Data Security has become a very critical part of any
organizational information system. Intrusion Detection
System (IDS) is used as a security measure to preserve data
integrity and system availability from various attacks. This
paper evaluates the performance of C4.5 classifier and its
combination using bagging, boosting and stacking over NSLKDD
dataset for IDS. This dataset set consists of selected
records of the complete KDD dataset.
Intrusion Detection Using Conditional Random FieldsIDES Editor
Intrusion detection systems have become a key
component in ensuring the safety of systems and networks.
This paper introduces the probabilistic approach called
Conditional Random Fields (CRF) for detecting network based
intrusions. In this paper, we have shown results for the issue
of accuracy using CRFs. It is demonstrated that high attack
detection accuracy can be achieved by using Conditional
Random Fields. Experimental results on the benchmark
KDD’99 intrusion data set show the importance of proposed
system. The improvement in attack detection accuracy is very
high for Probe, Denial of Service, U2R and R2L attacks.
Current keystroke dynamics applications have tackled the problem of traditional knowledge-based static password verification, but the problem of spontaneous
password verification persists. The intent of this study was to examine the predictive strength of typing patterns for spontaneous passwords. The typing patterns of an individual typing at a DELL® keyboard on a DELL OptiPlex® GX260 machine were recorded. Variables collected included keystroke press time and keystroke latency. Computed performance measures included false match rates (FMR) and false non match rates (FNMR) at various threshold levels.
Algorithms for extraction and visualization of
metadata from Domain Name Server records -- 2010 Third International Conference on Advances in Mesh Networks
Intrusion Detection Using Conditional Random FieldsIDES Editor
Intrusion detection systems have become a key
component in ensuring the safety of systems and networks.
This paper introduces the probabilistic approach called
Conditional Random Fields (CRF) for detecting network based
intrusions. In this paper, we have shown results for the issue
of accuracy using CRFs. It is demonstrated that high attack
detection accuracy can be achieved by using Conditional
Random Fields. Experimental results on the benchmark
KDD’99 intrusion data set show the importance of proposed
system. The improvement in attack detection accuracy is very
high for Probe, Denial of Service, U2R and R2L attacks.
Current keystroke dynamics applications have tackled the problem of traditional knowledge-based static password verification, but the problem of spontaneous
password verification persists. The intent of this study was to examine the predictive strength of typing patterns for spontaneous passwords. The typing patterns of an individual typing at a DELL® keyboard on a DELL OptiPlex® GX260 machine were recorded. Variables collected included keystroke press time and keystroke latency. Computed performance measures included false match rates (FMR) and false non match rates (FNMR) at various threshold levels.
Algorithms for extraction and visualization of
metadata from Domain Name Server records -- 2010 Third International Conference on Advances in Mesh Networks
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
Identification of Causal Variables for Building Energy Fault Detection by Sem...Keigo Yoshida
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
A Survey on DPI Techniques for Regular Expression Detection in Network Intrus...ijsrd.com
Deep Packet Inspection (DPI) is becoming more widely used in virtually all applications or services like Intrusion Detection System (IDS), which operate with or within a network. DPI analyzes all data present in the packet as it passes an inspection to determine the application transported and protocol. Deep packet inspection typically uses regular expression matching as a core operator. Regular expressions (RegExes) are used to flexibly represent complex string patterns in many applications ranging from network intrusion detection and prevention systems (NIDPSs). Regular expressions represent complex string pattern as attack signatures in DPI. It examine whether a packet’s payload matches any of a set of predefined regular expressions. There are various techniques developed in DPI for deep packet inspection for regular expression. We survey on these techniques for further improvement in regular expression detection in this paper. In the result we found that it is possible to reduce RegEx transaction memory required in network intrusion detection. We made this survey with possible use of DPI techniques in the wireless network.
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Real Time Speaker Identification System – Design, Implementation and ValidationIDES Editor
This paper presents design, implementation and
validation of a PC based Prototype speaker recognition and
verification system. This system is organized to receive speech
signal, find the features of speech signal, and recognize and
verify a person using voice as the biometric. The system is
implemented to capture the speech signal from microphone
and to compare it with the stored data base using filter-bank
based closed-set speaker verification system. At first, the
identification of the voice signals is done using an algorithm
developed in MATLAB. Next, a PC based prototype system is
developed and is validated in real time. Several tests were made
on different sets of voice signals, and measured the performance
and the speed of the proposed system in real environment. The
result confirmed the use of proposed system for various real
time applications.
