The Pattern classification system classifies the pattern into feature space within a boundary. In case adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS), Biometric Authentication, the pattern classification systems are used. Spam filtering is an adversary
application in which data can be employed by humans to attenuate perspective operations. To appraise the
security issue related Spam Filtering voluminous machine learning systems. We presented a framework for the experimental evaluation of the classifier security in an adversarial environments, that combines and constructs on the arms race and security by design, Adversary modelling and Data distribution under
attack. Furthermore, we presented a SVM, LR and MILR classifier for classification to categorize email as legitimate (ham) or spam emails on the basis of thee text samples
JPJ1425 Security Evaluation of Pattern Classifiers under Attackchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
JPJ1425 Security Evaluation of Pattern Classifiers under Attackchennaijp
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of N...IJMER
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
experts’ 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
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
An effective approach for tackling network security
problems is Intrusion detection systems (IDS). These kind of
systems play a key role in network security as they can detect
different types of attacks in networks, including DoS, U2R Probe
and R2L. In addition, IDS are an increasingly key part of the
system’s defense. Various approaches to IDS are now being used,
but are unfortunately relatively ineffective. Data mining techniques
and artificial intelligence play an important role in security
services. We will present a comparative study of three wellknown
intelligent algorithms in this paper. These are Radial Basis
Functions (RBF), Multilayer Perceptrons (MLP) and Support
Vector Machine (SVM).This work’s main interest is to benchmark
the performance of these3 intelligent algorithms. This is done by
using a dataset of about 9,000 connections, randomly chosen from
KDD'99’s 10% dataset. In addition, we investigate these
algorithms’ performance in terms of their attack classification
accuracy. The Simulation results are also analyzed and the
discussion is then presented. It has been observed that SVM with a
linear kernel (Linear-SVM) gives a better performance than MLP
and RBF in terms of its detection accuracy and processing speed.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A multi classifier prediction model for phishing detectioneSAT Journals
Abstract Phishing is the technique of stealing personal and sensitive information from an email client by sending emails that impersonates like the ones from some trustworthy organizations. Phishing mails are a specific type of spam mails; however the effects of them are much more terrible than alternate sorts. Mostly the phishing attackers aim the clients of the financial organizations, so its detection needs high priority. Lots of research activities are done to detect the phished emails, in the proposed methodology a multi-classifier prediction model is introduced for detecting phished emails. Our contention is that solitary classifier prediction might not be satisfactory to urge the clearest picture of the phishing email; multi-classifier prediction has accuracy 99.8% with an FP rate of 0.8%. Keywords: Phishing email detection, Machine learning techniques, Multi-classifier prediction model, Majority voting
Improved spambase dataset prediction using svm rbf kernel with adaptive boosteSAT Journals
Abstract Spam is no more garbage but risk as it includes virus attachments and spyware agents which make the recipients’ system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning algorithms have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should deal with it. In this paper we have proposed Hybrid classifier Adaptive boost with support vector machine RBF kernel on Spambase dataset. We have also extracted the features first by Principal component analysis. General Terms: Email Spam classification. Keywords: Adaboost, classifier, ensemble, machine learning, spam email, SVM.
Multi Stage Filter Using Enhanced Adaboost for Network Intrusion DetectionIJNSA Journal
Based on the analysis and distribution of network attacks in KDDCup99 dataset and real time traffic, this paper proposes a design of multi stage filter which is an efficient and effective approach in dealing with various categories of attacks in networks. The first stage of the filter is designed using Enhanced Adaboost with Decision tree algorithm to detect the frequent attacks occurs in the network and the second stage of the filter is designed using enhanced Adaboost with Naïve Byes algorithm to detect the moderate attacks occurs in the network. The final stage of the filter is used to detect the infrequent
attack which is designed using the enhanced Adaboost algorithm with Naïve Bayes as a base learner. Performance of this design is tested with the KDDCup99 dataset and is shown to have high detection rate with low false alarm rates.
Adversarial Attacks and Defenses in Malware Classification: A SurveyCSCJournals
As malware continues to grow more sophisticated and more plentiful - traditional signature and heuristics-based defenses no longer cut it. Instead, the industry has recently turned to using machine learning for malicious file detection. The challenge with this approach is that machine learning itself comes with vulnerabilities - and if left unattended presents a new attack surface for attackers to exploit.
In this paper we present a survey of research in the area of machine learning-based malware classifiers, the attacks they encounter, and the defensive measures available. We start by reviewing recent advances in malware classification, including the most important works using deep learning. We then discuss in detail the field of adversarial machine learning and conduct an exhaustive review of adversarial attacks and defenses in the field of malware classification.
