ئەز   Adel Sabry Eesa


Assistant professor

Specialties

Artificial Intelligence

Education

Doctor of Philosophy

Computer Science لە Zakho

2015

Master of Science

Computer Science لە Duhok University

2005

Membership


2017

2017-09-09,current
Governance in universities committee

Member

2016

2016-09-01,current
Graduate studies committee at computer science department.

Chairman

2015

2015-01-01,current
scientific committee at the computer science department.

Member

Academic Title

Assistant professor

2019-09-02

Lecturer

2015-03-30

Assistant Lecturer

2010-07-05

Awards

Appreciation Letter

2018-02
Supporting new alumni

Support new alumni, and provide a forum to form new friendships and business relationships with people of similar background.

 2018

Appreciation Letter

2018-02
Quality assurance

Quality assurance

 2018

Appreciation Letter

2017-02
Designing Provender balance system.

Designing Provender balance system For Duhok University

 2017

Published Journal Articles

Intelligent Automation & Soft Computing (Issue : 4) (Volume : 39)
Chase, Pounce, and Escape Optimization Algorithm

While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the... See more

While many metaheuristic optimization algorithms strive to address optimization challenges, they often grapple with the delicate balance between exploration and exploitation, leading to issues such as premature convergence, sensitivity to parameter settings, and difficulty in maintaining population diversity. In response to these challenges, this study introduces the Chase, Pounce, and Escape (CPE) algorithm, drawing inspiration from predator-prey dynamics. Unlike traditional optimization approaches, the CPE algorithm divides the population into two groups, each independently exploring the search space to efficiently navigate complex problem domains and avoid local optima. By incorporating a unique search mechanism that integrates both the average of the best solution and the current solution, the CPE algorithm demonstrates superior convergence properties. Additionally, the inclusion of a pouncing process facilitates rapid movement towards optimal solutions. Through comprehensive evaluations across various optimization scenarios, including standard test functions, Congress on Evolutionary Computation (CEC)-2017 benchmarks, and real-world engineering challenges, the effectiveness of the CPE algorithm is demonstrated. Results consistently highlight the algorithm’s performance, surpassing that of other well-known optimization techniques, and achieving remarkable outcomes in terms of mean, best, and standard deviation values across different problem domains, underscoring its robustness and versatility.

 2024-09
Applied Soft Computing
Letter: Application of Optimization Algorithms to Engineering Design Problems and Discrepancies in Mathematical Formulas

Engineering design optimization problems have attracted the attention of researchers since they appeared. Those who... See more

Engineering design optimization problems have attracted the attention of researchers since they appeared. Those who work on developing optimization algorithms, in particular, apply their developed algorithms to these problems in order to test their new algorithms’ capabilities. The mathematical discrepancy emerges during the implementation of equations and constraints related to these engineering problems. This is due to an error occurring in writing or transmitting these equations from one paper to another. Maintaining these discrepancies will have a negative impact on the assessment and model performance verification of the newly developed algorithms, as well as the decision-making process. To address this issue, this study investigates the mathematical discrepancies occurred by researchers in four well-known engineering design optimization problems (Welded Beam Design WBD, Speed Reducer Design SRD, Cantilever Beam Design CBD, and Multiple Disk Clutch Brake Design MDCBD). We have investigated some of the recently published papers in the literature, identifying discrepancies in their mathematical formulas, and fixing them appropriately by referring and comparing them to the original problem. Furthermore, all mathematical discrepancies , references, parameters, cost functions, constraints, and constraint errors are highlighted, arranged and organized in tables. As a result, this work can help readers and researchers avoid being confused and wasting time when working on these engineering design optimization problems.

 2023-03
Computers, Materials and Continua (Issue : 1) (Volume : 69)
Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and... See more

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The new method aims to avoid generating noise and control imbalances between and within classes. Various experiments have been carried out with six classifiers and four oversampling methods. Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.

 2021-06
Uludağ University Journal of The Faculty of Engineering (Issue : 1) (Volume : 26)
Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are... See more

Nowadays, with the rapid prevalence of networked machines and Internet technologies, intrusion detection systems are increasingly in demand. Consequently, numerous illicit activities by external and internal attackers need to be detected. Thus, earlier detection of such activities is necessary for protecting data and information. In this paper, we investigated the use of the Cuttlefish optimization algorithm as a new rule generation method for the classification task to deal with the intrusion detection problem. The effectiveness of the proposed method was tested using KDD Cup 99 dataset based on different evaluation methods. The obtained results were also compared with the results obtained by some classical well-known algorithms namely Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighborhood (K-NN). Our experimental results showed that the proposed method demonstrates a good classification performance and provides significantly preferable results when compared with the performance of other traditional algorithms. The proposed method produced 93.9%, 92.2%, and 94.7% in terms of precision, recall, and area under curve, respectively.