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
A Novel Steganography Technique that Embeds Security along with CompressionCSCJournals
Problem faced by today’s communicators is not only security but also the speed of communication. This paper provides a mechanism that increases the speed of communication by reducing the size of content; for this data compression method is used and security factor is added by using Steganography. Firstly, the focus has been made on Data Compression and Steganography. Finally, proposed technique has been discussed. In Proposed technique first data is compressed to reduce the size of the data and increase the data transfer rate. Thereafter on compressed data state table operation is applied to improve the security. Then, this is used as the input to the LSB technique of Steganography. At receiver end, firstly the LSB extraction technique is used, thereafter the state table operation in reverse form is applied and finally the original data is obtained. Hence our proposed technique is effective that can reduce data size, increase data transfer rate and provide the security during communication.
The importance of learning how patients feel and function when taking a new clinical therapy has been acknowledged by the FDA, EMA and other global regulatory authorities. Sponsors currently engaged in drug development programs appreciate and leverage the added value of patient-reported outcome (PRO) data. They no longer ask if PROs should be collected, but what phase to begin PRO collection.
This issue of Insights is intended to identify the costs (delays and expenses) of collecting patient-reported outcomes on paper; and compare these against electronic PRO capture. The intention of this issue to provide clinical teams with industry data that can refute the presumption that paper methods are cheaper than ePRO.
IJCER (www.ijceronline.com) International Journal of computational Engineeri...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Design and Implementation of Intrusion Detection and Prevention by Applying D...VAIBHAVSHARMA94659
Technology is continuously evolving and peoples are constantly using it on the large scale, creating a lot of problem for security related context and everything is digital and might be hacked, so it have to be prevented for unauthorized gain and Access . As online world Increase everyone is doing purchasing and selling online by using credit cards or debit cards due to which Information will pass through multiple end points and will have the probability to be get breached and to be trapped by some faulty personnel which will be used further ward for the fault Intensions, cybercrime have been increased to the very high scale in the present scenario’s As our study in [2] among all the cyber security Attacks DDos have been used up to the extent of about 40% approx. among all. These Attacks are dangerous for both the organization related information or individual information of any personnel, while on the other side to prevent for cyber security loss, Intelligent cyber prevention (ICP) [1] [3] system contains multiple layers of security containing authentications at Server Level , Network level, desktop or system level security, Antivirus level also.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
CLASSIFIER SELECTION MODELS FOR INTRUSION DETECTION SYSTEM (IDS)ieijjournal1
Any abnormal activity can be assumed to be anomalies intrusion. In the literature several techniques and
algorithms have been discussed for anomaly detection. In the most of cases true positive and false positive
parameters have been used to compare their performance. However, depending upon the application a
wrong true positive or wrong false positive may have severe detrimental effects. This necessitates inclusion
of cost sensitive parameters in the performance. Moreover the most common testing dataset KDD-CUP-99
has huge size of data which intern require certain amount of pre-processing. Our work in this paper starts
with enumerating the necessity of cost sensitive analysis with some real life examples. After discussing
KDD-CUP-99 an approach is proposed for feature elimination and then features selection to reduce the
number of more relevant features directly and size of KDD-CUP-99 indirectly. From the reported
literature general methods for anomaly detection are selected which perform best for different types of
attacks. These different classifiers are clubbed to form an ensemble. A cost opportunistic technique is
suggested to allocate the relative weights to classifiers ensemble for generating the final result. The cost
sensitivity of true positive and false positive results is done and a method is proposed to select the elements
of cost sensitivity metrics for further improving the results to achieve the overall better performance. The
impact on performance trade of due to incorporating the cost sensitivity is discussed.
NETWORK INTRUSION DATASETS USED IN NETWORK SECURITY EDUCATIONIJITE
There is a gap between the network security graduate and the professional life. In this paper we discussed the different types of network intrusion dataset and then we highlighted the fact that any student can easily create a network intrusion dataset that is representative of the network they are in. Intrusions can be in form of anomaly or network signature; the students cannot grasp all types but they have to have the ability to detect malicious packets within his network.