USING CATEGORICAL FEATURES IN MINING BUG TRACKING SYSTEMS TO ASSIGN BUG REPORTSijseajournal
Most bug assignment approaches utilize text classification and information retrieval techniques. These
approaches use the textual contents of bug reports to build recommendation models. The textual contents of
bug reports are usually of high dimension and noisy source of information. These approaches suffer from
low accuracy and high computational needs. In this paper, we investigate whether using categorical fields
of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports
instead of textual description. We build a classification model by utilizing the categorical features, as a
representation, for the bug report. The experimental evaluation is conducted using three projects namely
NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug
assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In
addition, it shows that the categorical features can improve the classification accuracy
Progress of Machine Learning in the Field of Intrusion Detection Systemsijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into
computer systems are increasing. Implementing intrusion detection systems have become extremely
important to help maintain good network security. Support vector machines (SVMs), a classic pattern
recognition tool, have been widely used in intrusion detection. They can handle very large data with high
efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model
enriched with a Gaussian kernel function based on the features of the training data for intrusion detection.
The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection
efficiency and false alarm rate, which can give better coverage and make detection more efficient.
11421ijcPROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYST...ijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
More Related Content
Similar to SPAM FILTERING SECURITY EVALUATION FRAMEWORK USING SVM, LR AND MILR
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of N...IJMER
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
experts’ 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
A web application detecting dos attack using mca and tameSAT Journals
Abstract
Interconnected systems, such as all kind of servers including web servers, are been always under the threats of network attackers. There are many popular attacks like man in middle attack, cross site scripting, spamming etc. but Denial of service attack is considered to be one of most dangerous attack on the networked applications. The attack causes many serious issues on these computing systems A denial-of-service (DoS) attack is an attempt to make a machine or network resource unavailable to the intended users. The performance of the server is reduced by the DoS attack, so, to increase the efficiency of the server, detection of the attack is necessary. Hence Multivariate Correlation Analysis’ issued, this approach employs triangle area for extracting the correlation information between network traffic. Our implemented system is evaluated using KDD Cup 99 data set, and the treatment of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The implemented system has capability of learning new patterns of legitimate network traffic hence it detect both known and unknown types of DoS attacks and we can say that It is working on the principle of anomaly based attack detection. Triangle-area-based technique is used to speed up the process. The stored legitimate profiles has to keep secured so Detection e=mechanism for the SQL injection is also implemented in the system. The system designed to carry out attack detection is a question-answer portal i.e. a web application and hence the system is using HTTP protocol unlike previous systems which were using TCP. Keywords: Denial-of-Service attack, Features Normalization, Triangle Area Map(TAM), Multivariate Correlation Analysis(MCA), anomaly based detection, SQL injection, HTTP, and TCP,
An effective approach for tackling network security
problems is Intrusion detection systems (IDS). These kind of
systems play a key role in network security as they can detect
different types of attacks in networks, including DoS, U2R Probe
and R2L. In addition, IDS are an increasingly key part of the
system’s defense. Various approaches to IDS are now being used,
but are unfortunately relatively ineffective. Data mining techniques
and artificial intelligence play an important role in security
services. We will present a comparative study of three wellknown
intelligent algorithms in this paper. These are Radial Basis
Functions (RBF), Multilayer Perceptrons (MLP) and Support
Vector Machine (SVM).This work’s main interest is to benchmark
the performance of these3 intelligent algorithms. This is done by
using a dataset of about 9,000 connections, randomly chosen from
KDD'99’s 10% dataset. In addition, we investigate these
algorithms’ performance in terms of their attack classification
accuracy. The Simulation results are also analyzed and the
discussion is then presented. It has been observed that SVM with a
linear kernel (Linear-SVM) gives a better performance than MLP
and RBF in terms of its detection accuracy and processing speed.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A multi classifier prediction model for phishing detectioneSAT Journals
Abstract Phishing is the technique of stealing personal and sensitive information from an email client by sending emails that impersonates like the ones from some trustworthy organizations. Phishing mails are a specific type of spam mails; however the effects of them are much more terrible than alternate sorts. Mostly the phishing attackers aim the clients of the financial organizations, so its detection needs high priority. Lots of research activities are done to detect the phished emails, in the proposed methodology a multi-classifier prediction model is introduced for detecting phished emails. Our contention is that solitary classifier prediction might not be satisfactory to urge the clearest picture of the phishing email; multi-classifier prediction has accuracy 99.8% with an FP rate of 0.8%. Keywords: Phishing email detection, Machine learning techniques, Multi-classifier prediction model, Majority voting
Improved spambase dataset prediction using svm rbf kernel with adaptive boosteSAT Journals
Abstract Spam is no more garbage but risk as it includes virus attachments and spyware agents which make the recipients’ system ruined, therefore, there is an emerging need for spam detection. Many spam detection techniques based on machine learning algorithms have been proposed. As the amount of spam has been increased tremendously using bulk mailing tools, spam detection techniques should deal with it. In this paper we have proposed Hybrid classifier Adaptive boost with support vector machine RBF kernel on Spambase dataset. We have also extracted the features first by Principal component analysis. General Terms: Email Spam classification. Keywords: Adaboost, classifier, ensemble, machine learning, spam email, SVM.
