SlideShare a Scribd company logo
IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661 Volume 4, Issue 3 (Sep.-Oct. 2012), PP 01-04
www.iosrjournals.org
www.iosrjournals.org 1 | Page
Utilization Data Mining to Detect Spyware
1
Parisa Bahraminikoo, 2
Mehdi Samiei yeganeh, 3
G.Praveen Babu
1, 2
(M.Tech.(S/w. Eng.), 3
(Associate Professor School of Information Technology, Jawaharlal Nehru
Technological University, Iran)
Abstract: Malicious software (malware) is any software that gives partial to full control of your computer to
do whatever the malware creator wants. Malware can be a virus, worm, Trojan, adware, spyware, root kit,
etc.Spyware is a type of malware (malicious software) installed on computers that collects information about
users without their knowledge. In the year 1956, Artificial Intelligence (AI) was established at Dartmuth College
during a conference. The technology developed so much that it started involving many other branches of
engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery
involving Artificial Intelligence. With the development of malware detection systems and Artificial Intelligence,
as a new technology for them, Artificial Intelligence has been applied in anti-virus engines. There are several AI
approaches that applied in spyware detection systems such as Artificial Neural Networks, Heuristic Technology
and Data Mining (DM) Technique. Heuristic-based Detection performs well against known Spyware but has not
been proven to be successful at detecting new spyware. In this paper we focus on DM-based malicious code
detectors using Breadth-First Search (BFS) approach, which are known to work well for detecting viruses and
similar software. BFS is a strategy for searching in a tree when search is limited to essentially two operations:
(a) visit and inspect a node of a tree; (b) gain access to visit the nodes that are neighbor to currently visited
node. The BFS begins at a root node and inspect all the neighboring nodes. Then for each of those neighbor
nodes in turn, it inspects their neighbor nodes which were unvisited, and so on.
Keywords: Spyware, Artificial intelligence, Data mining, Breadth-First Search.
I. Introduction
As the application of computer and Internet is more popular, it provides a convenient way to share the
information among different people; however it also gives chances to malware activities, such as propagating
malicious programs, including computer viruses [1]. Programs that have the potential to break the privacy and
security of a system can be labeled as Privacy Invasive Software [2]. These programs include: spyware, adware,
Trojans, greyware and backdoors [3]. Spyware is a type of malware (malicious software) installed
on computers that collects information about users without their knowledge. The presence of spyware is
typically hidden from the user and can be difficult to detect. Some spyware, such as keyloggers, may be
installed by the owner of a shared, corporate, or public computer intentionally in order to monitor users. While
the term spyware suggests software that monitors a user's computing, the functions of spyware can extend
beyond simple monitoring. Spyware can collect almost any type of data, including personal
information like Internet surfing habits, user logins, and bank or credit account information. Spyware can also
interfere with user control of a computer by installing additional software or redirecting Web browsers. Some
spyware can change computer settings, which can result in slow Internet connection speeds, un-authorized
changes in browser settings, or changes to software settings.
The goal of spyware is generally not to cause damage or to spread to other systems. Instead, spyware
programs monitor the behavior of users and steal private information, such as keystrokes and browsing patterns.
This information is then sent back to the spyware distributors and used as a basis for targeted advertisement
(e.g., pop-up ads) or marketing analysis [4]. AI is the science and engineering of making intelligent machines,
especially intelligent computer programs [1]. AI is set to play an important role in our lives. Researchers
produce new products which duplicate intelligence, understand speech, beat the opponent chess player, and
acting in complex conditions. The major problems of Artificial Intelligence include qualities such as knowledge,
planning, learning, reasoning, communication, perception and capability to move and control the objects [6].The
aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans
[5]. As following we will describe the main application of artificial intelligence that is applied in spyware
detection systems. The rest of this paper is organized as follows: section 2 briefly describes the Heuristic
Technology, Section 3 explains Data mining Technique and section 4 briefly describes the Neural Network
Technology.
Utilization Data Mining to Detect Spyware
www.iosrjournals.org 2 | Page
II. Heuristic Technology
At the present, the first and main application for spam filtering technique based on artificial
intelligence is Heuristic Technology. Current anti-spyware tools make use of signature-based methods by using
specific features or unique strings extracted from binaries or heuristic-based methods by using on the basis of
rules written by experts who define behavioral patterns as approaches against spyware. These approaches are
often considered ineffective against new malicious code [7, 8].
Heuristic Technology means "the ability of self-discovery" or "the knowledge and skills that use some
methods to determine", and intelligently analyze codes to detect the unknown virus by some rules while
scanning [9].Heuristics are used quite often in Artificial Intelligence based research. They are new and
constantly being refined by most antivirus companies over the last five years or so. Computational they are
much faster than signature based techniques. Heuristics look for a set of characteristics within a file in order to
determine whether or not it may be a potential threat. In a sense, heuristic anti-malware attempts to apply the
processes of human analysis to an object. In the same way that a human malware analyst would try to determine
the process of a given program and its actions, heuristic analysis performs the same intelligent decision-making
process, effectively acting as a virtual malware researcher. As the human malware analyst learns more from and
about emerging threats he or she can apply that knowledge to the heuristic analyzer through programming, and
improve future detection rates. Antivirus software may use one or several techniques to proactively detect
malware. The main essence of each method is to analyze the suspicious file’s characteristics and behavior to
determine if it is indeed malware.
The main concern with heuristic detection is that it often increases false positives. False positives are
when the antivirus software determines a file is malicious (and quarantines or deletes it) when in reality it is
perfectly fine and/or desired. Because some files may look like viruses but really aren’t, they are restricted and
stopped from working on your computer. In Heuristics based detection we can use Generic Signature, This
technique is particularly designed to locate variations of viruses. Several viruses are re-created and make
themselves known by a variety of names, but essentially come from the same family (or classification). Genetic
detection uses previous antivirus definitions to locate these similar “cousins” even if they use a slightly different
name or include some unusual characters. The best way to illustrate this idea is with identical twins. They may
have slightly different fingerprints, but their DNA is identical.
III. Neural Network Technology
An Artificial Neural Network (ANN) (Bishop, 1995) is an information processing paradigm that is
inspired by the way biological nervous systems (i.e., the brain) are modeled with regard to information
processing [13]. A neural network is designed to simulate a set of neurons, usually connected by synapses. In
the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to
another cell. Just as in biological systems, learning involves adjustments to the synaptic connections that exist
between the neurons. Neural networks can differ on: the way their neurons are connected; the specific kinds of
computations their neurons do; the way they transmit patterns of activity throughout the network; and the way
they learn including their learning rate. Neural networks are being applied to an increasing large number of real
world problems [14].
The neural network is configured for a specific application, such as data classification or pattern
recognition, through a learning process called training [15]. In [16] has introduced how to use Single layer
neural classifier to detection boot viruses, and the generic virus detector was incorporated into IBM Antivirus in
May, 1994.Its structure has been shown in Fig 1.
Figure 1: Single layer neural classifier
William Arnold and Gerald Tesauro constructed multiple neural network classifiers which can detect
unknown Win32 viruses by combining the individual classifier outputs using a voting procedure, following a
technique described in previous work (Kephart et al, 1995) on boot virus heuristics [17]. And the system has
508 achieved effectively.Authors of [18] presented a new rule generation method from neural networks formed
using a genetic algorithm (GA) with virus infection and deterministic mutation. This method can extract rules
(regularities) for a pattern classification and chaotic system identification by using the same system [1].
Utilization Data Mining to Detect Spyware
www.iosrjournals.org 3 | Page