 2021-04
Computers, Materials and Continua (Issue : 2) (Volume : 68)
Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

Over the past few decades, face recognition has become the most effective biometric technique in... See more

Over the past few decades, face recognition has become the most effective biometric technique in recognizing people's identity, as it is widely used in many areas of our daily lives. However, it is a challenging technique since facial images vary in rotations, expressions, and illuminations. To minimize the impact of these challenges, exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial features. Therefore, this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction, Fast Independent Component Analysis (Fas-tICA), and Linear Discriminant Analysis (LDA). In the presented method, first, face images are transformed to grayscale and resized to have a uniform size. After that, facial features are extracted from the aligned face image using Gabor, FastICA, and LDA methods. Finally, the nearest distance classifier is utilized to recognize the identity of the individuals. Here, the performance of six distance classifiers, namely Euclidean, Cosine, Bray-Curtis, Mahalanobis, Correlation, and Manhattan, are investigated. Experimental results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets: ORL, GT, FEI, and Yale. Moreover, it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.

 2021-04
International Journal of Research-GRANTHAALAYAH (Issue : 8) (Volume : 8)
OPTIMIZATION ALGORITHMS FOR INTRUSION DETECTION SYSTEM: A REVIEW

With the growth and development of the Internet, the devices and the hosts connected to... See more

With the growth and development of the Internet, the devices and the hosts connected to the Internet have become the target for attackers and intruders. Consequently, the integrity of systems and data has become more sophisticated. Meanwhile, many institutions suffer from money-losing or other losses due to attacks on computer systems. Accordingly, the detection of intrusion and attacks has become a challenge and a vital necessity at the same time. Many different methods were used to build intrusion detection systems (IDSs), and all these methods seek to a plus the efficiency of intrusion detection systems. This paper is a survey which tries to covers some of the optimization algorithms used in the field of intrusion detection in past ten years such as Artificial Bee Colony (ABC), Genetic Algorithm (GA), Cuttlefish Algorithms (CFA), and Particle Swarm Optimization (PSO). It is hoped that this review will provide useful insights about the intrusion detection literature and is a good source for anyone interested in applying one of the used optimization algorithms in the field of intrusion detection.

 2020-08
Academic Journal of Nawroz University (Issue : 2) (Volume : 9)
Rule Mining Using Particle Swarm Optimization for Intrusion Detection Systems

Traditional data mining techniques are commonly used to build the Intrusion Detection Systems IDSs. They... See more

Traditional data mining techniques are commonly used to build the Intrusion Detection Systems IDSs. They are designed on the basis of some probabilistic methods that still do not take into account some of the important properties of each feature in the dataset. We believe that each feature in the dataset has its own crucial role for its characteristics, which should be taken into consideration. In this work, instead of using the traditional technique or applying feature selection methods we proposed max and min boundary mining approach to solve Anomaly Intrusion Detection System AIDS problem. The main idea of the proposed method is to handle each feature in the dataset independently extracting two important properties represented by max-boundary and min-boundary. First, Particle Swarm Optimization PSO is used to search for the optimal max and min boundary for each feature in each class from the train data set. Second, the generated max and min boundaries are used as detection rules in order to detect anomalies from normal behavior using test dataset. KDD Cup 99 and the new version of KDD Cup 99 called NSL-KDD datasets are used to test the proposed model and its performance is compared with four well-known techniques such as J48, Naïve Bayes, PART and SMO. In addition, performance is also compared with some recent work. Experiment results show that the proposed model is outperformed all other algorithms in all terms (true positive rate, false positive rate, f-measure, Recall, Precision, MCC and AUC).

 2020-08
Expert Systems
A new clustering method based on the bio-inspired cuttlefish optimization algorithm

Most of the well-known clustering methods based on distance measures, distance metrics and similarity functions... See more

Most of the well-known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio-inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K-means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFAClustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.

 2019-12
Science Journal of University of Zakho (Issue : 4) (Volume : 7)
New Data Hiding Approach Based on Biological Functionality of DNA Sequence

Data hiding or steganography has been used ever since a secret message was needed to... See more

Data hiding or steganography has been used ever since a secret message was needed to be transferred. Data hiding methods need a medium to be cover for secret message that is to be sent. Different mediums are used such as image, video, audio, and last decade the deoxyribose nucleic acid (DNA). In this paper, a new data hiding approach based on the DNA sequence is proposed. Unlike many existing methods, the proposed method does not change the biological functionality of the DNA reference sequence when the sequence is translated into amino acids. The proposed method is consisting of two steps: the first step is encrypting the message using the Toffoli quantum gate. The second step is embedding the encrypted message into DNA sequence by taking one codon at a time and considering amino acids' biological functionality during the embedding process. Experimental results show that the proposed method outperforms the existing schemes preserving biological functionality in terms of cracking probability, and hiding capacity for bit per nucleotide.