Multi Stage Filter Using Enhanced Adaboost for Network Intrusion DetectionIJNSA Journal
Based on the analysis and distribution of network attacks in KDDCup99 dataset and real time traffic, this paper proposes a design of multi stage filter which is an efficient and effective approach in dealing with various categories of attacks in networks. The first stage of the filter is designed using Enhanced Adaboost with Decision tree algorithm to detect the frequent attacks occurs in the network and the second stage of the filter is designed using enhanced Adaboost with Naïve Byes algorithm to detect the moderate attacks occurs in the network. The final stage of the filter is used to detect the infrequent
attack which is designed using the enhanced Adaboost algorithm with Naïve Bayes as a base learner. Performance of this design is tested with the KDDCup99 dataset and is shown to have high detection rate with low false alarm rates.
Adversarial Attacks and Defenses in Malware Classification: A SurveyCSCJournals
As malware continues to grow more sophisticated and more plentiful - traditional signature and heuristics-based defenses no longer cut it. Instead, the industry has recently turned to using machine learning for malicious file detection. The challenge with this approach is that machine learning itself comes with vulnerabilities - and if left unattended presents a new attack surface for attackers to exploit.
In this paper we present a survey of research in the area of machine learning-based malware classifiers, the attacks they encounter, and the defensive measures available. We start by reviewing recent advances in malware classification, including the most important works using deep learning. We then discuss in detail the field of adversarial machine learning and conduct an exhaustive review of adversarial attacks and defenses in the field of malware classification.
USING CATEGORICAL FEATURES IN MINING BUG TRACKING SYSTEMS TO ASSIGN BUG REPORTSijseajournal
Most bug assignment approaches utilize text classification and information retrieval techniques. These
approaches use the textual contents of bug reports to build recommendation models. The textual contents of
bug reports are usually of high dimension and noisy source of information. These approaches suffer from
low accuracy and high computational needs. In this paper, we investigate whether using categorical fields
of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports
instead of textual description. We build a classification model by utilizing the categorical features, as a
representation, for the bug report. The experimental evaluation is conducted using three projects namely
NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug
assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In
addition, it shows that the categorical features can improve the classification accuracy
Progress of Machine Learning in the Field of Intrusion Detection Systemsijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into
computer systems are increasing. Implementing intrusion detection systems have become extremely
important to help maintain good network security. Support vector machines (SVMs), a classic pattern
recognition tool, have been widely used in intrusion detection. They can handle very large data with high
efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model
enriched with a Gaussian kernel function based on the features of the training data for intrusion detection.
The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection
efficiency and false alarm rate, which can give better coverage and make detection more efficient.
11421ijcPROGRESS OF MACHINE LEARNING IN THE FIELD OF INTRUSION DETECTION SYST...ijcisjournal
With the growth in the use of the Internet and local area networks, malicious attacks and intrusions into computer systems are increasing. Implementing intrusion detection systems have become extremely important to help maintain good network security. Support vector machines (SVMs), a classic pattern recognition tool, have been widely used in intrusion detection. They can handle very large data with high efficiency, are easy to use, and exhibit good prediction behavior. This paper presents a new SVM model enriched with a Gaussian kernel function based on the features of the training data for intrusion detection. The new model is tested with the CICIDS2017 dataset. The test proves better results in terms of detection efficiency and false alarm rate, which can give better coverage and make detection more efficient.
Similar to SPAM FILTERING SECURITY EVALUATION FRAMEWORK USING SVM, LR AND MILR (20)
NEW ONTOLOGY RETRIEVAL IMAGE METHOD IN 5K COREL IMAGESijcax
Semantic annotation of images is an important research topic on both image understanding and database
or web image search. Image annotation is a technique to choosing appropriate labels for images with
extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we
present a model, which combined effective features of visual topics (global features over an image) and
regional contexts (relationship between the regions in Image and each other regions images) to automatic
image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet
ontology) for the semantic relationships between tags in the classification and improving semantic gap
exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed
method of image annotation in addition to reducing the complexity of the classification, increased accuracy
compared to the another methods
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
COMPARATIVE ANALYSIS OF ROUTING PROTOCOLS IN MOBILE AD HOC NETWORKSijcax
A Mobile Ad Hoc Network (MANET) is a collection of mobile nodes that want to communicate without any
pre-determined infrastructure and fixed organization of available links. Each node in MANET operates as
a router, forwarding information packets for other mobile nodes. There are many routing protocols that
possess different performance levels in different scenarios. The main task is to evaluate the existing routing
protocols and finding by comparing them the best one. In this article we compare AODV, DSR, DSDV,
OLSR and DYMO routing protocols in mobile ad hoc networks (MANETs) to specify the best operational
conditions for each MANETs protocol. We study these five MANETs routing protocols by different
simulations in NS-2 simulator. We describe that pause time parameter affect their performance. This
performance analysis is measured in terms of Packet Delivery Ratio, Average End-to-End Delay,
Normalized Routing Load and Average Throughput.
