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The publishing of the present issue (Volumen 13, No 4, 2013) of the journal “Cybernetics and Information Technologies” is financially supported by FP7 project “Advanced Computing for Innovation” (ACOMIN), grant agreement 316087 of Call FP7 REGPOT-2012-2013-1.

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Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

Volumen 22 (2022): Edición 2 (June 2022)

Detalles de la revista
Formato
Revista
eISSN
1314-4081
Publicado por primera vez
13 Mar 2012
Periodo de publicación
4 veces al año
Idiomas
Inglés

Buscar

11 Artículos
Acceso abierto

A Scrutiny of Honeyword Generation Methods: Remarks on Strengths and Weaknesses Points

Publicado en línea: 23 Jun 2022
Páginas: 3 - 25

Resumen

Abstract

Honeyword system is a successful password cracking detection system. Simply the honeywords are (False passwords) that are accompanied to the sugarword (Real password). Honeyword system aims to improve the security of hashed passwords by facilitating the detection of password cracking. The password database will have many honeywords for every user in the system. If the adversary uses a honeyword for login, a silent alert will indicate that the password database might be compromised. All previous studies present a few remarks on honeyword generation methods for max two preceding methods only. So, the need for one that lists all preceding researches with their weaknesses is shown. This work presents all generation methods then lists the strengths and weaknesses of 26 ones. In addition, it puts 32 remarks that highlight their strengths and weaknesses points. This research has proved that every honeyword generation method has many weaknesses points.

Palabras clave

  • Flatness
  • honeychecker
  • honeywords
  • password
  • sweetwords
Acceso abierto

Development of a Scheme for Correcting Arbitrary Errors and Averaging Noise in Quantum Computing

Publicado en línea: 23 Jun 2022
Páginas: 26 - 35

Resumen

Abstract

Intensive research is currently being carried out to develop and create quantum computers and their software. This work is devoted to study of the influence of the environment on the quantum system of qubits. Quantum error correction is a set of methods for protecting quantum information and quantum state from unwanted interactions of the environment (decoherence) and other forms and types of noise. The article discusses the solution to the problem of research and development of corrective codes for rectifying several types of quantum errors that occur during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types of quantum errors and to create a corrective code for quantum error rectification. The scientific novelty is expressed in the exclusion of one of the shortcomings of the quantum computing process.

Palabras clave

  • Quantum register
  • quantum computer simulator
  • complex plane
  • qubit
  • quantum error
  • phase amplitude
Acceso abierto

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Publicado en línea: 23 Jun 2022
Páginas: 36 - 49

Resumen

Abstract

Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.

Palabras clave

  • Classification
  • LVQ
  • partial distance computation
  • PDLVQ
  • SOM
Acceso abierto

Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Publicado en línea: 23 Jun 2022
Páginas: 50 - 65

Resumen

Abstract

Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.

Palabras clave

  • Decision tree
  • virtual merging node
  • weak nodes
  • nodes similarity
  • data augmentation
Acceso abierto

An Insight on Clustering Protocols in Wireless Sensor Networks

Publicado en línea: 23 Jun 2022
Páginas: 66 - 85

Resumen

Abstract

Wireless Sensor Networks (WSN) have drawn the attention of many researchers as well as general users in recent years. Since WSN has a wide range of applications, including environmental monitoring, medical applications, and surveillance, their usage is not limited. As energy is a major constraint in WSN, it is necessary to employ techniques that reduce energy consumption in order to extend the network’s lifetime. Clustering, data aggregation, duty cycling, load balancing, and efficient routing are some of the techniques used to reduce energy consumption. In this paper, we discuss in details about clustering, its properties, the existing clustering protocols. The clustering protocols that support data aggregation will also be discussed. The paper concludes with considering the impact of clustering and data aggregation in WSN.

Palabras clave

  • WSN
  • clustering
  • data aggregation
Acceso abierto

Modelling Activity of a Malicious User in Computer Networks

Publicado en línea: 23 Jun 2022
Páginas: 86 - 95

Resumen

Abstract

In the present study, an extended classification of Internet users penetrating in computer networks and a definition of the motivation as a psychological and emotional state and main prerequisites for modelling of network intruder’s activity are suggested. A mathematical model as a quadratic function of malicious individual’s behavior and impact on the computer network based on three quantified factors, motivation, satisfaction and system protection is developed. Numerical simulation experiments of the unauthorized access and its effect onto the computer network are carried out. The obtained results are graphically illustrated and discussed.