IV. Data mining Technique
With the rapid development of Information Technology, the rapid growth of data has exceeded the
ability of the manual processing of data. So how to help people to extract the general knowledge from the mass
of data has become more and more important. In order to implement it, data mining technique is put forward and
soon becomes an active research direction. Data mining analyzes the observed sets to discover the unknown
relation and sum up the results of data analysis to make the owner of data to understand. Data Mining Algorithm
that is from Statistics, Pattern Identification, Machine Learning [5, 11], and Database and so on, has developed
comprehensive [1].
In [10] presented a data-mining framework that detects new, previously unseen malicious executables
accurately and automatically. The 2001 data mining study of malicious code [8] used. Three types of features,
i.e., Dynamic-link Library resource information, consecutive printable characters (strings) and byte sequences.In
[12] presented a network virus precaution system based on data mining shown in Fig 2.It can detect the
abnormal connecting behavior of network in real-time to discover the trace of worm virus, especially the
precaution action to the new worm virus to make administrator to adopt corresponding measure to avoid
tremendous loss.
Figure 2: The structure of warning system
Data mining techniques perform better than traditional techniques such as signature-base detection and
Heuristic-based detection. The focus of our analysis is executable files for the Windows platform. We use the
Waikato Environment for Knowledge Analysis (Weka) to perform the experiments. Weka is a suite of machine
learning algorithms and analysis tools, which is used in practice for solving data mining problems, first, we
extract features from the binary files We extract the features by using the Common Feature-based Extraction
(CFBE). The purpose of employing this approach is to evaluate two different techniques that use different types
of data representation, i.e., the occurrence of a feature and the frequency of a feature. CFBE method are used to
obtain Reduced Feature Sets (RFSs) which are then used to generate the ARFF files and includes instances from
the frequency range of 50-80. And we then apply a feature reduction method in order to reduce data set
complexity. In experiments for the detection of malware, sequences of bytes extracted from the hexadecimal
dump of the binary files have been represented by n-grams [3]. Finally, we convert the reduced feature set into
the Attribute-Relation File Format (ARFF). ARFF files are ASCII text files that include a set of data instances,
each described by a set of features [3].
Data mining base malicious approach have proven to be successful in detecting viruses and worms.
Overall accuracy of 90.5% is achieved with the BF-tree algorithms.
We evaluate each learning algorithm by performing cross-validation tests to ensure that the generated
classifiers are not tested on the training data. From the response of the classifiers the relevant confusion matrices
were created. Four metrics define the elements of the matrix: True Positives (TP), False Positives (FP), True
Negatives (TN) and False Negatives (FN).
Metric Abbreviation Meaning
True Positives TP Number of correctly identified benign programs.
False Positives FP Number of wrongly identified Spyware programs.
True Negatives TN Number of correctly identified Spyware programs.
False Negatives FN Number of wrongly identified benign programs.
Table 1 Evaluation metrics
We shall now demonstrate how multi-criteria metrics can be used as an approach to trade-off some of
the important aspects of EULA (End User License Agreement) classification [19]. The performance of each
classifier was evaluated using the true positive rate, false positive rate and overall accuracy which are defined as
follows:
True Positive Rate (TPR): Percentage of correctly identified benign programs (TP / TP+FN)
False Positive Rate (FPR): Percentage of wrongly identified Spyware programs (FP / TN+FP)
Overall Accuracy (ACC): Percentage of correctly identified programs (TP+TN / TP+TN+FP+FN)
Utilization Data Mining to Detect Spyware
www.iosrjournals.org 4 | Page
Table 2 show that Using the feature set produced by the CFBE feature selection method for n = 4, the BFT
decision tree classifier achieves the highest accuracy results in Frequency Range 50-80.
Table 2 Comparison of Algorithms for N-gram size = 4
V. Conclusion
Spyware technique has become the most important Prevention technique. With this technologies, the
system can detect virus invasion in real-time, and enlarge security management capacity of system
administrators to enhance the integrity of the infrastructure of information security.
With development of Artificial Intelligence technology has provided new methods and ideas for
spyware detection system. Intergraded spyware detection with AI will greatly improve the performance of the
existing spyware detection system, promote more effective artificial intelligence algorithms to be proposed, and
be applied in the popular detection field .The main objective of the present work is to establish a method in
Spyware detection research using data mining techniques. These techniques are used for information retrieval
and classification. Data mining-based malicious code detectors have been proven to be successful in detecting
clearly malicious code, e.g., like viruses and worms. Results from different studies have indicated that data
mining techniques perform better than traditional techniques against malicious code. However, spyware has not
received the same attention from researchers but it is spreading rapidly on both home and business computers.
Overall accuracy of 90.5% is achieved with the BF-tree algorithms.
References
[1]. Review on the application of Artificial Intelligence in Antivirus Detection System”, Xiao-bin Wang Guang-yuan Yang Yi-chao
Li Dan Liu.
[2]. M. Boldt and B. Carlsson, “Privacy-invasive software and preventive mechanisms,” 2nd International Conference on Systems and
Networks Communications, (ICSNC 2006), Oct. 28- Nov.2, IEEE Computer Society.
[3]. Detection of Spyware by Mining Executable Files” Raja Khurram Shazhad, Syed Imran Haider, Niklas Lavesson, 2010International
Conference onAvailability,Reliability and Security.
[4]. Behavior-based Spyware Detection” Engin Kirda and Christopher Kruegel, Greg Banks, Giovanni Vigna, and Richard A.
Kemmerer.
[5]. Review on use of Reinforcement Learning in Artificial Intelligence” Mehdi Samieiyeganeh , Parisa Bahraminikoo ,G.Praveen Babu
,12th
International Conference of Science and Technology Impact on Development and Justice held at Maulana Azad National Urdu
University, Hyderabad, India, on 7th
& 8th
February, 2012.
[6]. Chuck Williams. “A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE”, 10.0 109 83 IEEE.
[7]. C. D. Bozagac, “Application of Data Mining based Malicious Code Detection Techniques for Detecting new Spyware”, White
paper, Bilkent University, 2005.
[8]. M. Schultz, E. Eskin, F. Zadok, and S. Stolfo, “Data mining methods for detection of new malicious executables,” Proceedings of
IEEE Symposium on Security and Privacy, 14-16 May 2001, Los Alamitos,
[9]. Xianwei Zeng, Zhijun Zhang, and Zhi Zhang, “Heuristic skill of computer virus analysis based on virtual machine,” Computer
Applications and Software, Vol. 22(9), 2005, pp. 125-126.
[10]. Matthew G. Schultz, Eleazar Eskin, Erez Zadok, and Salvatore I. Stolfo, “Data Mining Methods for Detection of New Malicious
Executables,” The 2001 IEEE Symposium on Security and Privacy, Oakland. CA, 2001, pp.38-49.
[11]. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan.
[12]. Yufeng Yang, “The Network Virus Precaution System Based on Data Mining,” Journal of Shaoguan University, Vol. 26(12), 2005,
pp. 31-33.
[13]. Detection of Unknown Computer Worms based on Behavioral Classification of the Host”, Robert Moskovitch, Yuval Elovici, Lior
Rokach.
[14]. Review on Artificial Intelligence and Artificial neural networks” Mehdi Samieiyeganeh , ParisaBahraminikoo,G.PraveenBabu,
International Conference on Computing,Communications, Systems & Aeronautics (ICCCSA-12) , Organized by Malla Reddy
College of Engineering & Technology From March 30-31,2012. Hyderabad, India.
[15]. Artificial Intelligence: Neural Networks Simplified “Indranarain Ramlall1 University of Technology, Mauritius.
[16]. Kephart, J.O., "Biologically inspired defenses against computer viruses," Proceedings of International Joint Conference on Artificial
Intelligence, 1995, pp. 985-96.
[17]. R. Moskovitch, C. Feher, N. Tzachar, E. Berger, M. Gitelman, S. Dolev, and Y. Elovici, “Unknown malcode detection using
OPCODE representation,” 1st European Conference on Intelligence and Security Informatics, (EuroISI 2008), 3-5 Dec., Berlin,
Germany: Springer-Verlag, pp. 204-215.
[18]. Y. Elovici, A. Shabtai, R. Moskovitch, G. Tahan, and C. Glezer., “Applying Machine Learning Techniques for Detection of
Malicious Code in Network Traffic”, Proceedings of the 30th annual German conference on Advances in Artificial Intelligence, KI
2007, 10-13.
[19]. Niklas Lavesson , Martin Boldt , Paul Davidsson ,Andreas Jacobsson, “Learning to detect spyware using end user license
agreements”, Springer-Verlag London Limited 2009.
Algorithm Type TPR FPR ACC
BFT Frequency Range
50-80
Trees 0.992 0.977 0.731 0.730
89.896 (5.104)
88.5222 (5.899)
Random Forest
Frequency Range 50-80
Trees 0.979 0.960 0.665 0.720
89.489 (5.520)
87.077 (6.477)
Naive Bayes
Frequency Range 50-80
Bayes 0.973 0.916 0.730 0.705
88.209 (6.174)
83.703 (8.202)
SMO Frequency Range
50-80
Function 0.935 0.946
0.665
0.515
86.566 (8.130)
88.5222 (7.530)