PREDICTING ACADEMIC MAJOR OF STUDENTS USING BAYESIAN NETWORKS TO THE CASE OF ...ijcax
In this study, which took place current year in the city of Maragheh in IRAN. Number of high school
students in the fields of study: mathematics, Experimental Sciences, humanities, vocational, business and
science were studied and compared. The purpose of this research is to predict the academic major of high
school students using Bayesian networks. The effective factors have been used in academic major selection
for the first time as an effective indicator of Bayesian networks. Evaluation of Impacts of indicators on
each other, discretization data and processing them was performed by GeNIe. The proper course would be
advised for students to continue their education.
A Multi Criteria Decision Making Based Approach for Semantic Image Annotation ijcax
Automatic image annotation has emerged as an important research topic due to its potential application on
both image understanding and web image search. This paper presents a model, which integrates visual
topics and regional contexts to automatic image annotation. Regional contexts model the relationship
between the regions, while visual topics provide the global distribution of topics over an image. Previous
image annotation methods neglected the relationship between the regions in an image, while these regions
are exactly explanation of the image semantics, therefore considering the relationship between them are
helpful to annotate the images. Regional contexts and visual topics are learned by PLSA (Probability
Latent Semantic Analysis) from the training data. The proposed model incorporates these two types of
information by MCDM (Multi Criteria Decision Making) approach based on WSM (Weighted Sum
Method). Experiments conducted on the 5k Corel dataset demonstrate the effectiveness of the proposed
model.
On Fuzzy Soft Multi Set and Its Application in Information Systems ijcax
Research on information and communication technologies have been developed rapidly since it can be
applied easily to several areas like computer science, medical science, economics, environments,
engineering, among other. Applications of soft set theory, especially in information systems have been
found paramount importance. Recently, Mukherjee and Das defined some new operations in fuzzy soft
multi set theory and show that the De-Morgan’s type of results hold in fuzzy soft multi set theory with
respect to these newly defined operations. In this paper, we extend their work and study some more basic
properties of their defined operations. Also, we define some basic supporting tools in information system
also application of fuzzy soft multi sets in information system are presented and discussed. Here we define
the notion of fuzzy multi-valued information system in fuzzy soft multi set theory and show that every fuzzy
soft multi set is a fuzzy multi valued information system.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
SPAM FILTERING SECURITY EVALUATION FRAMEWORK USING SVM, LR AND MILR ijcax
The Pattern classification system classifies the pattern into feature space within a boundary. In case
adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS),
Biometric Authentication, the pattern classification systems are used. Spam filtering is an adversary
application in which data can be employed by humans to attenuate perspective operations. To appraise the
security issue related Spam Filtering voluminous machine learning systems. We presented a framework for
the experimental evaluation of the classifier security in an adversarial environments, that combines and
constructs on the arms race and security by design, Adversary modelling and Data distribution under
attack. Furthermore, we presented a SVM, LR and MILR classifier for classification to categorize email as
legitimate (ham) or spam emails on the basis of thee text samples.
Visually impaired people face many problems in their day to day lives. Among them, outdoor navigation is
one of the major concerns. The existing solutions based on Wireless Sensor Networks(WSN) and Global
Positioning System (GPS) track ZigBee units or RFID (Radio Frequency Identification) tags fixed on the
navigation system. The issues pertaining to these solutions are as follows: (1) It is suitable only when the
visually impaired person is commuting in a familiar environment; (2) The device provides only a one way
communication; (3) Most of these instruments are heavy and sometimes costly. Preferable solution would
be to make a system which is easy to carry and cheap.
The objective of this paper is to break down the technological barriers, and to propose a system by
developing an Android App which would help a visually impaired person while traveling via the public
transport system like Bus. The proposed system uses an inbuilt feature of smart phone such as GPS
location tracker to track the location of the user and Text to Speech converter. The system also integrates
Google Speech to Text converter for capturing the voice input and converts them to text. This system
recommends the requirement of installing a GPS module in buses for real time tracking. With minor
modification, this App can also help older people for independent navigation.
INTELLIGENT AGENT FOR PUBLICATION AND SUBSCRIPTION PATTERN ANALYSIS OF NEWS W...ijcax
The rapid growth of Internet has revolutionized online news reporting. Many users tend to use online news
websites to obtain news information. When considering Sri Lanka, there are numerous news websites,
which are subscribed on a daily basis. With the rise in this number of news websites, the Sri Lankan
authorities of media face the issue of lacking a proper methodology or a tool which is capable of tracking
and regulating publications made by different disseminators of news.
This paper proposes a News Agent toolbox which periodically extracts news articles and associated
comments with the aid of a concept called Mapping Rules; to classify them into Personalized Categories
defined in terms of keywords based Category Profiles. The proposed tool also analyzes comments made by
the readers with the aid of simple statistical techniques to discover the most popular news articles and
fluctuations in popularity of news stories.