Palabras clave

  • Cybersecurity
  • Cyberattack
  • Hacker Psychology
  • Hacker Behavior Modelling
Acceso abierto

Visualizing Interesting Patterns in Cyber Threat Intelligence Using Machine Learning Techniques

Publicado en línea: 23 Jun 2022
Páginas: 96 - 113

Resumen

Abstract

In an advanced and dynamic cyber threat environment, organizations need to yield more proactive methods to handle their cyber defenses. Cyber threat data known as Cyber Threat Intelligence (CTI) of previous incidents plays an important role by helping security analysts understand recent cyber threats and their mitigations. The mass of CTI is exponentially increasing, most of the content is textual which makes it difficult to analyze. The current CTI visualization tools do not provide effective visualizations. To address this issue, an exploratory data analysis of CTI reports is performed to dig-out and visualize interesting patterns of cyber threats which help security analysts to proactively mitigate vulnerabilities and timely predict cyber threats in their networks.

Palabras clave

  • Cyber threat intelligence
  • machine learning
  • visual analytics
  • tactics techniques and procedures
  • cyber threat actor
  • malware
Acceso abierto

An Augmented UCAL Model for Predicting Trajectory and Location

Publicado en línea: 23 Jun 2022
Páginas: 114 - 124

Resumen

Abstract

Predicting human mobility between locations plays an important role in a wide range of applications and services such as transportation, economics, sociology and other fields. Mobility prediction can be implemented through various machine learning algorithms that can predict the future trajectory of a user relying on the current trajectory and time, learning from historical sequences of locations previously visited by the user. But, it is not easy to capture complex patterns from the long historical sequences of locations. Inspired by the methods of the Convolutional Neural Network (CNN), we propose an augmented Union ConvAttention-LSTM (UCAL) model. The UCAL consists of the 1D CNN that allows capturing locations from historical trajectories and the augmented proposed model that contains an Attention technique with a Long Short-Term Memory (LSTM) in order to capture patterns from current trajectories. The experimental results prove the effectiveness of our proposed methodology that outperforms the existing models.

Palabras clave

  • Deep learning
  • LSTM
  • attention mechanism
  • human mobility prediction location
  • trajectory
Acceso abierto

Tunnel Parsing with the Token’s Lexeme

Publicado en línea: 23 Jun 2022
Páginas: 125 - 144

Resumen

Abstract

The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.

Palabras clave

  • Parsing algorithm
  • tunnel parsing
  • lexeme matching
  • advanced grammar
  • phrase state machine
Acceso abierto

Optimization of Cross Diagonal Pixel Value Differencing and Modulus Function Steganography Using Edge Area Block Patterns

Publicado en línea: 23 Jun 2022
Páginas: 145 - 159

Resumen

Abstract

The existence of a trade-off between embedding capacity and imperceptibility is a challenge to improve the quality of steganographic images. This research proposes to cross diagonal embedding Pixel Value Differencing (PVD) and Modulus Function (MF) techniques using edge area patterns to improve embedding capacity and imperceptibility simultaneously. At the same time still, maintain a good quality of security. By implementing them into 14 public datasets, the proposed techniques are proven to increase both capacity and imperceptibility. The cross diagonal embedding PVD is responsible for increasing the embedding capacity reaching an average value of 3.18 bits per pixel (bpp), and at the same time, the implementation of edge area block patterns-based embedding is a solution of improving imperceptibility toward an average value of PSNR above 40 dB and that of SSIM above 0.98. Aside from its success in increasing the embedding capacity and the imperceptibility, the proposed techniques remain resistant to RS attacks.

Palabras clave

  • Pixel value differencing
  • modulus function
  • image steganography
  • edge detection
  • enhanced payload capacity
Acceso abierto

A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Publicado en línea: 23 Jun 2022
Páginas: 160 - 178

Resumen

Abstract

The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.

Palabras clave

  • Rule generating model
  • student entrepreneurial potential detection
  • imbalanced data
  • theory of planned behavior
11 Artículos
Acceso abierto

A Scrutiny of Honeyword Generation Methods: Remarks on Strengths and Weaknesses Points

Publicado en línea: 23 Jun 2022
Páginas: 3 - 25

Resumen

Abstract

Honeyword system is a successful password cracking detection system. Simply the honeywords are (False passwords) that are accompanied to the sugarword (Real password). Honeyword system aims to improve the security of hashed passwords by facilitating the detection of password cracking. The password database will have many honeywords for every user in the system. If the adversary uses a honeyword for login, a silent alert will indicate that the password database might be compromised. All previous studies present a few remarks on honeyword generation methods for max two preceding methods only. So, the need for one that lists all preceding researches with their weaknesses is shown. This work presents all generation methods then lists the strengths and weaknesses of 26 ones. In addition, it puts 32 remarks that highlight their strengths and weaknesses points. This research has proved that every honeyword generation method has many weaknesses points.