More Related Content

What's hot

Basic survey on malware analysis, tools and techniques
Basic survey on malware analysis, tools and techniquesBasic survey on malware analysis, tools and techniques
Basic survey on malware analysis, tools and techniques
ijcsa
 
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSA SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
IJNSA Journal
 
Intrusion Detection System - False Positive Alert Reduction Technique
Intrusion Detection System - False Positive Alert Reduction TechniqueIntrusion Detection System - False Positive Alert Reduction Technique
Intrusion Detection System - False Positive Alert Reduction Technique
IDES Editor
 
Paper id 312201513
Paper id 312201513Paper id 312201513
Paper id 312201513
IJRAT
 
Vol 6 No 1 - October 2013
Vol 6 No 1 - October 2013Vol 6 No 1 - October 2013
Vol 6 No 1 - October 2013
ijcsbi
 
To use the concept of Data Mining and machine learning concept for Cyber secu...
To use the concept of Data Mining and machine learning concept for Cyber secu...To use the concept of Data Mining and machine learning concept for Cyber secu...
To use the concept of Data Mining and machine learning concept for Cyber secu...
Nishant Mehta
 
Network security using data mining concepts
Network security using data mining conceptsNetwork security using data mining concepts
Network security using data mining concepts
Jaideep Ghosh
 
A Study on Data Mining Based Intrusion Detection System
A Study on Data Mining Based Intrusion Detection SystemA Study on Data Mining Based Intrusion Detection System
A Study on Data Mining Based Intrusion Detection System
AM Publications
 
Optimised malware detection in digital forensics
Optimised malware detection in digital forensicsOptimised malware detection in digital forensics
Optimised malware detection in digital forensics
IJNSA Journal
 
Kg2417521755
Kg2417521755Kg2417521755
Kg2417521755
IJERA Editor
 
“AI techniques in cyber-security applications”. Flammini lnu susec19
“AI techniques in cyber-security applications”. Flammini lnu susec19“AI techniques in cyber-security applications”. Flammini lnu susec19
“AI techniques in cyber-security applications”. Flammini lnu susec19
Francesco Flammini
 
An enhanced approach for securing mobile agents from
An enhanced approach for securing mobile agents fromAn enhanced approach for securing mobile agents from
An enhanced approach for securing mobile agents from
eSAT Publishing House
 
An Intrusion Detection based on Data mining technique and its intended import...
An Intrusion Detection based on Data mining technique and its intended import...An Intrusion Detection based on Data mining technique and its intended import...
An Intrusion Detection based on Data mining technique and its intended import...
Editor IJMTER
 
Bt33430435
Bt33430435Bt33430435
Bt33430435
IJERA Editor
 
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
IJNSA Journal
 
Idps
IdpsIdps
Idps
iskrena
 

What's hot (16)

Basic survey on malware analysis, tools and techniques
Basic survey on malware analysis, tools and techniquesBasic survey on malware analysis, tools and techniques
Basic survey on malware analysis, tools and techniques
 
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLSA SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
A SURVEY ON MALWARE DETECTION AND ANALYSIS TOOLS
 
Intrusion Detection System - False Positive Alert Reduction Technique
Intrusion Detection System - False Positive Alert Reduction TechniqueIntrusion Detection System - False Positive Alert Reduction Technique
Intrusion Detection System - False Positive Alert Reduction Technique
 
Paper id 312201513
Paper id 312201513Paper id 312201513
Paper id 312201513
 
Vol 6 No 1 - October 2013
Vol 6 No 1 - October 2013Vol 6 No 1 - October 2013
Vol 6 No 1 - October 2013
 
To use the concept of Data Mining and machine learning concept for Cyber secu...
To use the concept of Data Mining and machine learning concept for Cyber secu...To use the concept of Data Mining and machine learning concept for Cyber secu...
To use the concept of Data Mining and machine learning concept for Cyber secu...
 