ADVANCED E-VOTING APPLICATION USING ANDROID PLATFORMijcax
The advancement in the mobile devices, wireless and web technologies given rise to the new application
that will make the voting process very easy and efficient. The E-voting promises the possibility of
convenient, easy and safe way to capture and count the votes in an election[1]. This research project
provides the specification and requirements for E-Voting using an Android platform. The e-voting means
the voting process in election by using electronic device. The android platform is used to develop an evoting application. At first, an introduction about the system is presented. Sections II and III describe all
the concepts (survey, design and implementation) that would be used in this work. Finally, the proposed evoting system will be presented. This technology helps the user to cast the vote without visiting the polling
booth. The application follows proper authentication measures in order to avoid fraud voters using the
system. Once the voting session is completed the results can be available within a fraction of seconds. All
the candidates vote count is encrypted and stored in the database in order to avoid any attacks and
disclosure of results by third person other than the administrator. Once the session is completed the admin
can decrypt the vote count and publish results and can complete the voting process.
The design of silicon chips in every semiconductor industry involves the testing of these chips with other
components on the board. The platform developed acts as power on vehicle for the silicon chips. This
Printed Circuit Board design that serves as a validation platform is foundational to the semiconductor
industry.
The manual/repetitive design activities that accompany the development of this board must be minimized to
achieve high quality, improve design efficiency, and eliminate human-errors. One of the time consuming
tasks in the board design is the Trace Length matching. The paper aims to reduce the length matching time
by automating it using SKILL scripts.
RESEARCH TRENDS İN EDUCATIONAL TECHNOLOGY İN TURKEY: 2010-2018 YEAR THESIS AN...ijcax
The purpose of this research is the analysis using meta-analysis of studies in the field of Educational
Technology in Turkey and in the field is to demonstrate how to get to that trend. For this purpose, a total of
263 studies were analyzed including 98 theses and 165 articles published between 2010-2018. Purpose
sampling method was used when selecting publications. In the research, while selecting articles and theses;
Turkey addressed; YOK Tez Tarama Database, Journal of Hacettepe University Faculty of Education,
Educational Sciences : Theory & Practice Journal, Education and Science Journal, Elementary Education
Online Journal, The Turkish Online Journal of Education and The Turkish Online Journal of Educational
Technology used in journals. Publications have been reviewed under 11 criteria. Index, year of
publication, research scope, method, education level, sample, number of samples, data collection methods,
analysis techniques, and research tendency, research topics in Educational Technology Research in Turkey
has revealed. The data is interpreted based on percentage and frequency and the results are shown using
the table.
RESEARCH TRENDS İN EDUCATIONAL TECHNOLOGY İN TURKEY: 2010-2018 YEAR THESIS AN...ijcax
The purpose of this research is the analysis using meta-analysis of studies in the field of Educational
Technology in Turkey and in the field is to demonstrate how to get to that trend. For this purpose, a total of
263 studies were analyzed including 98 theses and 165 articles published between 2010-2018. Purpose
sampling method was used when selecting publications. In the research, while selecting articles and theses;
Turkey addressed; YOK Tez Tarama Database, Journal of Hacettepe University Faculty of Education,
Educational Sciences : Theory & Practice Journal, Education and Science Journal, Elementary Education
Online Journal, The Turkish Online Journal of Education and The Turkish Online Journal of Educational
Technology used in journals. Publications have been reviewed under 11 criteria. Index, year of
publication, research scope, method, education level, sample, number of samples, data collection methods,
analysis techniques, and research tendency, research topics in Educational Technology Research in Turkey
has revealed. The data is interpreted based on percentage and frequency and the results are shown using
the table
IMPACT OF APPLYING INTERNATIONAL QUALITY STANDARDS ON MEDICAL EQUIPMENT IN SA...ijcax
With the great development that, modern medical technology is witnessing today, medical devices and
equipment have become a basic pillar of any healthcare system in the world and cannot be dispensed with,
so we find competition between the major companies that manufacture medical devices and equipment
resulting in a huge variety of complex modern medical technologies. These medical devices and equipment
require high accuracy in manufacturing and packaging in addition to operation, maintenance, and followup, because any error in any of the previous stages will have bad consequences for the patients and the
health system, there are many accidents that have led to some deaths. Therefore, we find that many medical
device producers and medical companies in addition to health service providers seek to find systems and
protocols to reduce accidents resulting from medical devices. As a result, many systems have recently
appeared that seek to protect from the dangers of medical devices and equipment. This research aims to
conduct a study of the effects of international standards on the safety of medical devices and equipment
and reduce their risks. By counting the international standards in force in the Kingdom of Saudi Arabia
that are applied by the Saudi Food and Drug Authority, making questionnaires, and distributing them to
health service providers and regulatory bodies for medical devices and equipment, considering the data,
these data will be analysed and evaluated the effectiveness of quality systems and standards in maintaining
Effectiveness and quality of medical devices and equipment. The study will include governmental and
private health services sectors.
