The Biological Immune System is a remarkable information processing and self-learning system that offers
stimulation to build Artificial Immune System (AIS).During the last two decades, the field of AIS is
progressing slowly and steadily as a branch of Computational Intelligence (CI). At present the AIS
algorithms such as Negative Selection Theory, Clonal Selection Theory, Immune Networks Theory, Danger
theory and Dendritic Cell Algorithm are widely used to solve many real world problems in a vast range of
domain areas such as Network Intrusion Detection (NID), Anomaly Detection, Clustering and
classification and Pattern recognition. This review paper critically discusses the theoretical foundation,
research methodologies and applications of the AIS.
Inspiration to Application: A Tutorial on Artificial Immune SystemsJulie Greensmith
Ā
A tutorial of the history and application of artificial immune systems, given as a research tutorial for the Intelligent Modelling and Analysis Research Group, School of Computer Science, University of Nottingham UK.
Design and Implementation of Artificial Immune System for Detecting Flooding ...Kent State University
Ā
Academic Paper: N. B. I. Al-Dabagh and I. A. Ali, "Design and implementation of artificial immune system for detecting flooding attacks," in High Performance Computing and Simulation (HPCS), 2011 International Conference on, 2011, pp. 381-390.
Inspiration to Application: A Tutorial on Artificial Immune SystemsJulie Greensmith
Ā
A tutorial of the history and application of artificial immune systems, given as a research tutorial for the Intelligent Modelling and Analysis Research Group, School of Computer Science, University of Nottingham UK.
Design and Implementation of Artificial Immune System for Detecting Flooding ...Kent State University
Ā
Academic Paper: N. B. I. Al-Dabagh and I. A. Ali, "Design and implementation of artificial immune system for detecting flooding attacks," in High Performance Computing and Simulation (HPCS), 2011 International Conference on, 2011, pp. 381-390.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
Ā
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and NaĆÆve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Developing an Artificial Immune Model for Cash Fraud Detection khawla Osama
Ā
Document from thesis done by Bsc students as graduation research , to develop a model that detect a cash card fraud base on the cash card holder pattern ,the technique used to detect fraud inspired from immune system
A Survey of Various Intrusion Detection Systemsijsrd.com
Ā
In this paper, we present an overview of existing intrusion detection techniques. All these algorithms are described more or less on their own. Intrusion detection system is a very popular and computationally expensive task. We also explain the fundamentals of intrusion detection system. We describe today's approaches for intrusion detection system. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
Self protection mechanism for wireless sensor networksIJNSA Journal
Ā
Because of the widespread use of wireless sensor ne
tworks in many applications, and due to the nature
of
the specifications of these networks (WSN) in terms
of wireless communication, the network contract
specifications, and published it in difficult envir
onments. All this leads to the network exposure to
many
types of external attacks. Therefore, the protectio
n of these networks from external attacks is consid
ered the
one of the most important researches at this time.
In this paper we investigated the security in wirel
ess
sensor networks, Limitations of WSN, Characteristic
Values for some types of attacks, and have been
providing protection mechanism capable of detecting
and protecting wireless sensor networks from a wid
e
range of attacks.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Ā
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Weapon detection using artificial intelligence and deep learning for security...Venkat Projects
Ā
Weapon detection using artificial intelligence and deep learning for security applications
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SS D and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Ā
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
APPLICATION OF SELF-ORGANIZING FEATURE MAPS AND MARKOV CHAINS TO RECOGNITION ...IAEME Publication
Ā
The number of studies in the field of behavior analysis is currently experiencing a
significant upsurge. This study presents two cancarantly approaches to identifying
anomalous activity within usersā behavior in cloud infrastructures, each of which
allows researchers to learn from the empirical data collected. The main purpose of
these approaches is the ongoing scoring of usersā actions in cloud infrastructures to
reveal anomalies in their activity. The first approach is based on the technique of
statistical hypothesis testing and uses Kohonen self-organizing maps to generate the
target statistics. The second approach is based on revealing strange activity in the
dynamics of userās behavior and uses Markov chains to describe their typical actions
Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of N...IJMER
Ā
Enormous studies on intrusion detection have widely applied data mining techniques to
finding out the useful knowledge automatically from large amount of databases, while few studies have
proposed classification data mining approaches. In an actual risk assessment process, the discovery of
intrusion detection prediction knowledge from experts is still regarded as an important task because
expertsā predictions depend on their subjectivity. Traditional statistical techniques and artificial
intelligence techniques are commonly used to solve this classification decision making. This paper
proposes an ant-miner based data mining method for discovering network intrusion detection rules from
large dataset. The obtained result of this experiment shows that clearly the ant-miner is superior than
ID3, J48, ADtree, BFtree, Simple cart. Although different classification models have been developed for
network intrusion detection, each of them has its strength and weakness, including the most commonly
applied Support Vector Machine(SVM)method and the clustering based on Self Organized Ant Colony
Network (CSOACN).Our algorithm is implemented and evaluated using a standard bench mark KDD99
dataset. Experiments show that ant-miner algorithm out performs than other methods in terms of both
classification rate and accuracy
NOVEL HYBRID INTRUSION DETECTION SYSTEM FOR CLUSTERED WIRELESS SENSOR NETWORKIJNSA Journal
Ā
Wireless sensor network (WSN) is regularly deployed in unattended and hostile environments. The WSN is vulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the most efficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM) and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low false alarm.
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Navigation Control and Path Mapping of a Mobile Robot using Artificial Immune...Waqas Tariq
Ā
This study aims to apply Artificial Immune Systems (AIS) to a mobile robot making it capable of traversing an unknown environment and mapping it while looking for the target. We have implemented a mixture of Antibody-Antibody (Ab-Ab) interaction algorithm coupled with negative selection algorithms to develop the proposed AIS controller. We have also developed a method for random generation of antibodies to make the system more similar to the actual biological process. Finally, a generalized architecture for representation of antibodies and antigens in a standard mobile robot using proximity sensors for interaction with the environment has been introduced. The results show that the proposed algorithm was able to explore the unknown environments while learning from past behavior and look for the target. It was also able to successfully map the traversed path and plot the obstacles based on their type.