Network security using data mining concepts
Network security using data mining conceptsNetwork security using data mining concepts
Network security using data mining concepts
 
A Study on Data Mining Based Intrusion Detection System
A Study on Data Mining Based Intrusion Detection SystemA Study on Data Mining Based Intrusion Detection System
A Study on Data Mining Based Intrusion Detection System
 
Optimised malware detection in digital forensics
Optimised malware detection in digital forensicsOptimised malware detection in digital forensics
Optimised malware detection in digital forensics
 
Kg2417521755
Kg2417521755Kg2417521755
Kg2417521755
 
“AI techniques in cyber-security applications”. Flammini lnu susec19
“AI techniques in cyber-security applications”. Flammini lnu susec19“AI techniques in cyber-security applications”. Flammini lnu susec19
“AI techniques in cyber-security applications”. Flammini lnu susec19
 
An enhanced approach for securing mobile agents from
An enhanced approach for securing mobile agents fromAn enhanced approach for securing mobile agents from
An enhanced approach for securing mobile agents from
 
An Intrusion Detection based on Data mining technique and its intended import...
An Intrusion Detection based on Data mining technique and its intended import...An Intrusion Detection based on Data mining technique and its intended import...
An Intrusion Detection based on Data mining technique and its intended import...
 
Bt33430435
Bt33430435Bt33430435
Bt33430435
 
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...
 
Idps
IdpsIdps
Idps
 

Viewers also liked

Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  NetworksSecure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
IOSR Journals
 
Hide and Seek: Embedding Audio into RGB 24-bit Color Image Sporadically Usin...
Hide and Seek: Embedding Audio into RGB 24-bit Color Image  Sporadically Usin...Hide and Seek: Embedding Audio into RGB 24-bit Color Image  Sporadically Usin...
Hide and Seek: Embedding Audio into RGB 24-bit Color Image Sporadically Usin...
IOSR Journals
 
C0151216
C0151216C0151216
C0151216
IOSR Journals
 
J0956064
J0956064J0956064
J0956064
IOSR Journals
 
E0932226
E0932226E0932226
E0932226
IOSR Journals
 
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
IOSR Journals
 
E0543443
E0543443E0543443
E0543443
IOSR Journals
 
G0613235
G0613235G0613235
G0613235
IOSR Journals
 
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
IOSR Journals
 
G0533441
G0533441G0533441
G0533441
IOSR Journals
 
I0614550
I0614550I0614550
I0614550
IOSR Journals
 
L0956974
L0956974L0956974
L0956974
IOSR Journals
 
A0340108
A0340108A0340108
A0340108
IOSR Journals
 
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating  Online EducationPretext Knowledge Grids on Unstructured Data for Facilitating  Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
IOSR Journals
 
C0531519
C0531519C0531519
C0531519
IOSR Journals
 
G0563337
G0563337G0563337
G0563337
IOSR Journals
 
E0343443
E0343443E0343443
E0343443
IOSR Journals
 
I01055860
I01055860I01055860
I01055860
IOSR Journals
 
H0563843
H0563843H0563843
H0563843
IOSR Journals
 
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern  RecognitionA Comprehensive Overview of Clustering Algorithms in Pattern  Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
IOSR Journals
 

Viewers also liked (20)

Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  NetworksSecure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor  Networks
Secure Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Networks
 
Hide and Seek: Embedding Audio into RGB 24-bit Color Image Sporadically Usin...
Hide and Seek: Embedding Audio into RGB 24-bit Color Image  Sporadically Usin...Hide and Seek: Embedding Audio into RGB 24-bit Color Image  Sporadically Usin...
Hide and Seek: Embedding Audio into RGB 24-bit Color Image Sporadically Usin...
 
C0151216
C0151216C0151216
C0151216
 
J0956064
J0956064J0956064
J0956064
 
E0932226
E0932226E0932226
E0932226
 
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
The Performance Characteristics of Low Voltage Insulators in a Polluted Envir...
 
E0543443
E0543443E0543443
E0543443
 
G0613235
G0613235G0613235
G0613235
 
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems An Improved Ant-Based Algorithm for Minimum Degree  Spanning Tree Problems
An Improved Ant-Based Algorithm for Minimum Degree Spanning Tree Problems
 
G0533441
G0533441G0533441
G0533441
 
I0614550
I0614550I0614550
I0614550
 
L0956974
L0956974L0956974
L0956974
 
A0340108
A0340108A0340108
A0340108
 
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating  Online EducationPretext Knowledge Grids on Unstructured Data for Facilitating  Online Education
Pretext Knowledge Grids on Unstructured Data for Facilitating Online Education
 
C0531519
C0531519C0531519
C0531519
 
G0563337
G0563337G0563337
G0563337
 
E0343443
E0343443E0343443
E0343443
 
I01055860
I01055860I01055860
I01055860
 
H0563843
H0563843H0563843
H0563843
 
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern  RecognitionA Comprehensive Overview of Clustering Algorithms in Pattern  Recognition
A Comprehensive Overview of Clustering Algorithms in Pattern Recognition
 

Similar to Utilization Data Mining to Detect Spyware

Malware analysis and detection using reverse Engineering, Available at: www....
Malware analysis and detection using reverse Engineering,  Available at: www....Malware analysis and detection using reverse Engineering,  Available at: www....
Malware analysis and detection using reverse Engineering, Available at: www....
Research Publish Journals (Publisher)
 
Survey on Malware Detection Techniques
Survey on Malware Detection TechniquesSurvey on Malware Detection Techniques
Survey on Malware Detection Techniques
Editor IJMTER
 
Integrated honeypot
Integrated honeypotIntegrated honeypot
Integrated honeypot
IAEME Publication
 
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
IJET - International Journal of Engineering and Techniques
 
A Comprehensive Review On Intrusion Detection System And Techniques
A Comprehensive Review On Intrusion Detection System And TechniquesA Comprehensive Review On Intrusion Detection System And Techniques
A Comprehensive Review On Intrusion Detection System And Techniques
Kelly Taylor
 
Optimised Malware Detection in Digital Forensics
Optimised Malware Detection in Digital Forensics Optimised Malware Detection in Digital Forensics
Optimised Malware Detection in Digital Forensics
IJNSA Journal
 
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSA STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
ijaia
 
Self Evolving Antivirus Based on Neuro-Fuzzy Inference System
Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemSelf Evolving Antivirus Based on Neuro-Fuzzy Inference System
Self Evolving Antivirus Based on Neuro-Fuzzy Inference System
IJRES Journal
 
Malware Detection Approaches using Data Mining Techniques.pptx
Malware Detection Approaches using Data Mining Techniques.pptxMalware Detection Approaches using Data Mining Techniques.pptx
Malware Detection Approaches using Data Mining Techniques.pptx
Alamgir Hossain
 
A trust system based on multi level virus detection
A trust system based on multi level virus detectionA trust system based on multi level virus detection
A trust system based on multi level virus detection
UltraUploader
 
Autonomic Anomaly Detection System in Computer Networks
Autonomic Anomaly Detection System in Computer NetworksAutonomic Anomaly Detection System in Computer Networks
Autonomic Anomaly Detection System in Computer Networks
ijsrd.com
 