Developing Product Configurator Tool Using CADs’ API with the help of Paramet...ijcax
Order placingis a crucial phase of lifecycle of a Mass-customizable product and seeks improvement in
Mechanical industry. ‘Product Configurator’ is a good solution to bring in data transparency and speed
up the process. Configuration tools arebeing used on a very small scale,reasons being lack of awareness
and dearer costs of existing tools. In this research work a product configurator is developedfor
Hydraulic Actuator (HA).This method uses Applicable Programing Interface (API) of a CAD tool coupled
with Visual Basics (VB) and MS Excel.Itis a standaloneapplication of VB and its integration into web
portal can be the future scope. The final aim was to reduce time delay at CRM phase,bring more
transparency in the ordering system and to establish a method which, small and medium scale enterprises
canafford. Trails on the tool developed generated Part-Assembly drawings, BOM and JT files in
moments.
DESIGN AND DEVELOPMENT OF CUSTOM CHANGE MANAGEMENT WORKFLOW TEMPLATES AND HAN...ijcax
A large no. of automobile companies finding a convinient way to manage design changes with the use of
various PLM techniques. Change in any product is something that should occur on timely basis to match
up with customer requirement and cost reduction. The change made in the vehicle designs directly affects
various concerned agencies. Automobile Vehicle structures contains thousands of parts and if there is any
change is occurring in child parts then it becomes important to track that impacted part, propose a solution
on that part and release a new assembly structure with feasible changes such that all efforts need to be
done for cost reduction.
Visually impaired people face many problems in their day to day lives. Among them, outdoor navigation is
one of the major concerns. The existing solutions based on Wireless Sensor Networks(WSN) and Global
Positioning System (GPS) track ZigBee units or RFID (Radio Frequency Identification) tags fixed on the
navigation system. The issues pertaining to these solutions are as follows: (1) It is suitable only when the
visually impaired person is commuting in a familiar environment; (2) The device provides only a one way
communication; (3) Most of these instruments are heavy and sometimes costly. Preferable solution would
be to make a system which is easy to carry and cheap.
The objective of this paper is to break down the technological barriers, and to propose a system by
developing an Android App which would help a visually impaired person while traveling via the public
transport system like Bus. The proposed system uses an inbuilt feature of smart phone such as GPS
location tracker to track the location of the user and Text to Speech converter. The system also integrates
Google Speech to Text converter for capturing the voice input and converts them to text. This system
recommends the requirement of installing a GPS module in buses for real time tracking. With minor
modification, this App can also help older people for independent navigation.
TEACHER’S ATTITUDE TOWARDS UTILISING FUTURE GADGETS IN EDUCATION ijcax
Today’s era is an era of modernization and globalization. Everything is happening at a very fast rate
whether it is politics, societal reforms, commercialization, transportation, or educational innovations. In
every few second, technology grows either in the form of arrival of the new devices/gadgets with millions of
apps and these latest technological objects may be in the form of hardware/software devices. We are the
educationists, teachers, students and stakeholders of present Indian educational system. These
gadgets/devices are partly being used by us or most of them are still unaware of these innovative
technologies due to the mass media or economical factor. So, there is a need to improvise ourselves
towards utilizing the future gadgets in order to explore the educational uses, barriers and preparatoryneeds of these available devices for educational purposes. This paper aims to study the opinion of the
teacher-educators about the usage of future gadgets in higher education. It will also contribute towards
establishing the list of latest technological devices, and how it can enhances the process of teachinglearning system.
IMPROVING REAL TIME TASK AND HARNESSING ENERGY USING CSBTS IN VIRTUALIZED CLOUDijcax
Cloud computing provides the facility for the business customers to scale up and down their resource usage
based on needs. This is because of the virtualization technology. The scheduling objectives are to improve
the system’s schedule ability for the real-time tasks and to save energy. To achieve the objectives, we
employed the virtualization technique and rolling-horizon optimization with vertical scheduling operation.
The project considers Cluster Scoring Based Task Scheduling (CSBTS) algorithm which aims to decrease
task’s completion time and the policies for VM’s creation, migration and cancellation are to dynamically
adjust the scale of cloud in a while meets the real-time requirements and to save energy
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
SPAM FILTERING SECURITY EVALUATION FRAMEWORK USING SVM, LR AND MILR
1. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
DOI:10.5121/ijcax.2016.3302 19
SPAM FILTERING SECURITY EVALUATION
FRAMEWORK USING SVM, LR AND MILR
Kunjali Pawar1
and Madhuri Patil2
1
M.E. Student, Dr. D.Y.Patil School of Engg. And Technology, Lohegaon, Pune,
Savitribai Phule Pune University, India.
2
Assistant Professor, Dr. D.Y.Patil School of Engg. And Technology, Lohegaon, Pune,
Savitribai Phule Pune University, India.