Quality and Distortion Evaluation of Audio Signal by SpectrumCSCJournals
Information hiding in digital audio can be used for such diverse applications as proof of ownership, authentication, integrity, secret communication, broadcast monitoring and event annotation. To achieve secure and undetectable communication, stegano-objects, and documents containing a secret message, should be indistinguishable from cover-objects, and show that documents not containing any secret message. In this respect, Steganalysis is the set of techniques that aim to distinguish between cover-objects and stegano-objects [1]. A cover audio object can be converted into a stegano-audio object via steganographic methods. In this paper we present statistical method to detect the presence of hidden messages in audio signals. The basic idea is that, the distribution of various statistical distance measures, calculated on cover audio signals and on stegano-audio signals vis-à-vis their de-noised versions, are statistically different. A distortion metric based on Signal spectrum was designed specifically to detect modifications and additions to audio media. We used the Signal spectrum to measure the distortion. The distortion measurement was obtained at various wavelet decomposition levels from which we derived high-order statistics as features for a classifier to determine the presence of hidden information in an audio signal. This paper looking at evidence in a criminal case probably has no reason to alter any evidence files. However, it is part of an ongoing terrorist surveillance might well want to disrupt the hidden information, even if it cannot be recovered
Identification of Causal Variables for Building Energy Fault Detection by Sem...Keigo Yoshida
K. Yoshida, M. Inui, T. Yairi, K. Machida, M. Shioya, and Y. Masukawa, "Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis", in Proc. ICDM Workshops, 2008, pp.164-173.
A Survey on DPI Techniques for Regular Expression Detection in Network Intrus...ijsrd.com
Deep Packet Inspection (DPI) is becoming more widely used in virtually all applications or services like Intrusion Detection System (IDS), which operate with or within a network. DPI analyzes all data present in the packet as it passes an inspection to determine the application transported and protocol. Deep packet inspection typically uses regular expression matching as a core operator. Regular expressions (RegExes) are used to flexibly represent complex string patterns in many applications ranging from network intrusion detection and prevention systems (NIDPSs). Regular expressions represent complex string pattern as attack signatures in DPI. It examine whether a packet’s payload matches any of a set of predefined regular expressions. There are various techniques developed in DPI for deep packet inspection for regular expression. We survey on these techniques for further improvement in regular expression detection in this paper. In the result we found that it is possible to reduce RegEx transaction memory required in network intrusion detection. We made this survey with possible use of DPI techniques in the wireless network.
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Real Time Speaker Identification System – Design, Implementation and ValidationIDES Editor
This paper presents design, implementation and
validation of a PC based Prototype speaker recognition and
verification system. This system is organized to receive speech
signal, find the features of speech signal, and recognize and
verify a person using voice as the biometric. The system is
implemented to capture the speech signal from microphone
and to compare it with the stored data base using filter-bank
based closed-set speaker verification system. At first, the
identification of the voice signals is done using an algorithm
developed in MATLAB. Next, a PC based prototype system is
developed and is validated in real time. Several tests were made
on different sets of voice signals, and measured the performance
and the speed of the proposed system in real environment. The
result confirmed the use of proposed system for various real
time applications.
Marathi Isolated Word Recognition System using MFCC and DTW FeaturesIDES Editor
This paper presents a Marathi database and isolated
Word recognition system based on Mel-frequency cepstral
coefficient (MFCC), and Distance Time Warping (DTW) as
features. For the extraction of the feature, Marathi speech
database has been designed by using the Computerized Speech
Lab. The database consists of the Marathi vowels, isolated
words starting with each vowels and simple Marathi sentences.
Each word has been repeated three times by the 35 speakers.
This paper presents the comparative recognition accuracy of
DTW and MFCC.
A Novel Steganography Technique that Embeds Security along with CompressionCSCJournals
Problem faced by today’s communicators is not only security but also the speed of communication. This paper provides a mechanism that increases the speed of communication by reducing the size of content; for this data compression method is used and security factor is added by using Steganography. Firstly, the focus has been made on Data Compression and Steganography. Finally, proposed technique has been discussed. In Proposed technique first data is compressed to reduce the size of the data and increase the data transfer rate. Thereafter on compressed data state table operation is applied to improve the security. Then, this is used as the input to the LSB technique of Steganography. At receiver end, firstly the LSB extraction technique is used, thereafter the state table operation in reverse form is applied and finally the original data is obtained. Hence our proposed technique is effective that can reduce data size, increase data transfer rate and provide the security during communication.
The importance of learning how patients feel and function when taking a new clinical therapy has been acknowledged by the FDA, EMA and other global regulatory authorities. Sponsors currently engaged in drug development programs appreciate and leverage the added value of patient-reported outcome (PRO) data. They no longer ask if PROs should be collected, but what phase to begin PRO collection.