Ceis 9 padeep kumar_final_paper
Ceis 9 padeep kumar_final_paperCeis 9 padeep kumar_final_paper
Ceis 9 padeep kumar_final_paper
Alexander Decker
 
Detection and prevention of keylogger spyware attacks
Detection and prevention of keylogger spyware attacksDetection and prevention of keylogger spyware attacks
Detection and prevention of keylogger spyware attacks
IAEME Publication
 
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
IJCI JOURNAL
 
Ijfls05
Ijfls05Ijfls05
Ijfls05
ijfls
 
savi technical ppt.pptx
savi technical ppt.pptxsavi technical ppt.pptx
savi technical ppt.pptx
4GH20CS407POONAM
 
Vulnerability Prevention Using Ethical Hacking.pdf
Vulnerability Prevention Using Ethical Hacking.pdfVulnerability Prevention Using Ethical Hacking.pdf
Vulnerability Prevention Using Ethical Hacking.pdf
MithunJV
 
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
drennanmicah
 
System call frequency analysis-based generative adversarial network model for...
System call frequency analysis-based generative adversarial network model for...System call frequency analysis-based generative adversarial network model for...
System call frequency analysis-based generative adversarial network model for...
IJECEIAES
 
Detecting Unknown Attacks Using Big Data Analysis
Detecting Unknown Attacks Using Big Data AnalysisDetecting Unknown Attacks Using Big Data Analysis
Detecting Unknown Attacks Using Big Data Analysis
Editor IJMTER
 

Similar to Utilization Data Mining to Detect Spyware (20)

Malware analysis and detection using reverse Engineering, Available at: www....
Malware analysis and detection using reverse Engineering,  Available at: www....Malware analysis and detection using reverse Engineering,  Available at: www....
Malware analysis and detection using reverse Engineering, Available at: www....
 
Survey on Malware Detection Techniques
Survey on Malware Detection TechniquesSurvey on Malware Detection Techniques
Survey on Malware Detection Techniques
 
Integrated honeypot
Integrated honeypotIntegrated honeypot
Integrated honeypot
 
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
[IJET-V1I6P6] Authors: Ms. Neeta D. Birajdar, Mr. Madhav N. Dhuppe, Ms. Trupt...
 
A Comprehensive Review On Intrusion Detection System And Techniques
A Comprehensive Review On Intrusion Detection System And TechniquesA Comprehensive Review On Intrusion Detection System And Techniques
A Comprehensive Review On Intrusion Detection System And Techniques
 
Optimised Malware Detection in Digital Forensics
Optimised Malware Detection in Digital Forensics Optimised Malware Detection in Digital Forensics
Optimised Malware Detection in Digital Forensics
 
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODSA STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
A STATIC MALWARE DETECTION SYSTEM USING DATA MINING METHODS
 
Self Evolving Antivirus Based on Neuro-Fuzzy Inference System
Self Evolving Antivirus Based on Neuro-Fuzzy Inference SystemSelf Evolving Antivirus Based on Neuro-Fuzzy Inference System
Self Evolving Antivirus Based on Neuro-Fuzzy Inference System
 
Malware Detection Approaches using Data Mining Techniques.pptx
Malware Detection Approaches using Data Mining Techniques.pptxMalware Detection Approaches using Data Mining Techniques.pptx
Malware Detection Approaches using Data Mining Techniques.pptx
 
A trust system based on multi level virus detection
A trust system based on multi level virus detectionA trust system based on multi level virus detection
A trust system based on multi level virus detection
 
Autonomic Anomaly Detection System in Computer Networks
Autonomic Anomaly Detection System in Computer NetworksAutonomic Anomaly Detection System in Computer Networks
Autonomic Anomaly Detection System in Computer Networks
 
Ceis 9 padeep kumar_final_paper
Ceis 9 padeep kumar_final_paperCeis 9 padeep kumar_final_paper
Ceis 9 padeep kumar_final_paper
 
Detection and prevention of keylogger spyware attacks
Detection and prevention of keylogger spyware attacksDetection and prevention of keylogger spyware attacks
Detection and prevention of keylogger spyware attacks
 
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITER...
 
Ijfls05
Ijfls05Ijfls05
Ijfls05
 
savi technical ppt.pptx
savi technical ppt.pptxsavi technical ppt.pptx
savi technical ppt.pptx
 
Vulnerability Prevention Using Ethical Hacking.pdf
Vulnerability Prevention Using Ethical Hacking.pdfVulnerability Prevention Using Ethical Hacking.pdf
Vulnerability Prevention Using Ethical Hacking.pdf
 
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
1Running Head COMPUTER WORMS MALWARE IN CYBER SECURITY14COM.docx
 
System call frequency analysis-based generative adversarial network model for...
System call frequency analysis-based generative adversarial network model for...System call frequency analysis-based generative adversarial network model for...
System call frequency analysis-based generative adversarial network model for...
 
Detecting Unknown Attacks Using Big Data Analysis
Detecting Unknown Attacks Using Big Data AnalysisDetecting Unknown Attacks Using Big Data Analysis
Detecting Unknown Attacks Using Big Data Analysis
 

More from IOSR Journals

A011140104
A011140104A011140104
A011140104
IOSR Journals
 
M0111397100
M0111397100M0111397100
M0111397100
IOSR Journals
 
L011138596
L011138596L011138596
L011138596
IOSR Journals
 
K011138084
K011138084K011138084
K011138084
IOSR Journals
 
J011137479
J011137479J011137479
J011137479
IOSR Journals
 
I011136673
I011136673I011136673
I011136673
IOSR Journals
 
G011134454
G011134454G011134454
G011134454
IOSR Journals
 
H011135565
H011135565H011135565
H011135565
IOSR Journals
 
F011134043
F011134043F011134043
F011134043
IOSR Journals
 
E011133639
E011133639E011133639
E011133639
IOSR Journals
 
D011132635
D011132635D011132635
D011132635
IOSR Journals
 
C011131925
C011131925C011131925
C011131925
IOSR Journals
 
B011130918
B011130918B011130918
B011130918
IOSR Journals
 
A011130108
A011130108A011130108
A011130108
IOSR Journals
 
I011125160
I011125160I011125160
I011125160
IOSR Journals
 
H011124050
H011124050H011124050
H011124050
IOSR Journals
 
G011123539
G011123539G011123539
G011123539
IOSR Journals
 
F011123134
F011123134F011123134
F011123134
IOSR Journals
 
E011122530
E011122530E011122530
E011122530
IOSR Journals
 
D011121524
D011121524D011121524
D011121524
IOSR Journals
 

More from IOSR Journals (20)