Palabras clave

  • Flatness
  • honeychecker
  • honeywords
  • password
  • sweetwords
Acceso abierto

Development of a Scheme for Correcting Arbitrary Errors and Averaging Noise in Quantum Computing

Publicado en línea: 23 Jun 2022
Páginas: 26 - 35

Resumen

Abstract

Intensive research is currently being carried out to develop and create quantum computers and their software. This work is devoted to study of the influence of the environment on the quantum system of qubits. Quantum error correction is a set of methods for protecting quantum information and quantum state from unwanted interactions of the environment (decoherence) and other forms and types of noise. The article discusses the solution to the problem of research and development of corrective codes for rectifying several types of quantum errors that occur during computational processes in quantum algorithms and models of quantum computing devices. The aim of the work is to study existing methods for correcting various types of quantum errors and to create a corrective code for quantum error rectification. The scientific novelty is expressed in the exclusion of one of the shortcomings of the quantum computing process.

Palabras clave

  • Quantum register
  • quantum computer simulator
  • complex plane
  • qubit
  • quantum error
  • phase amplitude
Acceso abierto

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Publicado en línea: 23 Jun 2022
Páginas: 36 - 49

Resumen

Abstract

Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.

Palabras clave

  • Classification
  • LVQ
  • partial distance computation
  • PDLVQ
  • SOM
Acceso abierto

Enhancing Weak Nodes in Decision Tree Algorithm Using Data Augmentation

Publicado en línea: 23 Jun 2022
Páginas: 50 - 65

Resumen

Abstract

Decision trees are among the most popular classifiers in machine learning, artificial intelligence, and pattern recognition because they are accurate and easy to interpret. During the tree construction, a node containing too few observations (weak node) could still get split, and then the resulted split is unreliable and statistically has no value. Many existing machine-learning methods can resolve this issue, such as pruning, which removes the tree’s non-meaningful parts. This paper deals with the weak nodes differently; we introduce a new algorithm Enhancing Weak Nodes in Decision Tree (EWNDT), which reinforces them by increasing their data from other similar tree nodes. We called the data augmentation a virtual merging because we temporarily recalculate the best splitting attribute and the best threshold in the weak node. We have used two approaches to defining the similarity between two nodes. The experimental results are verified using benchmark datasets from the UCI machine-learning repository. The results indicate that the EWNDT algorithm gives a good performance.

Palabras clave

  • Decision tree
  • virtual merging node
  • weak nodes
  • nodes similarity
  • data augmentation
Acceso abierto

An Insight on Clustering Protocols in Wireless Sensor Networks

Publicado en línea: 23 Jun 2022
Páginas: 66 - 85

Resumen

Abstract

Wireless Sensor Networks (WSN) have drawn the attention of many researchers as well as general users in recent years. Since WSN has a wide range of applications, including environmental monitoring, medical applications, and surveillance, their usage is not limited. As energy is a major constraint in WSN, it is necessary to employ techniques that reduce energy consumption in order to extend the network’s lifetime. Clustering, data aggregation, duty cycling, load balancing, and efficient routing are some of the techniques used to reduce energy consumption. In this paper, we discuss in details about clustering, its properties, the existing clustering protocols. The clustering protocols that support data aggregation will also be discussed. The paper concludes with considering the impact of clustering and data aggregation in WSN.

Palabras clave

  • WSN
  • clustering
  • data aggregation
Acceso abierto

Modelling Activity of a Malicious User in Computer Networks

Publicado en línea: 23 Jun 2022
Páginas: 86 - 95

Resumen

Abstract

In the present study, an extended classification of Internet users penetrating in computer networks and a definition of the motivation as a psychological and emotional state and main prerequisites for modelling of network intruder’s activity are suggested. A mathematical model as a quadratic function of malicious individual’s behavior and impact on the computer network based on three quantified factors, motivation, satisfaction and system protection is developed. Numerical simulation experiments of the unauthorized access and its effect onto the computer network are carried out. The obtained results are graphically illustrated and discussed.