Artificial immune systems can be defined as abstract or metaphorical computational systems
developed using ideas, theories, and components, extracted from the immune system. Most AIS aim
at solving complex computational or engineering problems, such as pattern recognition, elimination,
and optimisation. This is a crucial distinction between AIS and theoretical immune system models.
While the former is devoted primarily to computing, the latter is focused on the modelling of the IS
in order to understand its behaviour, so that contributions can be made to the biological sciences. It is
not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the
IS have contributed to the development of AIS. This paper discusses the concept of artificial immune
system. AIS has various algorithms such as: Immune Theory, Clonal Selection, negative selection.
All these are explained in this paper.
In this Paper, surveys, application, and comparison of three types of artificial
intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and
Artificial Immune Recognition System have been introduced.
Selecting the correct features is the most important thing in training and diagnosis
field and it is the core issue of this field, in this thesis, a trial is made to improve the
accuracies of the three proposed methods by trying to select the proper features from time
domain. The training is done by using the data collected from two-channel, horizontal
and vertical in three cases first, both time and frequency domains are used as features
input to the three proposed methods, secondly, using frequency domain only or thirdly,
using part of the time domain features with frequency domain features; for two speed. All
the three methods show excellent accuracy when training and diagnosis at same specific
speed especially SVM, while the accuracy is low when diagnosis at a speed that differs
from training speed. Also all the three methods give excellent diagnosis results when the
applied load at the same speed of training speed.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
Ā
In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), and NaĆÆve Bayes (NB) classifiers are used for training from the taxonomy of classifiers based on lazy and eager learners. In this paper, Chi-Square, a filter-based feature selection technique, is applied to the UNSW-NB15 dataset to reduce the irrelevant and redundant features. The performance of classifiers is measured in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, F1-Score, True Positive Rate (TPR) and False Positive Rate (FPR) with or without feature selection technique and comparative analysis of these machine learning classifiers is carried out.
Developing an Artificial Immune Model for Cash Fraud Detection khawla Osama
Ā
Document from thesis done by Bsc students as graduation research , to develop a model that detect a cash card fraud base on the cash card holder pattern ,the technique used to detect fraud inspired from immune system
A Survey of Various Intrusion Detection Systemsijsrd.com
Ā
In this paper, we present an overview of existing intrusion detection techniques. All these algorithms are described more or less on their own. Intrusion detection system is a very popular and computationally expensive task. We also explain the fundamentals of intrusion detection system. We describe today's approaches for intrusion detection system. From the broad variety of efficient techniques that have been developed we will compare the most important ones. We will systematize the techniques and analyze their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behavior of the algorithms is much more similar as to be expected.
Self protection mechanism for wireless sensor networksIJNSA Journal
Ā
Because of the widespread use of wireless sensor ne
tworks in many applications, and due to the nature
of
the specifications of these networks (WSN) in terms
of wireless communication, the network contract
specifications, and published it in difficult envir
onments. All this leads to the network exposure to
many
types of external attacks. Therefore, the protectio
n of these networks from external attacks is consid
ered the
one of the most important researches at this time.
In this paper we investigated the security in wirel
ess
sensor networks, Limitations of WSN, Characteristic
Values for some types of attacks, and have been
providing protection mechanism capable of detecting
and protecting wireless sensor networks from a wid
e
range of attacks.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Ā
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Weapon detection using artificial intelligence and deep learning for security...Venkat Projects
Ā
Weapon detection using artificial intelligence and deep learning for security applications
Security is always a main concern in every domain, due to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Due to growing demand in the protection of safety, security and personal properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. This paper implements automatic gun (or) weapon detection using a convolution neural network (CNN) based SS D and Faster RCNN algorithms. Proposed implementation uses two types of datasets. One dataset, which had pre-labelled images and the other one is a set of images, which were labelled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Ā
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
APPLICATION OF SELF-ORGANIZING FEATURE MAPS AND MARKOV CHAINS TO RECOGNITION ...IAEME Publication
Ā
The number of studies in the field of behavior analysis is currently experiencing a
significant upsurge. This study presents two cancarantly approaches to identifying
anomalous activity within usersā behavior in cloud infrastructures, each of which
allows researchers to learn from the empirical data collected. The main purpose of
these approaches is the ongoing scoring of usersā actions in cloud infrastructures to
reveal anomalies in their activity. The first approach is based on the technique of
statistical hypothesis testing and uses Kohonen self-organizing maps to generate the
target statistics. The second approach is based on revealing strange activity in the
dynamics of userās behavior and uses Markov chains to describe their typical actions
Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of N...IJMER
Ā
Enormous studies on intrusion detection have widely applied data mining techniques to
finding out the useful knowledge automatically from large amount of databases, while few studies have
proposed classification data mining approaches. In an actual risk assessment process, the discovery of
intrusion detection prediction knowledge from experts is still regarded as an important task because
expertsā predictions depend on their subjectivity. Traditional statistical techniques and artificial
intelligence techniques are commonly used to solve this classification decision making. This paper
proposes an ant-miner based data mining method for discovering network intrusion detection rules from
large dataset. The obtained result of this experiment shows that clearly the ant-miner is superior than
ID3, J48, ADtree, BFtree, Simple cart. Although different classification models have been developed for
network intrusion detection, each of them has its strength and weakness, including the most commonly
applied Support Vector Machine(SVM)method and the clustering based on Self Organized Ant Colony
Network (CSOACN).Our algorithm is implemented and evaluated using a standard bench mark KDD99
dataset. Experiments show that ant-miner algorithm out performs than other methods in terms of both
classification rate and accuracy
NOVEL HYBRID INTRUSION DETECTION SYSTEM FOR CLUSTERED WIRELESS SENSOR NETWORKIJNSA Journal
Ā
Wireless sensor network (WSN) is regularly deployed in unattended and hostile environments. The WSN is vulnerable to security threats and susceptible to physical capture. Thus, it is necessary to use effective mechanisms to protect the network. It is widely known, that the intrusion detection is one of the most efficient security mechanisms to protect the network against malicious attacks or unauthorized access. In this paper, we propose a hybrid intrusion detection system for clustered WSN. Our intrusion framework uses a combination between the Anomaly Detection based on support vector machine (SVM) and the Misuse Detection. Experiments results show that most of routing attacks can be detected with low false alarm.
