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Development of an Optimized Botnet Detection Framework based on Filters of Features and Machine Learning Classifiers using CICIDS2017 Dataset
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Abstract<p>Botnet is a malicious activity that tries to disrupt traffic of service in a server or network and causes great harm to the network. In modern years, Botnets became one of the threads that constantly evolving. IDS (intrusion detection system) is one type of solutions used to detect anomalies of networks and played an increasing role in the computer security and information systems. It follows different events in computer to decide to occur an intrusion or not, and it used to build a strategic decision for security purposes. The current paper <italic>suggests</italic> a hybrid detection Botnet model using machine learning approach, performed and analyzed to detect Botnet attacks using CICIDS2017 dataset. The proposed model designed based on two types of filters to the botnet features; Correlation Attribute Eval and Principal Component deployed to reduce the dataset dimensions and to decrease the time complexity of the botnet detection process. The detection enhancement achieved by reducing the features of the dataset from 85 to 9. The training stage of classifiers is developed and compared based on six classifiers called (Random Forest, IBK, JRip, Multilayer Perceptron, Naive Bayes and OneR) evaluated to accomplish an optimized detection model. The performance and results of the proposed framework are validated using well-known metrics such as Accuracy (ACC), Precision (Pr), Recall (Rc) and F-Measure (F1). The consequence is that the combination of Correlation Attribute Eval (filter) with JRip (classifier) together can satisfy significant improvement in the Botnet detection process using CICIDS2017 dataset.</p>
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Publication Date
Thu Jan 01 2015
Journal Name
Iraqi Journal Of Science
Keystroke Dynamics Authentication based on Naïve Bayes Classifier
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Authentication is the process of determining whether someone or something is, in fact, who or what it is declared to be. As the dependence upon computers and computer networks grows, the need for user authentication has increased. User’s claimed identity can be verified by one of several methods. One of the most popular of these methods is represented by (something user know), such as password or Personal Identification Number (PIN). Biometrics is the science and technology of authentication by identifying the living individual’s physiological or behavioral attributes. Keystroke authentication is a new behavioral access control system to identify legitimate users via their typing behavior. The objective of this paper is to provide user

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Publication Date
Sun Sep 24 2023
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Iris Data Compression Based on Hexa-Data Coding
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Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the origin

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Crossref
Publication Date
Thu Jan 01 2015
Journal Name
Journal Of Engineering
GNSS Baseline Configuration Based on First Order Design
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The quality of Global Navigation Satellite Systems (GNSS) networks are considerably influenced by the configuration of the observed baselines. Where, this study aims to find an optimal configuration for GNSS baselines in terms of the number and distribution  of baselines to improve the quality criteria of the GNSS networks. First order design problem (FOD) was applied in this research to optimize GNSS network baselines configuration, and based on sequential adjustment method to solve its objective functions.

FOD for optimum precision (FOD-p) was the proposed model which based on the design criteria of A-optimality and E-optimality. These design criteria were selected as objective functions of precision, whic

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Publication Date
Sun Apr 30 2023
Journal Name
Iraqi Journal Of Science
Keystroke Dynamics Authentication based on Naïve Bayes Classifier
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Authentication is the process of determining whether someone or something is,
in fact, who or what it is declared to be. As the dependence upon computers and
computer networks grows, the need for user authentication has increased. User’s
claimed identity can be verified by one of several methods. One of the most popular
of these methods is represented by (something user know), such as password or
Personal Identification Number (PIN). Biometrics is the science and technology of
authentication by identifying the living individual’s physiological or behavioral
attributes. Keystroke authentication is a new behavioral access control system to
identify legitimate users via their typing behavior. The objective of thi

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Publication Date
Fri Jan 26 2024
Journal Name
Iraqi Journal Of Science
A Secure Session Management Based on Threat Modeling
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A session is a period of time linked to a user, which is initiated when he/she arrives at a web application and it ends when his/her browser is closed or after a certain time of inactivity. Attackers can hijack a user's session by exploiting session management vulnerabilities by means of session fixation and cross-site request forgery attacks.
Very often, session IDs are not only identification tokens, but also authenticators. This means that upon login, users are authenticated based on their credentials (e.g., usernames/passwords or digital certificates) and issued session IDs that will effectively serve as temporary static passwords for accessing their sessions. This makes session IDs a very appealing target for attackers. In many c

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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Short Answers Assessment Approach based on Semantic Network
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      Finding similarities in texts is important in many areas such as information retrieval, automated article scoring, and short answer categorization. Evaluating short answers is not an easy task due to differences in natural language. Methods for calculating the similarity between texts depend on semantic or grammatical aspects. This paper discusses a method for evaluating short answers using semantic networks to represent the typical (correct) answer and students' answers. The semantic network of nodes and relationships represents the text (answers). Moreover, grammatical aspects are found  by measuring the similarity of parts of speech between the answers. In addition, finding hierarchical relationships between nodes in netwo

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Publication Date
Wed May 01 2019
Journal Name
Iraqi Journal Of Science
Optical Images Fusion Based on Linear Interpolation Methods
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Merging images is one of the most important technologies in remote sensing applications and geographic information systems. In this study, a simulation process using a camera for fused images by using resizing image for interpolation methods (nearest, bilinear and bicubic). Statistical techniques have been used as an efficient merging technique in the images integration process employing different models namely Local Mean Matching (LMM) and Regression Variable Substitution (RVS), and apply spatial frequency techniques include high pass filter additive method (HPFA).  Thus, in the current research, statistical measures have been used to check the quality of the merged images. This has been carried out by calculating the correlation a

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Publication Date
Wed Aug 30 2023
Journal Name
Iraqi Journal Of Science
Network Traffic Prediction Based on Time Series Modeling
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    Predicting the network traffic of web pages is one of the areas that has increased focus in recent years. Modeling traffic helps find strategies for distributing network loads, identifying user behaviors and malicious traffic, and predicting future trends. Many statistical and intelligent methods have been studied to predict web traffic using time series of network traffic. In this paper, the use of machine learning algorithms to model Wikipedia traffic using Google's time series dataset is studied. Two data sets were used for time series, data generalization, building a set of machine learning models (XGboost, Logistic Regression, Linear Regression, and Random Forest), and comparing the performance of the models using (SMAPE) and

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Publication Date
Mon Mar 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
Speech Enhancement Algorithm Based on a Hybrid Estimator
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Abstract<p>Speech is the essential way to interact between humans or between human and machine. However, it is always contaminated with different types of environment noise. Therefore, speech enhancement algorithms (SEA) have appeared as a significant approach in speech processing filed to suppress background noise and return back the original speech signal. In this paper, a new efficient two-stage SEA with low distortion is proposed based on minimum mean square error sense. The estimation of clean signal is performed by taking the advantages of Laplacian speech and noise modeling based on orthogonal transform (Discrete Krawtchouk-Tchebichef transform) coefficients distribution. The Discrete Kra</p> ... Show More
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Crossref (10)
Crossref
Publication Date
Thu Aug 01 2019
Journal Name
2019 2nd International Conference On Engineering Technology And Its Applications (iiceta)
Human Gait Identification System Based on Average Silhouette
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