◽

Data Mining ◽

Deep Learning ◽

Language Processing ◽

Educational Data Mining ◽

Research Field ◽

Machine Learning Techniques ◽

Mining Machine ◽

Learning Approaches ◽

Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

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Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i3.1818 ◽

2021 ◽

Vol 12 (3) ◽

pp. 4396-4405

Author(s):

R Kanthavel Et.al

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Elastic Tissue ◽

Learning Approaches ◽

X Rays ◽

Imaging Analysis ◽

Bone Injury ◽

Learning Techniques

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.

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