scholarly journals Expert Information Prediction Modeling In Pcap Files

2021 ◽  
Vol 12 (2) ◽  
pp. 2978-2985
Author(s):  
S. Leelalakshmi, Et. al.
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Security Threats ◽  
Web Traffic ◽  
Prediction Modeling ◽  
Network Information ◽  
Different Types ◽  
Data Environment ◽  
The Web

The cyber security field has more challenges and analysing pcap files for identifying the network traces provides important details. The web traffic consists of different types of transfer. Some data can be malicious and it can fall into different categories. Analysing and extracting features and applyying machine learning algorithms can prove to be more useful for indentifying the network information. Todays’ big data environment provides more way to security threats.

Applications of Machine Learning in Cyber Security

2020 ◽  
pp. 83-103 ◽  
Author(s):  
Charu Virmani ◽  
Tanu Choudhary ◽  
Anuradha Pillai ◽  
Manisha Rani
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Technological Systems ◽  
The World ◽  
Computer Scientists ◽  
Potential Risks ◽  
To Come ◽  
Time Basis ◽  
Applications Of Machine Learning

With the exponential rise in technological awareness in the recent decades, technology has taken over our lives for good, but with the application of computer-aided technological systems in various domains of our day-to-day lives, the potential risks and threats have also come to the fore, aiming at the various security features that include confidentiality, integrity, authentication, authorization, and so on. Computer scientists the world over have tried to come up, time and again, with solutions to these impending problems. With time, attackers have played out complicated attacks on systems that are hard to comprehend and even harder to mitigate. The very fact that a huge amount of data is processed each second in organizations gave birth to the concept of Big Data, thereby making the systems more adept and intelligent in dealing with unprecedented attacks on a real-time basis. This chapter presents a study about applications of machine learning algorithms in cyber security.

Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security

2021 ◽  
pp. 1085-1099
Author(s):  
S. Abijah Roseline ◽  
S. Geetha
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Security Threat ◽  
Signature Detection ◽  
Learning Techniques ◽  
Feature Based

Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.

scholarly journals Heterogeneous Network Representation Learning

2020 ◽  
Author(s):  
Yuxiao Dong ◽  
Ziniu Hu ◽  
Kuansan Wang ◽  
Yizhou Sun ◽  
Jie Tang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Heterogeneous Network ◽  
Representation Learning ◽  
Machine Learning Algorithms ◽  
Future Directions ◽  
Relational Properties ◽  
Open Research ◽  
Different Types ◽  
Graph Neural Networks

Representation learning has offered a revolutionary learning paradigm for various AI domains. In this survey, we examine and review the problem of representation learning with the focus on heterogeneous networks, which consists of different types of vertices and relations. The goal of this problem is to automatically project objects, most commonly, vertices, in an input heterogeneous network into a latent embedding space such that both the structural and relational properties of the network can be encoded and preserved. The embeddings (representations) can be then used as the features to machine learning algorithms for addressing corresponding network tasks. To learn expressive embeddings, current research developments can fall into two major categories: shallow embedding learning and graph neural networks. After a thorough review of the existing literature, we identify several critical challenges that remain unaddressed and discuss future directions. Finally, we build the Heterogeneous Graph Benchmark to facilitate open research for this rapidly-developing topic.

A Machine Learning-Based Lexicon Approach for Sentiment Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 8-22
Author(s):  
Tirath Prasad Sahu ◽  
Sarang Khandekar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Main Idea ◽  
Machine Learning Algorithms ◽  
Online Review ◽  
Product Reviews ◽  
Huge Amount ◽  
Text Documents ◽  
Proposed Model ◽  
The Web

Sentiment analysis can be a very useful aspect for the extraction of useful information from text documents. The main idea for sentiment analysis is how people think for a particular online review, i.e. product reviews, movie reviews, etc. Sentiment analysis is the process where these reviews are classified as positive or negative. The web is enriched with huge amount of reviews which can be analyzed to make it meaningful. This article presents the use of lexicon resources for sentiment analysis of different publicly available reviews. First, the polarity shift of reviews is handled by negations. Intensifiers, punctuation and acronyms are also taken into consideration during the processing phase. Second, words are extracted which have some opinion; these words are then used for computing score. Third, machine learning algorithms are applied and the experimental results show that the proposed model is effective in identifying the sentiments of reviews and opinions.