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Navigation Control and Path Mapping of a Mobile Robot using Artificial Immune...Waqas Tariq
Ā
This study aims to apply Artificial Immune Systems (AIS) to a mobile robot making it capable of traversing an unknown environment and mapping it while looking for the target. We have implemented a mixture of Antibody-Antibody (Ab-Ab) interaction algorithm coupled with negative selection algorithms to develop the proposed AIS controller. We have also developed a method for random generation of antibodies to make the system more similar to the actual biological process. Finally, a generalized architecture for representation of antibodies and antigens in a standard mobile robot using proximity sensors for interaction with the environment has been introduced. The results show that the proposed algorithm was able to explore the unknown environments while learning from past behavior and look for the target. It was also able to successfully map the traversed path and plot the obstacles based on their type.
Artificial immune systems can be defined as abstract or metaphorical computational systems
developed using ideas, theories, and components, extracted from the immune system. Most AIS aim
at solving complex computational or engineering problems, such as pattern recognition, elimination,
and optimisation. This is a crucial distinction between AIS and theoretical immune system models.
While the former is devoted primarily to computing, the latter is focused on the modelling of the IS
in order to understand its behaviour, so that contributions can be made to the biological sciences. It is
not exclusive, however, the use of one approach into the other and, indeed, theoretical models of the
IS have contributed to the development of AIS. This paper discusses the concept of artificial immune
system. AIS has various algorithms such as: Immune Theory, Clonal Selection, negative selection.
All these are explained in this paper.
In this Paper, surveys, application, and comparison of three types of artificial
intelligence in machinery fault diagnosis: Neural Network, Support Vector Machines, and
Artificial Immune Recognition System have been introduced.
Selecting the correct features is the most important thing in training and diagnosis
field and it is the core issue of this field, in this thesis, a trial is made to improve the
accuracies of the three proposed methods by trying to select the proper features from time
domain. The training is done by using the data collected from two-channel, horizontal
and vertical in three cases first, both time and frequency domains are used as features
input to the three proposed methods, secondly, using frequency domain only or thirdly,
using part of the time domain features with frequency domain features; for two speed. All
the three methods show excellent accuracy when training and diagnosis at same specific
speed especially SVM, while the accuracy is low when diagnosis at a speed that differs
from training speed. Also all the three methods give excellent diagnosis results when the
applied load at the same speed of training speed.
With the development growing of network technology, computer networks became increasingly
wide and opened. This evolution gave birth to new techniques allowing accessibility of networks
and information systems with an aim of facilitating the transactions. Consequently, these
techniques gave also birth to new forms of threats. In this article, we present the utility to use a
system of intrusion detection through a presentation of these characteristics. Using as
inspiration the immune biological system, we propose a model of artificial immune system
which is integrated in the behavior of distributed agents on the network in order to ensure a
good detection of intrusions. We also present the internal structure of the immune agents and
their capacity to distinguish between self and not self. The agents are able to achieve
simultaneous treatments, are able to auto-adaptable to environment evolution and have also the
property of distributed coordination.
ENHANCING ENGLISH WRITING SKILLS THROUGH INTERNET-PLUS TOOLS IN THE PERSPECTI...ijfcstjournal
Ā
This investigation delves into incorporating a hybridized memetic strategy within the framework of English
composition pedagogy, leveraging Internet Plus resources. The study aims to provide an in-depth analysis
of how this method influences studentsā writing competence, their perceptions of writing, and their
enthusiasm for English acquisition. Employing an explanatory research design that combines qualitative
and quantitative methods, the study collects data through surveys, interviews, and observations of studentsā
writing performance before and after the intervention. Findings demonstrate a beneficial impact of
integrating the memetic approach alongside Internet Plus tools on the writing aptitude of English as a
Foreign Language (EFL) learners. Students reported increased engagement with writing, attributing it to
the use of Internet plus tools. They also expressed that the memetic approach facilitated a deeper
understanding of cultural and social contexts in writing. Furthermore, the findings highlight a significant
improvement in studentsā writing skills following the intervention. This study provides significant insights
into the practical implementation of the memetic approach within English writing education, highlighting
the beneficial contribution of Internet Plus tools in enriching students' learning journeys.
A SURVEY TO REAL-TIME MESSAGE-ROUTING NETWORK SYSTEM WITH KLA MODELLINGijfcstjournal
Ā
Messages routing over a network is one of the most fundamental concept in communication which requires
simultaneous transmission of messages from a source to a destination. In terms of Real-Time Routing, it
refers to the addition of a timing constraint in which messages should be received within a specified time
delay. This study involves Scheduling, Algorithm Design and Graph Theory which are essential parts of
the Computer Science (CS) discipline. Our goal is to investigate an innovative and efficient way to present
these concepts in the context of CS Education. In this paper, we will explore the fundamental modelling of
routing real-time messages on networks. We study whether it is possible to have an optimal on-line
algorithm for the Arbitrary Directed Graph network topology. In addition, we will examine the message
routingās algorithmic complexity by breaking down the complex mathematical proofs into concrete, visual
examples. Next, we explore the Unidirectional Ring topology in finding the transmissionās
āmakespanā.Lastly, we propose the same network modelling through the technique of Kinesthetic Learning
Activity (KLA). We will analyse the data collected and present the results in a case study to evaluate the
effectiveness of the KLA approach compared to the traditional teaching method.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESijfcstjournal
Ā
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
SYSTEM ANALYSIS AND DESIGN FOR A BUSINESS DEVELOPMENT MANAGEMENT SYSTEM BASED...ijfcstjournal
Ā
A design of a sales system for professional services requires a comprehensive understanding of the
dynamics of sale cycles and how key knowledge for completing sales is managed. This research describes
a design model of a business development (sales) system for professional service firms based on the Saudi
Arabian commercial market, which takes into account the new advances in technology while preserving
unique or cultural practices that are an important part of the Saudi Arabian commercial market. The
design model has combined a number of key technologies, such as cloud computing and mobility, as an
integral part of the proposed system. An adaptive development process has also been used in implementing
the proposed design model.