 2019-12
International Journal of Advanced Computer Science and Applications, (Issue : 9) (Volume : 8)
Features Optimization for ECG Signals Classification

A new method is used in this work to classify ECG beats. The new method... See more

A new method is used in this work to classify ECG beats. The new method is about using an optimization algorithm for selecting the features of each beat then classify them. For each beat, twenty-four higher order statistical features and three timing interval features are obtained. Five types of beat classes are used for classification in this work, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch (LBBB) and right bundle branch (RBBB). Cuttlefish algorithm is used for feature selection which is a new bio-inspired optimization algorithm. Four classifiers are used within CFA, Scaled Conjugate Gradient Artificial Neural Network (SCG-ANN), K-Nearest Neighborhood (KNN), Interactive Dichotomizer 3 (ID3) and Support Vector Machine (SVM). The final results show an accuracy of 97.96% for ANN, 95.71% for KNN, 94.69% for ID3 and 93.06% for SVM, these results were tested on fourteen signal records from MIT-HIH database, where 1400 beats were extracted from these records.

 2018-01
International Journal of Advanced Computer Science and Applications (Issue : 8) (Volume : 9)
A New Message Encryption Method based on Amino Acid Sequences and Genetic Codes

As the use of technology is increasing rapidly, the amount of shared, sent, and received... See more

As the use of technology is increasing rapidly, the amount of shared, sent, and received information is also increasing in the same way. As a result, this necessitates the need for finding techniques that can save and secure the information over the net. There are many methods that have been used to protect information such as hiding information and encryption. In this study, we propose a new encryption method making use of amino acid and DNA sequences. In addition, several criteria including data size, key size and the probability of cracking are used to evaluate the proposed method. The results show that the performance of the proposed method is better than many common encryption methods, such as RSA in terms of evaluation criteria.

 2018-01
Science Journal of University of Zakho (Issue : 4) (Volume : 5)
A DIDS Based on The Combination of Cuttlefish Algorithm and Decision Tree

Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent... See more

Different Distributed Intrusion Detection Systems (DIDS) based on mobile agents have been proposed in recent years to protect computer systems from intruders. Since intrusion detection systems deal with a large amount of data, keeping the best quality of features that represent the whole data and removing the redundant and irrelevant features are important tasks in these systems. In this paper, a novel DIDS based on the combination of Cuttlefish Optimization Algorithm (CFA) and Decision Tree (DT) is proposed. The proposed system uses an agent called Rule and Feature Generator Agent (RFGA)for reducing the dimensionality of the data by generating a subset of features with their corresponding rules. RFGA agent uses CFA to search for the optimal subset of features, while DT is used as a measurement on the selected features. The proposed model is tested on the KDD Cup 99 dataset. The obtained results show that the proposed system gives a better performance even with a small subset of 5 features when compared with the using all 41 features.

 2017-12
Science Journal of University of Zakho (Issue : 4) (Volume : 5)
Normalization Methods For Backpropagation: A Comparative Study

Neural Networks (NN) have been used by many researchers to solve problems in several domains... See more

Neural Networks (NN) have been used by many researchers to solve problems in several domains including classification and pattern recognition, and Backpropagation (BP) which is one of the most well-known artificial neural network models. Constructing effective NN applications relies on some characteristics such as the network topology, learning parameter, and normalization approaches for the input and the output vectors. The Input and the output vectors for BP need to be normalized properly in order to achieve the best performance of the network. This paper applies several normalization methods on several UCI datasets and comparing between them to find the best normalization method that works better with BP. Norm, Decimal scaling, Mean-Man, Median-Mad, Min-Max, and Z-score normalization are considered in this study. The comparative study shows that the performance of Mean-Mad and Median-Mad is better than all remaining methods. On the other hand, the worst result is produced with Norm method.

 2017-12
Expert Systems with Applications (Issue : 5) (Volume : 42)
A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is... See more

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgment on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate when compared with the obtained results using all features.