A011140104
A011140104A011140104
A011140104
 
M0111397100
M0111397100M0111397100
M0111397100
 
L011138596
L011138596L011138596
L011138596
 
K011138084
K011138084K011138084
K011138084
 
J011137479
J011137479J011137479
J011137479
 
I011136673
I011136673I011136673
I011136673
 
G011134454
G011134454G011134454
G011134454
 
H011135565
H011135565H011135565
H011135565
 
F011134043
F011134043F011134043
F011134043
 
E011133639
E011133639E011133639
E011133639
 
D011132635
D011132635D011132635
D011132635
 
C011131925
C011131925C011131925
C011131925
 
B011130918
B011130918B011130918
B011130918
 
A011130108
A011130108A011130108
A011130108
 
I011125160
I011125160I011125160
I011125160
 
H011124050
H011124050H011124050
H011124050
 
G011123539
G011123539G011123539
G011123539
 
F011123134
F011123134F011123134
F011123134
 
E011122530
E011122530E011122530
E011122530
 
D011121524
D011121524D011121524
D011121524
 

Recently uploaded

Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
bellared2
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
Steven Carlson
 
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
OnBoard
 
Semantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software DevelopmentSemantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software Development
Baishakhi Ray
 
Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3
DianaGray10
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
Jimmy Lai
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
alexjohnson7307
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
FIDO Alliance
 
Acumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptxAcumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptx
BrainSell Technologies
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
Mastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for SuccessMastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for Success
David Wilson
 
What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024
Stephanie Beckett
 
Sonkoloniya documentation - ONEprojukti.pdf
Sonkoloniya documentation - ONEprojukti.pdfSonkoloniya documentation - ONEprojukti.pdf
Sonkoloniya documentation - ONEprojukti.pdf
SubhamMandal40
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
Bhajan Mehta
 
The Path to General-Purpose Robots - Coatue
The Path to General-Purpose Robots - CoatueThe Path to General-Purpose Robots - Coatue
The Path to General-Purpose Robots - Coatue
Razin Mustafiz
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
SAI KAILASH R
 
Discovery Series - Zero to Hero - Task Mining Session 1
Discovery Series - Zero to Hero - Task Mining Session 1Discovery Series - Zero to Hero - Task Mining Session 1
Discovery Series - Zero to Hero - Task Mining Session 1
DianaGray10
 
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
FIDO Alliance
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
Tech Guru
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 

Recently uploaded (20)

Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
Russian Girls Call Navi Mumbai 🎈🔥9920725232 🔥💋🎈 Provide Best And Top Girl Ser...
 
Vulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive OverviewVulnerability Management: A Comprehensive Overview
Vulnerability Management: A Comprehensive Overview
 
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
Mastering Board Best Practices: Essential Skills for Effective Non-profit Lea...
 
Semantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software DevelopmentSemantic-Aware Code Model: Elevating the Future of Software Development
Semantic-Aware Code Model: Elevating the Future of Software Development
 
Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3Communications Mining Series - Zero to Hero - Session 3
Communications Mining Series - Zero to Hero - Session 3
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
 
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
leewayhertz.com-AI agents for healthcare Applications benefits and implementa...
 
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
UX Webinar Series: Essentials for Adopting Passkeys as the Foundation of your...
 
Acumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptxAcumatica vs. Sage Intacct _Construction_July (1).pptx
Acumatica vs. Sage Intacct _Construction_July (1).pptx
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
Mastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for SuccessMastering OnlyFans Clone App Development: Key Strategies for Success
Mastering OnlyFans Clone App Development: Key Strategies for Success
 
What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024What's New in Teams Calling, Meetings, Devices June 2024
What's New in Teams Calling, Meetings, Devices June 2024
 
Sonkoloniya documentation - ONEprojukti.pdf
Sonkoloniya documentation - ONEprojukti.pdfSonkoloniya documentation - ONEprojukti.pdf
Sonkoloniya documentation - ONEprojukti.pdf
 
Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17Mule Experience Hub and Release Channel with Java 17
Mule Experience Hub and Release Channel with Java 17
 
The Path to General-Purpose Robots - Coatue
The Path to General-Purpose Robots - CoatueThe Path to General-Purpose Robots - Coatue
The Path to General-Purpose Robots - Coatue
 
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and DisadvantagesBLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
BLOCKCHAIN TECHNOLOGY - Advantages and Disadvantages
 
Discovery Series - Zero to Hero - Task Mining Session 1
Discovery Series - Zero to Hero - Task Mining Session 1Discovery Series - Zero to Hero - Task Mining Session 1
Discovery Series - Zero to Hero - Task Mining Session 1
 
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
UX Webinar Series: Drive Revenue and Decrease Costs with Passkeys for Consume...
 
Intel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdfIntel Unveils Core Ultra 200V Lunar chip .pdf
Intel Unveils Core Ultra 200V Lunar chip .pdf
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 