ABSTRACT
The Pattern classification system classifies the pattern into feature space within a boundary. In case
adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS),
Biometric Authentication, the pattern classification systems are used. Spam filtering is an adversary
application in which data can be employed by humans to attenuate perspective operations. To appraise the
security issue related Spam Filtering voluminous machine learning systems. We presented a framework for
the experimental evaluation of the classifier security in an adversarial environments, that combines and
constructs on the arms race and security by design, Adversary modelling and Data distribution under
attack. Furthermore, we presented a SVM, LR and MILR classifier for classification to categorize email as
legitimate (ham) or spam emails on the basis of thee text samples.
KEYWORDS
Adversary Model, Multiple Instance Logistic Regression, Pattern Classification, Security Evaluation, Spam
Filtering
1. INTRODUCTION
This Machine learning systems bid an unparalleled resilience in acting with emerging input in a
variation of applications, such as Intrusion Detection Systems (IDS) [1] and the spam filtering of
e-mails. Whenever machine learning is used to prevent illegal or unsanctioned activity [2] and
there is an economic incentive, adversaries will attempt to avoid the stability provided.
Constraints on how adversaries can employs the training data (TR) and test data (TS) for
classifiers used to encounter incredulous behaviour make problems in this area tractable and
interesting. Pattern classification has earned eminence in different fields which contains security
concerned applications like the Spam Filtering, the Network Intrusion Detection System (NIDS),
and Biometric Authentication to distinguish between the legitimate and malicious samples [3].
In specific, there are three main clear issues can be recognized: (a) examining the weaknesses
(vulnerabilities) of classification algorithms, and the corresponding attacks; (b) creating the novel
methodologies to assess the classifier security under these attacks, which is not possible using the
classical performance evaluation methodologies; (c) establishing the design methods [4] to
guarantee the classifier security in an adversarial environment. The goal of attacker is to defeat
the normal process of spam filters so that they can send spams [5].
2. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
20
The respite of the paper is unionized as follows: The section II, scrutinize about the problem
definition on the Security Evaluation. The section III, discuss a proposed system framework for
Spam Filtering Security Evaluation. The section IV scrutinizes the algorithms and evaluation of
these algorithms using examples. The remainder of section V covers expected results of
classifiers. The section VI, summarize the conclusion and the future scope.
2. PROBLEM STATEMENT
2.1. Problem statement
• Existing methods address one of the main open issues of evaluating at design phase the
security of pattern classifiers.
• Even though the design phase of secure classifiers is a different issue than security
evaluation, existing framework could be exploited to this end. For instance spam filtering
existing system considers SVM and LR classifier.
• To apply an empirical security evaluation framework and provide security to Spam
Filtering application and use best classifier in our framework.
2.2. Solving approach
The proposed system focuses on multiple instance logistic regression (MILR) strategy. In the
recommended strategy, emails are treated as bags of multiple instances [6] and a logistic model at
the instance level is indirectly learned by exploiting the bag level binomial log-likelihood
function [14].
3. PROPOSED FRAMEWORK
The contribution of this paper is-
• Classification of email using SVM, LR and MILR classifiers.
• Intent to increase classification results, classifier called Multiple Instance Logistic
Regression (MILR) is used.
• MILR differs from a single instance supervised learning [7], such that by splitting an
email into several instances, a MI learner will be capable to identify the spam part of the
text mail even though text mail has been injected with good words which solve the
efficiency issue for GWI attack [14].
The data distribution gives the training data and testing data separately [8]. The testing data can
be manually generated by the user during the compose emails. The assumptions can be given with
the help of Adversary Model [9]. Modelling the adversary is dependent on the attack scenarios. It
consists of goal, knowledge, capability, strategies of the adversary as shown in figure 1.
The classification application can be authenticated by authenticator. It consists of three classifiers
like SVM, LR and MILR. The classifiers acts like an algorithms. These classifiers give its
classification results either the email is spam or legitimate or normal. The training data can be
already trained by admin. This user can be an attacker or an authorized person. For this
application user can perform classification techniques (SVM, LR, and MILR) by means of
analysis. This training region subsists of all types of mails such as spam or ham. For classification
of testing part/mail using training part, different classifiers are used. SVM, LR and MILR
classifiers are used for classification and result analysis [11].
3. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
21
Figure1. Security Evaluation Framework using Spam Filtering
The Pattern classifier classifies or distinguishes the pattern (which is combination of feature
which can be characterized by the individuals) into the feature space or word space within a
boundary [10], [12], [13]. The goal is to partition the feature or word space in the class labeled
decision regions. Therefore, for the ambition of model selection considers that the developer
wants to appoint a Support Vector Machine (SVM) with a linear kernel, a Logistic Regression
(LR) classifier, and Multiple Instance Logistic Regression (MILR). In the proposed system, for
the classification purpose SVMs are actualized with the LibSVM, Logistic Regression (LR) and
Multiple Instance Logistic Regression (MILR) is used for practical analysis.
4. ALGORITHMS WITH EXAMPLE
Firstly, a framework is presented for the empirical evaluation of classifier based on simulation of
potential attack scenarios. The existing system considers SVM and LR classifiers [11]. The
proposed system focuses on multiple instance logistic regression (MILR) strategy.