 2015-04
Turkish Journal of Electrical Engineering & Computer Sciences (Issue : 2) (Volume : 23)
A new feature selection model based on ID3 and bees algorithm for intrusion detection system

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework.... See more

Intrusion detection systems (IDSs) have become a necessary component of computers and information security framework. IDSs commonly deal with a large amount of data traffic and these data may contain redundant and unimportant features. Choosing the best quality of features that represent all of the data and exclude the redundant features is a crucial topic in IDSs. In this paper, a new combination approach based on the ID3 algorithm and the bees algorithm (BA) is proposed to select the optimal subset of features for an IDS. The BA is used to generate a subset of features, and the ID3 algorithm is used as a classi er. The proposed model is applied to KDD Cup 99 dataset. The obtained results show that the feature subset generated by the proposed ID3-BA gives a higher accuracy and detection rate with a lower false alarm rate when compared to the results obtained by using all features.

 2015-02
af. J. of Comp. & Math’s (Issue : 2) (Volume : 8)
Intrusion Detection System Based on Neural Networks Using Bipolar Input with Bipolar Sigmoid Activation Function

Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are... See more

Vulnerabilities in common security components such as firewalls are inevitable. Intrusion Detection Systems (IDS) are used as another wall to protect computer systems and to identify corresponding vulnerabilities. The purpose of this paper is to use the Backpropagation algorithm for IDS by applying bipolar input “input is represented as (1, -1)”, and bipolar sigmoid activation function. The KDD Cup 99 dataset is used in this paper. The number of train dataset is 4947 connection records, and the number of test dataset is 3117 connection records. The results of the proposed method show that the PSP is 88.32 and CPT equal to 0.286.

 2011-12
Raf. J. of Comp. & Math’ (Issue : 1) (Volume : 8)
Intrusion Detection System Based on Decision Tree and Clustered Continuous Inputs

With the rapid expansion of computer networks during the past decade, security has become a... See more

With the rapid expansion of computer networks during the past decade, security has become a crucial issue for computer systems. Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems (IDSs). The purpose of this paper is to use ID3 algorithm for IDS and extend it to deal not only with discrete values but also with continuous ones, by using K_mean algorithm to partition each continuous attribute values to three clusters. The full 10% KDD Cup 99 train dataset and the full Correct test dataset are used. The results of the proposed method show an improvement in the performance as compared to standard ID3 using classical partition method.

 2011-11
Eng. & Tech. Journal, (Issue : 2) (Volume : 29)
Intrusion Detection and Attack Classifier Based on Three Techniques: A Comparative Study

Different soft-computing based methods have been proposed in recent years for the development of intrusion... See more

Different soft-computing based methods have been proposed in recent years for the development of intrusion detection systems. The purpose of this work is to develop, implement and evaluate an anomaly off-line based intrusion detection system using three techniques; data mining association rules, decision trees, and artificial neural network, then comparing among them to decide which technique is better in its performance for the intrusion detection system. Several methods have been proposed to modify these techniques to improve the classification process. For association rules, the majority vote classifier was modified to build a new classifier that can recognize anomalies. With decision trees, ID3 algorithm was modified to deal not only with discrete values but also to deal with numerical values. For neural networks, a back-propagation algorithm has been used as the learning algorithm with a different number of input patterns (118, 51, and 41) to introduce the important knowledge about the intruder to the neural networks. Different types of normalization methods were applied to the input patterns to speed up the learning process. The full 10% KDD Cup 99 train dataset and the full correct test dataset are used in this work. The results of the proposed techniques show that there is an improvement in the performance compared to the standard techniques, furthermore, the Percentage of Successful Prediction (PSP) and Cost Per Test (CPT) of neural networks and decision trees are better than association rules. On the other hand, the training time for the neural network takes longer time than the decision trees.

 2011-01
International Journal of Scientific & Engineering Research (Issue : 9) (Volume : 4)
Cuttlefish Algorithm – A Novel Bio-InspiredOptimization Algorithm

In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented.... See more

In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented. The algorithm mimics the mechanism of color changing behavior used by the cuttlefish to solve numerical global optimization problems. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores, and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. The proposed algorithm considers two main processes: reflection and visibility. Reflection process is proposed to simulate the light reflection mechanism used by these three layers, while the visibility is proposed to simulate the visibility of matching pattern used by the cuttlefish. These two processes are used as a search strategy to find the global optimal solution. The efficiency of this algorithm is also tested with some other popular biology inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior to other algorithms.