AN ALGORITHM FOR SOLVING LINEAR OPTIMIZATION PROBLEMS SUBJECTED TO THE INTERS...ijfcstjournal
Ā
Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict
functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the
role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy
relational inequality constraints is investigated. The feasible region is formed as the intersection of two
inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the
resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with
max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a
necessary and sufficient condition and three other necessary conditions are presented to conceptualize the
feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal
objective value. Also, it is proved that the optimal solution of the problem is always resulted from the
unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented
to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is
proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can
easily generate a feasible test problem and employ our algorithm to it.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Ā
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that
open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility
of each sensor nodes are measured through the water atmosphere from the water flow for sensor based
protocol formations. Researchers have developed many routing protocols. However, those lost their charm
with the time. This can be the demand of the age to supply associate degree upon energy-efficient and
ascendable strong routing protocol for under water actuator networks. During this work, the authors tend
to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to
offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use
of total energy consumption and ensures packet transmission which redirects as an additional reliability in
compare to different routing protocols. In this work, the authors have used the level of forwarding node,
residual energy and distance from the forwarding node to the causing node as a proof in multicasting
technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a
wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned
protocol.
STRUCTURAL DYNAMICS AND EVOLUTION OF CAPSULE ENDOSCOPY (PILL CAMERA) TECHNOLO...ijfcstjournal
Ā
This research paper examined and re-evaluates the technological innovation, theory, structural dynamics
and evolution of Pill Camera(Capsule Endoscopy) technology in redirecting the response manner of small
bowel (intestine) examination in human. The Pill Camera (Endoscopy Capsule) is made up of sealed
biocompatible material to withstand acid, enzymes and other antibody chemicals in the stomach is a
technology that helps the medical practitioners especially the general physicians and the
gastroenterologists to examine and re-examine the intestine for possible bleeding or infection. Before the
advent of the Pill camera (Endoscopy Capsule) the colonoscopy was the local method used but research
showed that some parts (bowel) of the intestine canāt be reach by mere traditional method hence the need
for Pill Camera. Countless number of deaths from stomach disease such as polyps, inflammatory bowel
(Crohnās diseases), Cancers, Ulcer, anaemia and tumours of small intestines which ordinary would have
been detected by sophisticated technology like Pill Camera has become norm in the developing nations.
Nevertheless, not only will this paper examine and re-evaluate the Pill Camera Innovation, theory,
Structural dynamics and evolution it unravelled and aimed to create awareness for both medical
practitioners and the public.
AN OPTIMIZED HYBRID APPROACH FOR PATH FINDINGijfcstjournal
Ā
Path finding algorithm addresses problem of finding shortest path from source to destination avoiding
obstacles. There exist various search algorithms namely A*, Dijkstra's and ant colony optimization. Unlike
most path finding algorithms which require destination co-ordinates to compute path, the proposed
algorithm comprises of a new method which finds path using backtracking without requiring destination
co-ordinates. Moreover, in existing path finding algorithm, the number of iterations required to find path is
large. Hence, to overcome this, an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new technique(modified 8-
neighbor approach). The proposed algorithm can become essential part in location based, network, gaming
applications. grid traversal, navigation, gaming applications, mobile robot and Artificial Intelligence.
EAGRO CROP MARKETING FOR FARMING COMMUNITYijfcstjournal
Ā
The Major Occupation in India is the Agriculture; the people involved in the Agriculture belong to the poor
class and category. The people of the farming community are unaware of the new techniques and Agromachines, which would direct the world to greater heights in the field of agriculture. Though the farmers
work hard, they are cheated by agents in todayās market. This serves as a opportunity to solve
all the problems that farmers face in the current world. The eAgro crop marketing will serve as a better
way for the farmers to sell their products within the country with some mediocre knowledge about using
the website. This would provide information to the farmers about current market rate of agro-products,
their sale history and profits earned in a sale. This site will also help the farmers to know about the market
information and to view agricultural schemes of the Government provided to farmers.
EDGE-TENACITY IN CYCLES AND COMPLETE GRAPHSijfcstjournal
Ā
It is well known that the tenacity is a proper measure for studying vulnerability and reliability in graphs.
Here, a modified edge-tenacity of a graph is introduced based on the classical definition of tenacity.
Properties and bounds for this measure are introduced; meanwhile edge-tenacity is calculated for cycle
graphs and also for complete graphs.
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
Ā
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
PSTECEQL: A NOVEL EVENT QUERY LANGUAGE FOR VANETāS UNCERTAIN EVENT STREAMSijfcstjournal
Ā
In recent years, the complex event processing technology has been used to process the VANETās temporal
and spatial event streams. However, we usually cannot get the accurate data because the device sensing
accuracy limitations of the system. We only can get the uncertain data from the complex and limited
environment of the VANET. Because the VANETās event streams are consist of the uncertain data, so they
are also uncertain. How effective to express and process these uncertain event streams has become the core
issue for the VANET system. To solve this problem, we propose a novel complex event query language
PSTeCEQL (probabilistic spatio-temporal constraint event query language). Firstly, we give the definition
of the possible world model of VANETās uncertain event streams. Secondly, we propose an event query
language PSTeCEQL and give the syntax and the operational semantics of the language. Finally, we
illustrate the validity of the PSTeCEQL by an example.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
Ā
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
A MUTATION TESTING ANALYSIS AND REGRESSION TESTINGijfcstjournal
Ā
Software testing is a testing which conducted a test to provide information to client about the quality of the
product under test. Software testing can also provide an objective, independent view of the software to
allow the business to appreciate and understand the risks of software implementation. In this paper we
focused on two main software testing āmutation testing and mutation testing. Mutation testing is a
procedural testing method, i.e. we use the structure of the code to guide the test program, A mutation is a
little change in a program. Such changes are applied to model low level defects that obtain in the process
of coding systems. Ideally mutations should model low-level defect creation. Mutation testing is a process
of testing in which code is modified then mutated code is tested against test suites. The mutations used in
source code are planned to include in common programming errors. A good unit test typically detects the
program mutations and fails automatically. Mutation testing is used on many different platforms, including
Java, C++, C# and Ruby. Regression testing is a type of software testing that seeks to uncover
new software bugs, or regressions, in existing functional and non-functional areas of a system after
changes such as enhancements, patches or configuration changes, have been made to them. When defects
are found during testing, the defect got fixed and that part of the software started working as needed. But
there may be a case that the defects that fixed have introduced or uncovered a different defect in the
software. The way to detect these unexpected bugs and to fix them used regression testing. The main focus
of regression testing is to verify that changes in the software or program have not made any adverse side
effects and that the software still meets its need. Regression tests are done when there are any changes
made on software, because of modified functions.