4.1. SVM Classifier
Algorithm: SVM classifier
Input:
Set of email data ,
(Combination of positively and negatively labeled data)
Output: Classified email data (Spam/Ham)
4. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
22
Process:
Step1: Distribution of positively and negatively labeled data according to features.
Step2: Compute Mapping function Ø().
Step3: Set bias as 1 to every vectors
Step4: Compute dot (.) products equations
Step5: Calculate the value of discriminate Hyperplanes α1, α2, α3,..
Step6: Predict positive and negative samples
Example:
Figure 2 shows the evaluation of SVM classifier with an example.
Figure2. SVM example
4.2. LR Classifier
Algorithm: LR classifier
Consider y= 1, if word spam and y=0, if word is not Spam (Ham).
For Spam Filtering, classification of words as spam or ham
5. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
23
The probability of word is
The probability for spam per word is
The probability for ham per word is
And
Example:
Figure 3 shows the evaluation of LR classifier with an example.
Figure3. LR example
4.2. MILR Classifier
Algorithm: MILR classifier
Input:
Set of email data ,
Be probability that the ith
email is Positive,
Be probability that the ith
email is Negative
6. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
24
Is a vector of the word frequency counts (or tf-idf weight) of unique terms in every email.
Process:
Step 1: Binomial log-likelihood function is:
Step 2: In a single instance setting, probability has sigmoid response function as:
Step 3: In multiple instances, setting estimate the instance-level class probabilities
has a sigmoidal response function as:
Where, is the jth
instance in the ith
data, and and are the parameters that need to be
estimated.
Step 4: In a single instance setting, probability has sigmoid response function as:
Step 5: In multiple instance setting estimate the instance-level class probabilities
has a sigmoidal response function as:
5. EXPECTED RESULTS
The experimental results of the classification of legitimate (normal) and Spam e-mails are
computed using the accuracy of the classifiers. The input of the proposed system is the number of
testing samples. The computations of results are based on different mails. Some mails are trained
before test and some new mails are also tested in proposed system. The analysis shows that MILR
approach perform better as compare to SVM and LR algorithm by considering parameters like
time, mean absolute error, etc. The accuracy can be calculated using following formula,
Classification accuracy:
t
c
N
N
Ac =
Where, = Number of words which are correctly classified
= Total number of words
In this experiment, the efficiency of SVM, LR and MILR [11], [14] is evaluated to test the ability
of classifiers [15]. Figure2 shows the output of accuracy on every classifier. Here we use the
Spring Tool Suite (STS) with wampserver for database connectivity. Table 1 shows the accuracy
result for queries M1, M2, M3 and M4 mail in terms of percentage (%).
7. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
25
M1: Code demonstrates how to get input from user.
M2: This is too dirty place. Please clean otherwise wash it.
M3: I hate you idiot. I will kill you idiot.
M4: The Indian Mujahideen claimed responsibility for the Jaipur bombings through an email sent
to Indian media and declared open war against India.
TABLE I: Classification Accuracy
Figure2. Graphical representation of classification accuracy
M1 M2 M3 M4
SVM 58.93 61.24 74.49 58.93
LR 55.24 60.2 65.48 58.93
MILR 64.73 68.75 73.33 64.58
8. International Journal of Computer- Aided Technologies (IJCAx) Vol.3, No. 2/3, July 2016
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6. CONCLUSION
This paper focused on experimental security evaluation of the pattern classifiers which improve
prediction accuracy of spam filtering application. For classification and analysis three classifiers
are used, are called SVM, LR and MILR. The proposed framework acquainted on a model of the
adversary, and on a model of data distribution; accommodates an analytical approach for the
training and testing sets generation that accredits security evaluation and can furnish the
application distinct techniques. In the future, we will extend the data classification algorithm that
will improve accuracy and performance of the system by means of spam detection.
ACKNOWLEDGEMENTS
We would like to thank the researchers as well as publishers for making their resources available
and teachers for their guidance. We are thankful to the authorities of Savitribai Phule Pune
University and concern members for their constant guidelines and support. We are also thankful
to reviewer for their valuable suggestions and also thank the college authorities for providing the
required infrastructure and support.
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AUTHORS
Ms. Kunjali Pawar received Bachelor of Engineering degree in Computer Science &
Engineering in 2014 and now pursuing Post Graduation (M.E.) in the department of
Computer Engineering from Dr. D.Y.Patil School of Engineering and Technology in
the current academic year 2015-16. She is now studying for the domain Data Mining
and Information Retrieval as research purpose on Security Evaluation of Pattern
Classifier against attack using Spam filtering during her academic year.
Prof. Mrs. Madhuri Patil. She is presently working as an Assistant Professor in the
department of computer engineering, Dr. D. Y. Patil School of Engineering and
Technology, Pune, Maharashtra, India. She has 6 years experience in teaching field and
her research area is Data Mining and Information Retrieval.