 2009-09
Raf. J. of Comp. & Math’s (Issue : 9) (Volume : 9)
Reduction of Associated noise of hiding in sound file

Nois

 2008-01

Thesis

2015
A Novel Cuttlefish Optimization Algorithm for Distributed Intrusion Detection System

In this thesis, a novel Cuttlefish optimization Algorithm (CFA) is proposed for IDS. First: The... See more

In this thesis, a novel Cuttlefish optimization Algorithm (CFA) is proposed for IDS. First: The proposed algorithm is a new Bio-Inspired optimization algorithm that can mimic the color changing behavior of cuttlefish to find the optimal solution. The second proposal is to modify this optimization algorithm to be used as a feature selection model for IDS. Finally, a new Distributed Intrusion Detection System (DIDS) was designed based on the combination of CFA and Decision Tree (DT).

 2025
2009
A Comparative Study among Several Modified Intrusion Detection System Techniques

The purpose of this thesis is to design, implement and evaluate an anomaly off-line based... See more

The purpose of this thesis is to design, implement and evaluate an anomaly off-line based intrusion detection system using three techniques data mining association rules, decision trees, and artificial neural network, then comparing among them to decide which technique is better in its performance for the intrusion detection system. While most of the previous studies have focused on the classification of records in one of two general classes - normal and attacks, this thesis also aims to solve a multiclass

 2025

Conference

2018 International Conference on Advanced Science and Engineering (ICOASE)
 2019-10
A New Dimensional Reduction Based on Cuttlefish Algorithm for Human Cancer Gene Expression

Currently, the main problem in DNA Microarray is classification due to the thousands of numbers of genes together, and this huge number of genes can make the classification task very difficult. Therefore, feature selection is... See more

Currently, the main problem in DNA Microarray is classification due to the thousands of numbers of genes together, and this huge number of genes can make the classification task very difficult. Therefore, feature selection is a very important task for gene classification. This paper presents a new model which uses a Cuttlefish Algorithm (CFA) to select the most informative features, while K-Nearest Neighbor (KNN) is used to measure the quality of the selected features that are produced by the CFA. Eight datasets are used to evaluate the performance of the proposed model and compared with the performance of four well-known existing classification techniques such as KNN, DT, Hidden Markov models (HMM), and SVM. The obtained results show that the proposed technique outperforms these existing techniques in five datasets among eight datasets.

ICAINN 2015 : 17th International Conference on Artificial Intelligence and Neural Networks
 2015-07
A New DIDS Design Based on a Combination Feature Selection Approach

Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a... See more

Feature selection has been used in many fields such as classification, data mining and object recognition and proven to be effective for removing irrelevant and redundant features from the original dataset. In this paper, a new design of a distributed intrusion detection system using a combination feature selection model based on bees and decision tree. Bees algorithm is used as the search strategy to find the optimal subset of features, whereas the decision tree is used as a judgment for the selected features. Both the produced features and the generated rules are used by Decision Making Mobile Agent to decide whether there is an attack or not in the networks. Decision Making Mobile Agent will migrate through the networks, moving from node to another, if it found that there is an attack on one of the nodes, it then alerts the user through User Interface Agent or takes some action through Action Mobile Agent. The KDD Cup 99 dataset is used to test the effectiveness of the proposed system. The results show that even if only four features are used, the proposed system gives a better performance when it is compared with the obtained results using all 41 features.

International Conference on Computer Science (ICCS 2014)
 2014-09
A New Tool for Global Optimization Problems- Cuttlefish Algorithm

This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors... See more

This paper presents a new meta-heuristic bio-inspired optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. The proposed algorithm considers mainly two processes: reflection and visibility. Reflection process simulates light reflection mechanism used by these layers, while visibility process simulates the visibility of matching patterns of the cuttlefish. To show the effectiveness of the algorithm, it is tested with some other popular bio-inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior when compared with these algorithms.

3rd International Conference on System Engineering and Modeling (ICSEM)
 2014-05
A Combinational Approach To Intrusion Detection Data For Feature Selection

This paper presents a new combinational approach to select an optimal subset of features of Intrusion Detection Data. The main idea is to take advantage of ID3 Algorithm and Bees Algorithm (BA). In the proposed... See more

This paper presents a new combinational approach to select an optimal subset of features of Intrusion Detection Data. The main idea is to take advantage of ID3 Algorithm and Bees Algorithm (BA). In the proposed feature selection model, BA is used as a search strategy for a subset generation and ID3 is used as the classifier. The effectiveness of the model is tested on the random subsets collected from KDD-cup 99 data set. Standard Intrusion Detection System (IDS) measurements such as detection rate, false alarm rate, and classification accuracy are used to evaluate the performance of the new model. The results obtained show that the feature subset generated by the proposed ID3-BA can achieve classification accuracy and detection rate superior to the results obtained by using all features.