Detection of Bot Accounts on Social Media Considering Its Imbalanced Nature

2021 ◽  
pp. 162-176
Author(s):  
Isha Y. Agarwal ◽  
Dipti P. Rana ◽  
Devanshi Bhatia ◽  
Jay Rathod ◽  
Kaneesha J. Gandhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Machine Learning Algorithms ◽  
Security Threats ◽  
Private Data ◽  
Normal Behavior ◽  
Social Media Platforms ◽  
Harmful Content ◽  
Negative Impacts ◽  
Ease Of Access

Social media has completely transformed the way people communicate. However, every revolution brings with it some negative impacts. Due to its popularity amongst tons of global users, these platforms have a huge volume of data. The ease of access with minimal verification of new users on social media has led to the creation of the bot accounts used to collect private data, spread false and harmful content, and also poses many security threats. A lot of concerns have been raised with the increment in the quantity of bot accounts on different social media platforms. Also there is a high imbalance between bot and non-bot accounts where the imbalance is a result of 'normal behavior' of bot users. The research aims at identifying the artificial bots accounts on Twitter using various machine learning algorithms and content-based classification based on features provided on the platform and recent tweets of users respectively.

A Review of Regression Models in Machine Learning

2021 ◽  
Vol 2 (2) ◽  
pp. 40-47
Author(s):  
Sunil Kumar ◽  
Vaibhav Bhatnagar
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Regression Model ◽  
Regression Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Analysis Model ◽  
Data Set ◽  
Data Regression ◽  
Different Types

Machine learning is one of the active fields and technologies to realize artificial intelligence (AI). The complexity of machine learning algorithms creates problems to predict the best algorithm. There are many complex algorithms in machine learning (ML) to determine the appropriate method for finding regression trends, thereby establishing the correlation association in the middle of variables is very difficult, we are going to review different types of regressions used in Machine Learning. There are mainly six types of regression model Linear, Logistic, Polynomial, Ridge, Bayesian Linear and Lasso. This paper overview the above-mentioned regression model and will try to find the comparison and suitability for Machine Learning. A data analysis prerequisite to launch an association amongst the innumerable considerations in a data set, association is essential for forecast and exploration of data. Regression Analysis is such a procedure to establish association among the datasets. The effort on this paper predominantly emphases on the diverse regression analysis model, how they binning to custom in context of different data sets in machine learning. Selection the accurate model for exploration is the most challenging assignment and hence, these models considered thoroughly in this study. In machine learning by these models in the perfect way and thru accurate data set, data exploration and forecast can provide the maximum exact outcomes.

scholarly journals Optimized Classification Predictions with a New Index Combining Machine Learning Algorithms

2018 ◽  
Vol 27 (03) ◽  
pp. 1850012 ◽  
Author(s):  
Androniki Tamvakis ◽  
Christos-Nikolaos Anagnostopoulos ◽  
George Tsirtsis ◽  
Antonios D. Niros ◽  
Sofie Spatharis
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Individual Performance ◽  
Optimal Combination ◽  
Machine Learning Algorithms ◽  
Combined Effect ◽  
Different Types ◽  
Open Issue ◽  
The Relationship ◽  
Selection Of

Voting is a commonly used ensemble method aiming to optimize classification predictions by combining results from individual base classifiers. However, the selection of appropriate classifiers to participate in voting algorithm is currently an open issue. In this study we developed a novel Dissimilarity-Performance (DP) index which incorporates two important criteria for the selection of base classifiers to participate in voting: their differential response in classification (dissimilarity) when combined in triads and their individual performance. To develop this empirical index we firstly used a range of different datasets to evaluate the relationship between voting results and measures of dissimilarity among classifiers of different types (rules, trees, lazy classifiers, functions and Bayes). Secondly, we computed the combined effect on voting performance of classifiers with different individual performance and/or diverse results in the voting performance. Our DP index was able to rank the classifier combinations according to their voting performance and thus to suggest the optimal combination. The proposed index is recommended for individual machine learning users as a preliminary tool to identify which classifiers to combine in order to achieve more accurate classification predictions avoiding computer intensive and time-consuming search.

scholarly journals On the challenges of predicting microscopic dynamics of online conversations

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
John Bollenbacher ◽  
Diogo Pacheco ◽  
Pik-Mai Hui ◽  
Yong-Yeol Ahn ◽  
Alessandro Flammini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Long Range ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Generative Models ◽  
Machine Learning Algorithms ◽  
Macroscopic Structure ◽  
Online Conversation ◽  
Near Future

AbstractTo what extent can we predict the structure of online conversation trees? We present a generative model to predict the size and evolution of threaded conversations on social media by combining machine learning algorithms. The model is evaluated using datasets that span two topical domains (cryptocurrency and cyber-security) and two platforms (Reddit and Twitter). We show that it is able to predict both macroscopic features of the final trees and near-future microscopic events with moderate accuracy. However, predicting the macroscopic structure of conversations does not guarantee an accurate reconstruction of their microscopic evolution. Our model’s limited performance in long-range predictions highlights the challenges faced by generative models due to the accumulation of errors.

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