Utilization Data Mining to Detect Spyware

  • 1. IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661 Volume 4, Issue 3 (Sep.-Oct. 2012), PP 01-04 www.iosrjournals.org www.iosrjournals.org 1 | Page Utilization Data Mining to Detect Spyware 1 Parisa Bahraminikoo, 2 Mehdi Samiei yeganeh, 3 G.Praveen Babu 1, 2 (M.Tech.(S/w. Eng.), 3 (Associate Professor School of Information Technology, Jawaharlal Nehru Technological University, Iran) Abstract: Malicious software (malware) is any software that gives partial to full control of your computer to do whatever the malware creator wants. Malware can be a virus, worm, Trojan, adware, spyware, root kit, etc.Spyware is a type of malware (malicious software) installed on computers that collects information about users without their knowledge. In the year 1956, Artificial Intelligence (AI) was established at Dartmuth College during a conference. The technology developed so much that it started involving many other branches of engineering such as electronics, robotics etc. This eventually led to much more complex and smart machinery involving Artificial Intelligence. With the development of malware detection systems and Artificial Intelligence, as a new technology for them, Artificial Intelligence has been applied in anti-virus engines. There are several AI approaches that applied in spyware detection systems such as Artificial Neural Networks, Heuristic Technology and Data Mining (DM) Technique. Heuristic-based Detection performs well against known Spyware but has not been proven to be successful at detecting new spyware. In this paper we focus on DM-based malicious code detectors using Breadth-First Search (BFS) approach, which are known to work well for detecting viruses and similar software. BFS is a strategy for searching in a tree when search is limited to essentially two operations: (a) visit and inspect a node of a tree; (b) gain access to visit the nodes that are neighbor to currently visited node. The BFS begins at a root node and inspect all the neighboring nodes. Then for each of those neighbor nodes in turn, it inspects their neighbor nodes which were unvisited, and so on. Keywords: Spyware, Artificial intelligence, Data mining, Breadth-First Search. I. Introduction As the application of computer and Internet is more popular, it provides a convenient way to share the information among different people; however it also gives chances to malware activities, such as propagating malicious programs, including computer viruses [1]. Programs that have the potential to break the privacy and security of a system can be labeled as Privacy Invasive Software [2]. These programs include: spyware, adware, Trojans, greyware and backdoors [3]. Spyware is a type of malware (malicious software) installed on computers that collects information about users without their knowledge. The presence of spyware is typically hidden from the user and can be difficult to detect. Some spyware, such as keyloggers, may be installed by the owner of a shared, corporate, or public computer intentionally in order to monitor users. While the term spyware suggests software that monitors a user's computing, the functions of spyware can extend beyond simple monitoring. Spyware can collect almost any type of data, including personal information like Internet surfing habits, user logins, and bank or credit account information. Spyware can also interfere with user control of a computer by installing additional software or redirecting Web browsers. Some spyware can change computer settings, which can result in slow Internet connection speeds, un-authorized changes in browser settings, or changes to software settings. The goal of spyware is generally not to cause damage or to spread to other systems. Instead, spyware programs monitor the behavior of users and steal private information, such as keystrokes and browsing patterns. This information is then sent back to the spyware distributors and used as a basis for targeted advertisement (e.g., pop-up ads) or marketing analysis [4]. AI is the science and engineering of making intelligent machines, especially intelligent computer programs [1]. AI is set to play an important role in our lives. Researchers produce new products which duplicate intelligence, understand speech, beat the opponent chess player, and acting in complex conditions. The major problems of Artificial Intelligence include qualities such as knowledge, planning, learning, reasoning, communication, perception and capability to move and control the objects [6].The aim of Artificial Intelligence is to develop the machines to perform the tasks in a better way than the humans [5]. As following we will describe the main application of artificial intelligence that is applied in spyware detection systems. The rest of this paper is organized as follows: section 2 briefly describes the Heuristic Technology, Section 3 explains Data mining Technique and section 4 briefly describes the Neural Network Technology.
  • 2. Utilization Data Mining to Detect Spyware www.iosrjournals.org 2 | Page II. Heuristic Technology At the present, the first and main application for spam filtering technique based on artificial intelligence is Heuristic Technology. Current anti-spyware tools make use of signature-based methods by using specific features or unique strings extracted from binaries or heuristic-based methods by using on the basis of rules written by experts who define behavioral patterns as approaches against spyware. These approaches are often considered ineffective against new malicious code [7, 8]. Heuristic Technology means "the ability of self-discovery" or "the knowledge and skills that use some methods to determine", and intelligently analyze codes to detect the unknown virus by some rules while scanning [9].Heuristics are used quite often in Artificial Intelligence based research. They are new and constantly being refined by most antivirus companies over the last five years or so. Computational they are much faster than signature based techniques. Heuristics look for a set of characteristics within a file in order to determine whether or not it may be a potential threat. In a sense, heuristic anti-malware attempts to apply the processes of human analysis to an object. In the same way that a human malware analyst would try to determine the process of a given program and its actions, heuristic analysis performs the same intelligent decision-making process, effectively acting as a virtual malware researcher. As the human malware analyst learns more from and about emerging threats he or she can apply that knowledge to the heuristic analyzer through programming, and improve future detection rates. Antivirus software may use one or several techniques to proactively detect malware. The main essence of each method is to analyze the suspicious file’s characteristics and behavior to determine if it is indeed malware. The main concern with heuristic detection is that it often increases false positives. False positives are when the antivirus software determines a file is malicious (and quarantines or deletes it) when in reality it is perfectly fine and/or desired. Because some files may look like viruses but really aren’t, they are restricted and stopped from working on your computer. In Heuristics based detection we can use Generic Signature, This technique is particularly designed to locate variations of viruses. Several viruses are re-created and make themselves known by a variety of names, but essentially come from the same family (or classification). Genetic detection uses previous antivirus definitions to locate these similar “cousins” even if they use a slightly different name or include some unusual characters. The best way to illustrate this idea is with identical twins. They may have slightly different fingerprints, but their DNA is identical. III. Neural Network Technology An Artificial Neural Network (ANN) (Bishop, 1995) is an information processing paradigm that is inspired by the way biological nervous systems (i.e., the brain) are modeled with regard to information processing [13]. A neural network is designed to simulate a set of neurons, usually connected by synapses. In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another cell. Just as in biological systems, learning involves adjustments to the synaptic connections that exist between the neurons. Neural networks can differ on: the way their neurons are connected; the specific kinds of computations their neurons do; the way they transmit patterns of activity throughout the network; and the way they learn including their learning rate. Neural networks are being applied to an increasing large number of real world problems [14]. The neural network is configured for a specific application, such as data classification or pattern recognition, through a learning process called training [15]. In [16] has introduced how to use Single layer neural classifier to detection boot viruses, and the generic virus detector was incorporated into IBM Antivirus in May, 1994.Its structure has been shown in Fig 1. Figure 1: Single layer neural classifier William Arnold and Gerald Tesauro constructed multiple neural network classifiers which can detect unknown Win32 viruses by combining the individual classifier outputs using a voting procedure, following a technique described in previous work (Kephart et al, 1995) on boot virus heuristics [17]. And the system has 508 achieved effectively.Authors of [18] presented a new rule generation method from neural networks formed using a genetic algorithm (GA) with virus infection and deterministic mutation. This method can extract rules (regularities) for a pattern classification and chaotic system identification by using the same system [1].