Palabras clave

  • Cybersecurity
  • Cyberattack
  • Hacker Psychology
  • Hacker Behavior Modelling
Acceso abierto

Visualizing Interesting Patterns in Cyber Threat Intelligence Using Machine Learning Techniques

Publicado en línea: 23 Jun 2022
Páginas: 96 - 113

Resumen

Abstract

In an advanced and dynamic cyber threat environment, organizations need to yield more proactive methods to handle their cyber defenses. Cyber threat data known as Cyber Threat Intelligence (CTI) of previous incidents plays an important role by helping security analysts understand recent cyber threats and their mitigations. The mass of CTI is exponentially increasing, most of the content is textual which makes it difficult to analyze. The current CTI visualization tools do not provide effective visualizations. To address this issue, an exploratory data analysis of CTI reports is performed to dig-out and visualize interesting patterns of cyber threats which help security analysts to proactively mitigate vulnerabilities and timely predict cyber threats in their networks.

Palabras clave

  • Cyber threat intelligence
  • machine learning
  • visual analytics
  • tactics techniques and procedures
  • cyber threat actor
  • malware
Acceso abierto

An Augmented UCAL Model for Predicting Trajectory and Location

Publicado en línea: 23 Jun 2022
Páginas: 114 - 124

Resumen

Abstract

Predicting human mobility between locations plays an important role in a wide range of applications and services such as transportation, economics, sociology and other fields. Mobility prediction can be implemented through various machine learning algorithms that can predict the future trajectory of a user relying on the current trajectory and time, learning from historical sequences of locations previously visited by the user. But, it is not easy to capture complex patterns from the long historical sequences of locations. Inspired by the methods of the Convolutional Neural Network (CNN), we propose an augmented Union ConvAttention-LSTM (UCAL) model. The UCAL consists of the 1D CNN that allows capturing locations from historical trajectories and the augmented proposed model that contains an Attention technique with a Long Short-Term Memory (LSTM) in order to capture patterns from current trajectories. The experimental results prove the effectiveness of our proposed methodology that outperforms the existing models.

Palabras clave

  • Deep learning
  • LSTM
  • attention mechanism
  • human mobility prediction location
  • trajectory
Acceso abierto

Tunnel Parsing with the Token’s Lexeme

Publicado en línea: 23 Jun 2022
Páginas: 125 - 144

Resumen

Abstract

The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.

Palabras clave

  • Parsing algorithm
  • tunnel parsing
  • lexeme matching
  • advanced grammar
  • phrase state machine
Acceso abierto

Optimization of Cross Diagonal Pixel Value Differencing and Modulus Function Steganography Using Edge Area Block Patterns

Publicado en línea: 23 Jun 2022
Páginas: 145 - 159

Resumen

Abstract

The existence of a trade-off between embedding capacity and imperceptibility is a challenge to improve the quality of steganographic images. This research proposes to cross diagonal embedding Pixel Value Differencing (PVD) and Modulus Function (MF) techniques using edge area patterns to improve embedding capacity and imperceptibility simultaneously. At the same time still, maintain a good quality of security. By implementing them into 14 public datasets, the proposed techniques are proven to increase both capacity and imperceptibility. The cross diagonal embedding PVD is responsible for increasing the embedding capacity reaching an average value of 3.18 bits per pixel (bpp), and at the same time, the implementation of edge area block patterns-based embedding is a solution of improving imperceptibility toward an average value of PSNR above 40 dB and that of SSIM above 0.98. Aside from its success in increasing the embedding capacity and the imperceptibility, the proposed techniques remain resistant to RS attacks.

Palabras clave

  • Pixel value differencing
  • modulus function
  • image steganography
  • edge detection
  • enhanced payload capacity
Acceso abierto

A Rule-Generation Model for Class Imbalances to Detect Student Entrepreneurship Based on the Theory of Planned Behavior

Publicado en línea: 23 Jun 2022
Páginas: 160 - 178

Resumen

Abstract

The ability to identify the entrepreneurial potential of students enables higher education institutions to contribute to the economic and social development of a country. Current research trends regarding the detection of student entrepreneurial potential have the greatest challenge in the unequal ratio of datasets. This study proposes a rule-generation model in an imbalanced situation to classify student entrepreneurship based on the Theory of Planned Behavior (TPB). The result is a ruleset that is used for the early detection of student entrepreneurial potential. The proposed method consists of three main stages, namely preprocessing data to classify data based on TPB variables, generating a dataset by clustering and selecting attributes by sampling to balance the data, and finally generating a ruleset. Furthermore, the results of the detecting ruleset have been evaluated with actual data from the student tracer study as ground truth. The evaluation results show high accuracy so that the ruleset can be applied to the higher education environment in the future.

Palabras clave

  • Rule generating model
  • student entrepreneurial potential detection
  • imbalanced data
  • theory of planned behavior

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