GREEN WSN- OPTIMIZATION OF ENERGY USE THROUGH REDUCTION IN COMMUNICATION WORK...ijfcstjournal
Ā
Advances in micro fabrication and communication techniques have led to unimaginable proliferation of
WSN applications. Research is focussed on reduction of setup operational energy costs. Bulk of operational
energy costs are linked to communication activities of WSN. Any progress towards energy efficiency has a
potential of huge savings globally. Therefore, every energy efficient step is an endeavour to cut costs and
āGo Greenā. In this paper, we have proposed a framework to reduce communication workload through: Innetwork compression and multiple query synthesis at the base-station and modification of query syntax
through introduction of Static Variables. These approaches are general approaches which can be used in
any WSN irrespective of application.
A NEW MODEL FOR SOFTWARE COSTESTIMATION USING HARMONY SEARCHijfcstjournal
Ā
Accurate and realistic estimation is always considered to be a great challenge in software industry.
Software Cost Estimation (SCE) is the standard application used to manage software projects. Determining
the amount of estimation in the initial stages of the project depends on planning other activities of the
project. In fact, the estimation is confronted with a number of uncertainties and barriersā, yet assessing the
previous projects is essential to solve this problem. Several models have been developed for the analysis of
software projects. But the classical reference method is the COCOMO model, there are other methods
which are also applied such as Function Point (FP), Line of Code(LOC); meanwhile, the expert`s opinions
matter in this regard. In recent years, the growth and the combination of meta-heuristic algorithms with
high accuracy have brought about a great achievement in software engineering. Meta-heuristic algorithms
which can analyze data from multiple dimensions and identify the optimum solution between them are
analytical tools for the analysis of data. In this paper, we have used the Harmony Search (HS)algorithm for
SCE. The proposed model which is a collection of 60 standard projects from Dataset NASA60 has been
assessed.The experimental results show that HS algorithm is a good way for determining the weight
similarity measures factors of software effort, and reducing the error of MRE.
AGENT ENABLED MINING OF DISTRIBUTED PROTEIN DATA BANKSijfcstjournal
Ā
Mining biological data is an emergent area at the intersection between bioinformatics and data mining
(DM). The intelligent agent based model is a popular approach in constructing Distributed Data Mining
(DDM) systems to address scalable mining over large scale distributed data. The nature of associations
between different amino acids in proteins has also been a subject of great anxiety. There is a strong need to
develop new models and exploit and analyze the available distributed biological data sources. In this study,
we have designed and implemented a multi-agent system (MAS) called Agent enriched Quantitative
Association Rules Mining for Amino Acids in distributed Protein Data Banks (AeQARM-AAPDB). Such
globally strong association rules enhance understanding of protein composition and are desirable for
synthesis of artificial proteins. A real protein data bank is used to validate the system.
International Journal on Foundations of Computer Science & Technology (IJFCST)ijfcstjournal
Ā
International Journal on Foundations of Computer Science & Technology (IJFCST) is a Bi-monthly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of the Foundations of Computer Science & Technology. Over the last decade, there has been an explosion in the field of computer science to solve various problems from mathematics to engineering. This journal aims to provide a platform for exchanging ideas in new emerging trends that needs more focus and exposure and will attempt to publish proposals that strengthen our goals. Topics of interest include, but are not limited to the following:
Because the technology is used largely in the last decades; cybercrimes have become a significant
international issue as a result of the huge damage that it causes to the business and even to the ordinary
users of technology. The main aims of this paper is to shed light on digital crimes and gives overview about
what a person who is related to computer science has to know about this new type of crimes. The paper has
three sections: Introduction to Digital Crime which gives fundamental information about digital crimes,
Digital Crime Investigation which presents different investigation models and the third section is about
Cybercrime Law.
DISTRIBUTION OF MAXIMAL CLIQUE SIZE UNDER THE WATTS-STROGATZ MODEL OF EVOLUTI...ijfcstjournal
Ā
In this paper, we analyze the evolution of a small-world network and its subsequent transformation to a
random network using the idea of link rewiring under the well-known Watts-Strogatz model for complex
networks. Every link u-v in the regular network is considered for rewiring with a certain probability and if
chosen for rewiring, the link u-v is removed from the network and the node u is connected to a randomly
chosen node w (other than nodes u and v). Our objective in this paper is to analyze the distribution of the
maximal clique size per node by varying the probability of link rewiring and the degree per node (number
of links incident on a node) in the initial regular network. For a given probability of rewiring and initial
number of links per node, we observe the distribution of the maximal clique per node to follow a Poisson
distribution. We also observe the maximal clique size per node in the small-world network to be very close
to that of the average value and close to that of the maximal clique size in a regular network. There is no
appreciable decrease in the maximal clique size per node when the network transforms from a regular
network to a small-world network. On the other hand, when the network transforms from a small-world
network to a random network, the average maximal clique size value decreases significantly
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Ā
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
Ā
As AI technology is pushing into IT I was wondering myself, as an āinfrastructure container kubernetes guyā, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefitās both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
Ā
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
ā¢ The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
ā¢ Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
ā¢ Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
ā¢ Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Ā
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Ā
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
Ā
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
Ā
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more āmechanicalā approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Ā
Clients donāt know what they donāt know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clientsā needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Ā
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overviewā
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Ā
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Ā
Applications of artificial immune system a review
1. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
DOI:10.5121/ijfcst.2015.5104 35
APPLICATIONS OF ARTIFICIAL IMMUNE
SYSTEM: A REVIEW
Arannya.S
Faculty of Information Technology
University of Moratuwa
ABSTRACT
The Biological Immune System is a remarkable information processing and self-learning system that offers
stimulation to build Artificial Immune System (AIS).During the last two decades, the field of AIS is
progressing slowly and steadily as a branch of Computational Intelligence (CI). At present the AIS
algorithms such as Negative Selection Theory, Clonal Selection Theory, Immune Networks Theory, Danger
theory and Dendritic Cell Algorithm are widely used to solve many real world problems in a vast range of
domain areas such as Network Intrusion Detection (NID), Anomaly Detection, Clustering and
classification and Pattern recognition. This review paper critically discusses the theoretical foundation,
research methodologies and applications of the AIS.
