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An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers
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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade the detection rates of current NIDSs, thorough analyses are essential to identify where ML predictors outperform them. The first step is to provide assessment of most used NIDS worldwide, Snort, and comparing its performance with ML classifiers. This paper provides an empirical study to evaluate performance of Snort and four supervised ML classifiers, KNN, Decision Tree, Bayesian net and Naïve Bays against network attacks, probing, Brute force and DoS. By measuring Snort metric, True Alarm Rate, F-measure, Precision and Accuracy and compares them with the same metrics conducted from applying ML algorithms using Weka tool. ML classifiers show an elevated performance with over 99% correctly classified instances for most algorithms, While Snort intrusion detection system shows a degraded classification of about 25% correctly classified instances, hence identifying Snort weaknesses towards certain attack types and giving leads on how to overcome those weaknesses. 

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Publication Date
Mon Jan 01 2024
Journal Name
Lecture Notes In Networks And Systems
Using Machine Learning to Control Congestion in SDN: A Review
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Publication Date
Sun Jul 30 2023
Journal Name
American Journal Of Environmental Economics
Impact of Brand Capital on the Stock Price Crash Risk, an Empirical Study
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The factors influencing the financial market are rapidly becoming more complex. The impact of non-financial factors on the performance of a company’s common stock can increase in ways that were not previously expected. This study investigated how brand capital affects the risk of stock prices in Iraqi private banks listed on the Iraq Stock Exchange failing by identifying the likelihood of a crash caused by a negative deviation in the distribution of returns on ordinary shares. As a result, the current study’s concept is to review an analytical knowledge framework of the nature of that relationship, its changes, and its impact on the pricing of ordinary shares of the banks of the researched sector for the years 2009 to 2017, as w

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Publication Date
Sat Aug 10 2024
Journal Name
Cureus
Machine Learning and Vision: Advancing the Frontiers of Diabetic Cataract Management
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Publication Date
Sun May 01 2022
Journal Name
Journal Of Engineering
Performance Analysis of different Machine Learning Models for Intrusion Detection Systems
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In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

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Publication Date
Fri Jan 01 2016
Journal Name
Procedia Economics And Finance
Impact of Information Technology Infrastructure on Innovation Performance: An Empirical Study on Private Universities In Iraq
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Publication Date
Wed Oct 01 2014
Journal Name
Al–bahith Al–a'alami
The Arts of Media Writing / An Empirical Study on the Privacy of Media Writing
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Media writing is accuracy writing. Clarity and concision are its predominant features. It is a writing that goes straight to the essence because it has no time to waste. Furthermore, it must be as accurate as scientific writing. It is destined for the average reader and has to be understood by everyone. However, it can be as elegant as literary writing. The variety in its forms of expression does not prevent media writing from having its own amplitude.

In short, this study is a practical approach that aims at studying different kinds of writing styles and identifying the specificity of media writing using some patterns and examples

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Publication Date
Fri Jul 30 2021
Journal Name
Iraqi Journal For Electrical And Electronic Engineering
EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm
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The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi

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Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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Publication Date
Sun Jul 01 2018
Journal Name
Journal Of Aerosol Science
On the design of miniature parallel-plate differential mobility classifiers
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Publication Date
Thu Feb 21 2019
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Analysis Tax Advantage of Financing Lease: An Empirical Study
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The Purpose of this study are  analyze financial lease advantage through analyze and discuss financial lease cost, and achieve tax advantage to reach study objective. study include two firms ,oil firm and construction firm with limited liability. The inductive method is used for the applied part in analyzing the financial data of the companies considered in 2011-2015.The result of the study shows that the financial  lease achieve present value of the costs is positive. This study found out the results that verify the hypothesis: The tax advantage of financial Leasing is characterized by decreasing cost and achieving higher tax shield. The study also found the most important recommendations of awareness of the benefits arising f

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