  • 3. Utilization Data Mining to Detect Spyware www.iosrjournals.org 3 | Page IV. Data mining Technique With the rapid development of Information Technology, the rapid growth of data has exceeded the ability of the manual processing of data. So how to help people to extract the general knowledge from the mass of data has become more and more important. In order to implement it, data mining technique is put forward and soon becomes an active research direction. Data mining analyzes the observed sets to discover the unknown relation and sum up the results of data analysis to make the owner of data to understand. Data Mining Algorithm that is from Statistics, Pattern Identification, Machine Learning [5, 11], and Database and so on, has developed comprehensive [1]. In [10] presented a data-mining framework that detects new, previously unseen malicious executables accurately and automatically. The 2001 data mining study of malicious code [8] used. Three types of features, i.e., Dynamic-link Library resource information, consecutive printable characters (strings) and byte sequences.In [12] presented a network virus precaution system based on data mining shown in Fig 2.It can detect the abnormal connecting behavior of network in real-time to discover the trace of worm virus, especially the precaution action to the new worm virus to make administrator to adopt corresponding measure to avoid tremendous loss. Figure 2: The structure of warning system Data mining techniques perform better than traditional techniques such as signature-base detection and Heuristic-based detection. The focus of our analysis is executable files for the Windows platform. We use the Waikato Environment for Knowledge Analysis (Weka) to perform the experiments. Weka is a suite of machine learning algorithms and analysis tools, which is used in practice for solving data mining problems, first, we extract features from the binary files We extract the features by using the Common Feature-based Extraction (CFBE). The purpose of employing this approach is to evaluate two different techniques that use different types of data representation, i.e., the occurrence of a feature and the frequency of a feature. CFBE method are used to obtain Reduced Feature Sets (RFSs) which are then used to generate the ARFF files and includes instances from the frequency range of 50-80. And we then apply a feature reduction method in order to reduce data set complexity. In experiments for the detection of malware, sequences of bytes extracted from the hexadecimal dump of the binary files have been represented by n-grams [3]. Finally, we convert the reduced feature set into the Attribute-Relation File Format (ARFF). ARFF files are ASCII text files that include a set of data instances, each described by a set of features [3]. Data mining base malicious approach have proven to be successful in detecting viruses and worms. Overall accuracy of 90.5% is achieved with the BF-tree algorithms. We evaluate each learning algorithm by performing cross-validation tests to ensure that the generated classifiers are not tested on the training data. From the response of the classifiers the relevant confusion matrices were created. Four metrics define the elements of the matrix: True Positives (TP), False Positives (FP), True Negatives (TN) and False Negatives (FN). Metric Abbreviation Meaning True Positives TP Number of correctly identified benign programs. False Positives FP Number of wrongly identified Spyware programs. True Negatives TN Number of correctly identified Spyware programs. False Negatives FN Number of wrongly identified benign programs. Table 1 Evaluation metrics We shall now demonstrate how multi-criteria metrics can be used as an approach to trade-off some of the important aspects of EULA (End User License Agreement) classification [19]. The performance of each classifier was evaluated using the true positive rate, false positive rate and overall accuracy which are defined as follows: True Positive Rate (TPR): Percentage of correctly identified benign programs (TP / TP+FN) False Positive Rate (FPR): Percentage of wrongly identified Spyware programs (FP / TN+FP) Overall Accuracy (ACC): Percentage of correctly identified programs (TP+TN / TP+TN+FP+FN)
  • 4. Utilization Data Mining to Detect Spyware www.iosrjournals.org 4 | Page Table 2 show that Using the feature set produced by the CFBE feature selection method for n = 4, the BFT decision tree classifier achieves the highest accuracy results in Frequency Range 50-80. Table 2 Comparison of Algorithms for N-gram size = 4 V. Conclusion Spyware technique has become the most important Prevention technique. With this technologies, the system can detect virus invasion in real-time, and enlarge security management capacity of system administrators to enhance the integrity of the infrastructure of information security. With development of Artificial Intelligence technology has provided new methods and ideas for spyware detection system. Intergraded spyware detection with AI will greatly improve the performance of the existing spyware detection system, promote more effective artificial intelligence algorithms to be proposed, and be applied in the popular detection field .The main objective of the present work is to establish a method in Spyware detection research using data mining techniques. These techniques are used for information retrieval and classification. Data mining-based malicious code detectors have been proven to be successful in detecting clearly malicious code, e.g., like viruses and worms. Results from different studies have indicated that data mining techniques perform better than traditional techniques against malicious code. However, spyware has not received the same attention from researchers but it is spreading rapidly on both home and business computers. Overall accuracy of 90.5% is achieved with the BF-tree algorithms. References [1]. Review on the application of Artificial Intelligence in Antivirus Detection System”, Xiao-bin Wang Guang-yuan Yang Yi-chao Li Dan Liu. [2]. M. Boldt and B. Carlsson, “Privacy-invasive software and preventive mechanisms,” 2nd International Conference on Systems and Networks Communications, (ICSNC 2006), Oct. 28- Nov.2, IEEE Computer Society. [3]. Detection of Spyware by Mining Executable Files” Raja Khurram Shazhad, Syed Imran Haider, Niklas Lavesson, 2010International Conference onAvailability,Reliability and Security. [4]. Behavior-based Spyware Detection” Engin Kirda and Christopher Kruegel, Greg Banks, Giovanni Vigna, and Richard A. Kemmerer. [5]. Review on use of Reinforcement Learning in Artificial Intelligence” Mehdi Samieiyeganeh , Parisa Bahraminikoo ,G.Praveen Babu ,12th International Conference of Science and Technology Impact on Development and Justice held at Maulana Azad National Urdu University, Hyderabad, India, on 7th & 8th February, 2012. [6]. Chuck Williams. “A BRIEF INTRODUCTION TO ARTIFICIAL INTELLIGENCE”, 10.0 109 83 IEEE. [7]. C. D. Bozagac, “Application of Data Mining based Malicious Code Detection Techniques for Detecting new Spyware”, White paper, Bilkent University, 2005. [8]. M. Schultz, E. Eskin, F. Zadok, and S. Stolfo, “Data mining methods for detection of new malicious executables,” Proceedings of IEEE Symposium on Security and Privacy, 14-16 May 2001, Los Alamitos, [9]. Xianwei Zeng, Zhijun Zhang, and Zhi Zhang, “Heuristic skill of computer virus analysis based on virtual machine,” Computer Applications and Software, Vol. 22(9), 2005, pp. 125-126. [10]. Matthew G. Schultz, Eleazar Eskin, Erez Zadok, and Salvatore I. Stolfo, “Data Mining Methods for Detection of New Malicious Executables,” The 2001 IEEE Symposium on Security and Privacy, Oakland. CA, 2001, pp.38-49. [11]. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan. [12]. Yufeng Yang, “The Network Virus Precaution System Based on Data Mining,” Journal of Shaoguan University, Vol. 26(12), 2005, pp. 31-33. [13]. Detection of Unknown Computer Worms based on Behavioral Classification of the Host”, Robert Moskovitch, Yuval Elovici, Lior Rokach. [14]. Review on Artificial Intelligence and Artificial neural networks” Mehdi Samieiyeganeh , ParisaBahraminikoo,G.PraveenBabu, International Conference on Computing,Communications, Systems & Aeronautics (ICCCSA-12) , Organized by Malla Reddy College of Engineering & Technology From March 30-31,2012. Hyderabad, India. [15]. Artificial Intelligence: Neural Networks Simplified “Indranarain Ramlall1 University of Technology, Mauritius. [16]. Kephart, J.O., "Biologically inspired defenses against computer viruses," Proceedings of International Joint Conference on Artificial Intelligence, 1995, pp. 985-96. [17]. R. Moskovitch, C. Feher, N. Tzachar, E. Berger, M. Gitelman, S. Dolev, and Y. Elovici, “Unknown malcode detection using OPCODE representation,” 1st European Conference on Intelligence and Security Informatics, (EuroISI 2008), 3-5 Dec., Berlin, Germany: Springer-Verlag, pp. 204-215. [18]. Y. Elovici, A. Shabtai, R. Moskovitch, G. Tahan, and C. Glezer., “Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic”, Proceedings of the 30th annual German conference on Advances in Artificial Intelligence, KI 2007, 10-13. [19]. Niklas Lavesson , Martin Boldt , Paul Davidsson ,Andreas Jacobsson, “Learning to detect spyware using end user license agreements”, Springer-Verlag London Limited 2009. Algorithm Type TPR FPR ACC BFT Frequency Range 50-80 Trees 0.992 0.977 0.731 0.730 89.896 (5.104) 88.5222 (5.899) Random Forest Frequency Range 50-80 Trees 0.979 0.960 0.665 0.720 89.489 (5.520) 87.077 (6.477) Naive Bayes Frequency Range 50-80 Bayes 0.973 0.916 0.730 0.705 88.209 (6.174) 83.703 (8.202) SMO Frequency Range 50-80 Function 0.935 0.946 0.665 0.515 86.566 (8.130) 88.5222 (7.530)