KEYWORDS
Artificial Immune System, Negative Selection, Clonal Selection, Immune Network.
1. INTRODUCTION
Almost all the human inventions have taken nature functions as the inspiration, especially human
body and its functions lead to emergence of Artificial Intelligence Techniques. The Artificial
Neural Networks are inspired by human neural network and its functions, Genetic Algorithms are
inspired by biological genetic functions, and likewise the Artificial Immune System (AIS) is also
inspired by Biological Immune System (BIS) and its functions. But not like artificial neural
networks and genetic algorithms, the AIS has extracted almost all the functions of BIS, as BIS is
a robust, error tolerance, decentralized and adaptive system[1].
The concept of AIS was proposed by Farmer, Packard and Perelson in late 1980s, but it has
emerged in 1990s as a class of computational intelligence [2]. In BIS, white blood cells protecting
our body from unwanted attacks from fungus, bacteria and viruses, by having well established
network system [3]. By mimicking the cells and organism of BIS such as B-cells, T-cells, born
marrow and antigens as in instance of a class, the immunologists have implemented five
algorithms, namely Clonal Selection Theory (CST), Immune Network Theory (INT), Negative
Selection Theory (NSA), Danger theory and Dendritic Cells Algorithm (DCA). Among these
CST, INT and NST have well established by immunologists as first generation of AIS, but
Danger theory and DCA are not yet well proven but they have many potentially interesting
background as the second generation of AIS [4]. However, as an emerging artificial intelligence
technique, AIS has already reached to a significant level with number of approaches to address
many real world complex problems in a vast range of domain areas such as anomaly detection,
pattern recognition, optimization, intrusion detection. Further these approaches can also be
applied into robotics too.
2. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
36
Prior to that, Section II gives a brief introduction to BIS where AIS is inspired from. Section III
give the Classes of AIS, IV the section will discuss the applications of AIS and finally the
Conclusion for conclude the review work.
2. OVERVIEW OF BIS
The BIS is naturally well sophisticated, and decentralized, error tolerance, robust and adaptive
system which plays two major roles; protecting the body against invading micro-organism such
as fungi, bacteria and virus and keeping them out by failing them or destroying them and
regulating bodily functions. The immunologists have found that, the BIS have two functional
parts, namely innate immune system and adaptive immune system. The function of innate
immune system is responding to known threats while the adaptive immune system is tackling the
encountered threats. However within these two parts they have little cross over when they are
functioning against pathogens [5]
The key ability of BIS is, it can distinguish the bodyās own cells- called self-cells and foreign
cells-called non-self-cells. Normally the immune system works with self-cells which are carrying
molecules, but when noticed a cell or organism carrying foreign invaders (non-self), it will
quickly launch the attack; this is so called immune response. Another major capability of BIS is,
it can remember millions of distinguishing enemies. Therefore they can produce secretions and
can match up those cells and wipe nearly all of them out, by having a dynamic communication
network [6].
The organs of BIS that are spread throughout the body are called lymphoid organs, as they are
generated by lymphocytes (white blood cells), and are the key players of BIS. Lymphocytes are
produced by bone marrow (itās the source of all blood cells), which is in the hollow center of
bones and by using blood vessels, lymphocytes are travelling throughout the body. The
lymphocytes have three subclasses; namely B-cells, T-cells and NKT cells and AIS are
mimicking the functions of these cells [7].
The B-cells works primarily by concealing solvable know as antibodies and they mill around a
lymph node and wait for an antigen. Once the antigen arrives it will match up with a specific
antibody and proceed the immune response. At that time the antigen binds the antibody, the B-
cell overwhelm it and the B-cell becomes large plasma, which can produce number of antibody
copies (up to 100 million copies an hour), after a special helper T-cell joins the action. Then these
antibodies will travel throughout the body by bloodstream to search more antigens. The
antibodies of B-cells cannot kill an invading organism by themselves, but they make those
antigens by their antibodies and let other immune cells to kill them [8].
The T-cells contribute to immune action in two ways; some help to regulate the overall immune
response while the others which are called cytotoxic directly contact the non-self ācells (the cell
marked by the antibodies of B-cells) and abolish them. The helper T-cells play a major role here.
They are responsible to activate many immune cells including B-cells and other T-cells. The
Killer cells (NKT) can be divided into at least two parts; cytotoxic T-cells and natural killer cells
and both contain granules filled with intoxicating chemicals to destroy on contact [9].
3. THE CLASSES OF AIS
This section will discuss about the classes of AIS and the existing approaches of those classes.
3.1. The Clonal Selection Theory (CST)
Burnet has proposed CST in 1959. As we have seen in the Section II, when B-cells encountered a
non-self-cell it will automatically take the immune action against the cell. By that it will create
3. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
37
plasma from the particular antigen to destroy the antibody [10]. This concept is extracted by CST.
The CST has three main features;
ļ· The new cells copy their parents (clone) exposed to a transformation mechanism with
high rates. ļ
ļ· Eliminate the newly distinguished lymphocytes carrying self-reactive receptors. ļ
ļ· Proliferation and separation on contact of mature cells with non-self-cells. ļ
Table 1. The approaches of CST [11], [10]
Author and Year Algorithm Purpose of the algorithm
Castro and Zuben (2002) CLONALG Learning and Optimization.
Rouchen (2003) Immunity Clonal Strategy Optimization.
Algorithm (ICS)
Garret (2004) Adaptive Clonal Selection Increase the efficiency of
(ACS) (is an alternation of clones when doing
CLONALG) Optimization.
Yu and Hou (2004) Enhanced CLONALG Enhance the efficiency of
Detection and learning.
Camples (2005) Rea-Coded Clonal Selection Electromagnetic design
Algorithm (RCSA). Optimization.
Gong (2207) Extended CLONALG Increase the efficiency of
learning by using logic
adaptive method to learn the
Antibody population.
3.2. Negative Selection Theory (NST)
The needs of negative selection are to provide tolerance for self-cells. It deals with the immune
system's ability to detect non-self-cells without reacting to self-cells. During the production of T-
cells, a pseudo-random genetic rearrangement process will be preceded by receptors. Then they
undertake an editing process in the thymus which is called the negative selection [12].
4. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
38
Table 2. The approaches of NST [11]
Author Algorithms Purpose of those
algorithms
Forrest (1994) Founder of NSA Anomaly Detection
Ayara (2002) NSMutation Removes Data redundancy
Gonzalez and Cannady Self-adaptive NSA Anomaly detection
(2004)
Igawa and Ohashi (2008) Artificial Negative Selection Classification and Clustering
Classifier (ANSC)
3.3. Immune Network Theory
The Immune Network Theory was proposed in the mid-seventies (Jerne 1974). The theory was
that the immune system maintains an idiotic network of interconnected B cells for non-self-cell
identification. These cells both increase and overwhelm each other in certain ways that lead to
the steadiness of the network. Two B cells are connected if the affinities they share exceed a
certain inception, and the strength of the connection is directly proportional to the affinity they
share [13].
Table 3. The approaches of INT [11], [13]
Author Algorithms Purpose of those
algorithms
Timmis (2000) Artificial Immune Network Data Analysis
(AINE)
Castro and Zuben (2000) aiNet (with some features of Increase the efficiency of
AINE) Data Analysis
Castro and Timmis (2002) Hierarchy of aiNet Data Analysis and
Clustering
Nasraoui (2003) TECNO-STREAMS Clustering by detecting
unwanted entries.
Bentley and Timmis (2204) Fractal Immune Network Classification and Clustering
regardless of the data.
Lug and Liu (2004) Reactive Immune Network Mobile Robot Learning
(RIN) Navigation Strategies
5. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.5, No.1, January 2015
39
3.4. Danger Theory
The advocator Matzinger has proposed this theory in 2002, and it has become popular among
immunologists during last decade. The idea behind her proposal is foreignness of a cell is not the
important factor to trigger the immune response and selfness is not guarantee of acceptance [14].
The BIS do not react for the nutrient cells which have got into the body via consuming foods and
drinks even though they are not self-cells. Therefore she has concluded that BIS reacting danger
instead of foreignness. Danger is a signal which is emitted by an unnaturally injured cell.
Table 4. The approaches of danger theory [14]
Author Algorithms Purpose of those
algorithms
Aickelin and Cayzer (2002) Applications of Danger To distinguish between the
Theory (First paper on DT) positive danger signal and
negative danger signal
Prieto DTAL (Danger Theory Goalkeeper strategy in robot
Algorithm) soccer
Iqbal and Maarof DASTON Intelligent Data Processing
3.5. Dendritic Cell Algorithm (DCA)
The DCA is mimicking the function of naturally occurred dendritic cells (DCs), which are
responsible for foreign invaders detection. They can get the signal from unhealthy cells and by
combine those various signals, the DCs produce their own signals. These output signals are the
one which are instructing the BIS cells for the immune response against the non-self-cell [15].
Table 5. The approaches of DCA [15]
Author Algorithms Purpose of those
algorithms
Oates (2004) Robotic DCA (with image Robotic classification
processing) problems
J.Kim and P.Bentley (2006) BeeAIS-DC (inspired by Misbehavior detection
MANET routing protocol) system
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4. APPLICATIONS OF AIS
This section will discuss about the application areas of AIS by using the approaches which are
discussed in SECTION III.
4.1. Pattern recognition
Here binary string representation is used to recognize the pattern of a sample population.
Therefore B-cells are taken as objects and antibodies and antigens are represented as a string
form of 1ās and 0ās, where antibodyās representation is the complementary of antigens. For the
testing purpose the antigens are represented in three different ways, each with 20 elements and
each will be 33% of the population (Figure 1).
The Farmers approach has used to find how well B-cellsā antibody matches with the presented
antigens. The following figures depict how the matching can be done.
The Figure 2 depicts the original form of the antibody and antigens and how the antibodies have
changed according to the antigens.
Figure 1. Antigens representation
11111111110000000000 33%
00000000001111111111 33%
00000111111111100000 33%
Figure 2. Antibody matching
Antibody 0 0 1 0 1 0 1 1 1 0
Antigen 1 0 0 0 1 1 1 0 1 0
Bit shifted Antibody 0 1 1 1 0 0 0 1 0 1
Figure 3. Pseudo code of Antibody shifting
Repeat
c=Ag XOR Ab
M0=
For each section consisting of 2 04 more 1s record their length
M1=M0+ 2
If M1>Mmax the
Mmax=M1 Shift Ab right
1 bit
Until Ab shift complete.
The antibody and antigen matching algorithms are illustrated in Figure 3. (Ag-Antigen and Ab-
Antibody).
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Matching value calculation is represented in the Figure 4. The 12 indicate the number of
matching elements, and it has been added to the number of sections, for example 6 elements-26.
Therefore the final matching value is 88%.
As for the conclusion, seven set of different antigens pattern have been taken and they have been
tested by the same algorithm.
Test 1, 4 and 7 have shown almost a similar representation and the other set has represented in
different ways. The Figure 6 depicts how the result can be changed according to the antigen
pattern representation.
Figure 4 Matching value Calculation
Antigen: 0 1 1 0 0 0 0 1 1 1 1 0 1 1 0
Antibody: 1 0 0 1 1 1 1 0 0 1 0 1 1 0 1
XOR: 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 12
Length: 6 2 2 2
Match value: 12+ 2
6
+ 2
2
+ 2
2
+ 2
2
88
Figure 5. The set of antigens which has taken for testing
1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0
0 Test 1
1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0
0 Test 2
1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1
0 Test 3
0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
1 Test 4
1 0 0 0 0 1 0 1 0 1 0 0 1 1 1 1 0 1 1
0 Test 5
0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0
1 Test 6
0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0
0 Test 7
Figure 6. Results of testing
Test
No of B
cells Worst Average Best
Test 1 20 50 2678 16397
Test 2 20 47 2736 16393
Test 3 20 67 314 2055
Test 4 20 50 2678 16397
Test 5 20 38 110 263
Test 6 20 72 586 4109
Test 7 20 50 2678 16397
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The original antigens representations such as test 1, 4 and 7 have given higher best and average
matching value, and test 2ās best and average matching values are also merely closer to the
corresponding original representationās (Test 1, 4 &7) value. As from the observation it has been
concluded as AIS also have the same pattern recognition ability as BIS [16]. Therefore, the AIS
algorithms can used to identify any complex populationsā patterns in efficient way.
4.2. Anomaly Detection
Computer viruses, hardware false and fraudulent connections can be considered as anomalies. To
detect those anomalies the immunologists have extracted the concept of BIS. The main function
of anomaly detection is protecting the systems from intruders [17]. For this non-self-cells are
considered as unwanted or unauthorized connections and self-cells are considered as system
itself. It is not the case that non-self-cells are always triggers the immune response, by time some
non-self-cells can change as self-cells and wise versa. According to danger theory concept,
danger alarm is the key player that triggers the immune response. In AIS, the concept applied
system should transmit danger signal when it gets an unwanted or unauthorized access. The
following situation can be considered as unwanted access;
ļ· Too high or low memory usage. ļ
ļ· Insertion of inappropriate disk or its activities. ļ
ļ· Files changing unexpectedly and frequently (file size) ļ
ļ· Unwanted or unauthorized connection access. ļ
Once the danger alarm is emitted by the connected devise or a system, the detector system
(immune system) has to respond quickly. But for this action it not necessary to have the detector
system nearby (physically) the effected device or system. When the detector gets the danger
signals consequently, from the first signal the detector will identify the effected system and from
the following signals the detector will further get into the effected system and it will specifically
identify the dangerous component. After the confirmation of the antigen (effected part) the
detector will send the information to the corrective action part which have already inbuilt.
Therefore the anomaly detection system is avoiding human interactions and solving the issues by
its self [18].
4.3. Intrusion Detection Systems (IDS)
When come to the network security traditional IDS such as data encryption mechanism, fire wall
have already failed due to the malicious attacks and their highly technical attacking mechanisms
[7]. This unsecured situation tools the concept of IDS, but to build a proper IDS, the following
functions are needed,
ļ· Data collection ļ
ļ· Data processing ļ
ļ· Intrusion recognition- key activity. ļ
ļ· Reporting about the intrusion ļ
ļ· Response to the detected intrusion. ļ
To identify the unwanted accesses the IDS should monitor the connected system continuously, to
check whether the access has the symptoms to an attack or it is a valid use of the operator. Figure
7 depicts the generalized organization of IDS, where straight line indicates the flow of data and
control and dashed line response to intrusive activities [19].
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Figure 7. Organizations of IDS
The major function intrusion detection will happen in the manner of observing the intrusions and
matching with the already existing details about the intrusions (in knowledge base) and itsā
behavior, and the corrective action will be taken place according to the detected intrusion and its
destructiveness. Therefore well-knows will be detected by the system easily and efficiently and
the actions will also be taken place at the moment it got detected. Because of this features only
the IDS became famous in the industry. But intrusions are evolving continuously and polymorph.
Therefore pre-defined mechanism of IDS will fail in this case. To avoid this risk the system has to
update daily and it can be achieved automatically or manually. When updating the system
manually it will consume time and labor hours. But automatically updatable systems are possible
with the help of learning and adaptive algorithms. Unfortunately this type of knowledge bases is
most expensive, but this type of IDS will be more precise when compared to the traditional IDSs
[20].
4.4. Optimization and Clustering
Optimization and clustering is one of the most popular application areas in AIS [21]. More
advance AIS approaches used to address multimodal optimization problems. The CLONALG
algorithm has proposed to recognize the pattern and to optimize the population according to the
patterns. To address data compression and clustering issues including non-linear separable and
high-dimensional issues opt-aiNet algorithm which is a combination of CLONALG and ai-Net
has been used [22].
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When functioning, firstly the system will do clustering as an optimization process, where the
whole population will be divided into individual subgroups and the fitness peak will be indicated
in each cluster. Then the aiNet perform destruction collaboration of the network cells each other.
It is possible to maintain an active control of number of network population by evaluating the
degree of resemblance among the population. When compared to CLONALG, opt-aiNet has
performed better and found many peaks of the population as it has used greedy search algorithms
to find the peaks of each and every cluster (Figure 8) [23].
Figure 8 Optimization results
Equation (1) represents the function used for the greedy search.
( ) ( ) ( ) (1)
It is a combination of many local optimums and a global optimum. Binary string are used for the
values of x and y with the length of 22. As figure 4.8 indicates the solution covers most of the
peaks including the global optimum. Therefore the conclusion can be made in an effective
manner as its results will be coming in a sorted order. Therefore by using AIS approaches, the
optimization can be done more efficiently.
5. DISCUSION
AIS are a comparatively new field of study that has gained well-known recognition and attention
and it is a research area that ties the discipline of immunology, computer science and engineering.
AIS can be applied across various areas including learning, information retrieval,
communications, design, and health. This is extremely vital in the medical and electronic field.
During the past two decades, they have been attracted a lot of interest from researchers aiming to
develop immune-based models and techniques to solve complex computational problems. The
immunologists try to expand the approaches of artificial immune systems by studying the BIS and
its functionalities. By identifying more approaches the immunologists intend to address major
unresolved problems like cancers, space optimization and network security issues. AIS would
benefit more if there was more prominence in the use network security as it has been as a major
issue now days. However, as research is still in early phases, it is apparent that there are much
more research work to be done and has much promise in altering the world.
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ACKNOWLEDGEMENT
I would like to use this paper to thank my dada, my better half and my family for the grate
support they gave throughout my life time. I would like to express my thankfulness to Mrs. K.A
Dilini T Kulawansa and Miss. Priyanga Talagala in the Department of Computational
Mathematics of the Faculty of Information Technology of the University of Moratuwa for
proposing this area of research to me and for their instructions on approaching the problem and
valuable guidance throughout.
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AUTHOR
I am Arannya Sivasubramaiam, undergraduate of Faculty of Information Technology,
University of Moratuwa. Iām the first child who got into a university from my family,
therefore from that day onward my father has some hope on me and he wants me to
end up with as a high qualified personality in our society. Till the day started to read
about Artificial Immune System (AIS), I have put all my effort on basketball, but now
I have more interested on AIS, than basketball. As an aim of my life I would like to
follow my higher studies on AIS, for that I set this paper as the first objectives and
believe I have attained it as well.