This issue of Insights is intended to identify the costs (delays and expenses) of collecting patient-reported outcomes on paper; and compare these against electronic PRO capture. The intention of this issue to provide clinical teams with industry data that can refute the presumption that paper methods are cheaper than ePRO.
IJCER (www.ijceronline.com) International Journal of computational Engineeri...ijceronline
Call for paper 2012, hard copy of Certificate, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJCER, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, research and review articles, IJCER Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathematics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer review journal, indexed journal, research and review articles, engineering journal, www.ijceronline.com, research journals,
yahoo journals, bing journals, International Journal of Computational Engineering Research, Google journals, hard copy of Certificate,
journal of engineering, online Submission
Design and Implementation of Intrusion Detection and Prevention by Applying D...VAIBHAVSHARMA94659
Technology is continuously evolving and peoples are constantly using it on the large scale, creating a lot of problem for security related context and everything is digital and might be hacked, so it have to be prevented for unauthorized gain and Access . As online world Increase everyone is doing purchasing and selling online by using credit cards or debit cards due to which Information will pass through multiple end points and will have the probability to be get breached and to be trapped by some faulty personnel which will be used further ward for the fault Intensions, cybercrime have been increased to the very high scale in the present scenario’s As our study in [2] among all the cyber security Attacks DDos have been used up to the extent of about 40% approx. among all. These Attacks are dangerous for both the organization related information or individual information of any personnel, while on the other side to prevent for cyber security loss, Intelligent cyber prevention (ICP) [1] [3] system contains multiple layers of security containing authentications at Server Level , Network level, desktop or system level security, Antivirus level also.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
CLASSIFIER SELECTION MODELS FOR INTRUSION DETECTION SYSTEM (IDS)ieijjournal1
Any abnormal activity can be assumed to be anomalies intrusion. In the literature several techniques and
algorithms have been discussed for anomaly detection. In the most of cases true positive and false positive
parameters have been used to compare their performance. However, depending upon the application a
wrong true positive or wrong false positive may have severe detrimental effects. This necessitates inclusion
of cost sensitive parameters in the performance. Moreover the most common testing dataset KDD-CUP-99
has huge size of data which intern require certain amount of pre-processing. Our work in this paper starts
with enumerating the necessity of cost sensitive analysis with some real life examples. After discussing
KDD-CUP-99 an approach is proposed for feature elimination and then features selection to reduce the
number of more relevant features directly and size of KDD-CUP-99 indirectly. From the reported
literature general methods for anomaly detection are selected which perform best for different types of
attacks. These different classifiers are clubbed to form an ensemble. A cost opportunistic technique is
suggested to allocate the relative weights to classifiers ensemble for generating the final result. The cost
sensitivity of true positive and false positive results is done and a method is proposed to select the elements
of cost sensitivity metrics for further improving the results to achieve the overall better performance. The
impact on performance trade of due to incorporating the cost sensitivity is discussed.
NETWORK INTRUSION DATASETS USED IN NETWORK SECURITY EDUCATIONIJITE
There is a gap between the network security graduate and the professional life. In this paper we discussed the different types of network intrusion dataset and then we highlighted the fact that any student can easily create a network intrusion dataset that is representative of the network they are in. Intrusions can be in form of anomaly or network signature; the students cannot grasp all types but they have to have the ability to detect malicious packets within his network.
Network Intrusion Datasets Used In Network Security EducationIJITE
There is a gap between the network security graduate and the professional life. In this paper we discussed
the different types of network intrusion dataset and then we highlighted the fact that any student can easily
create a network intrusion dataset that is representative of the network they are in. Intrusions can be in
form of anomaly or network signature; the students cannot grasp all types but they have to have the ability
to detect malicious packets within his network.
Current issues - International Journal of Network Security & Its Applications...IJNSA Journal
nternational Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBE...IJNSA Journal
In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparison study of machine learning classifiers to detect anomalies IJECEIAES
In this era of Internet ensuring the confidentiality, authentication and integrity of any resource exchanged over the net is the imperative. Presence of intrusion prevention techniques like strong password, firewalls etc. are not sufficient to monitor such voluminous network traffic as they can be breached easily. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database.Thus, the need for real-time detection of aberrations is observed. Existing signature based detection techniques like antivirus only offers protection against known attacks whose signatures are stored in the database. Machine learning classifiers are implemented here to learn how the values of various fields like source bytes, destination bytes etc. in a network packet decides if the packet is compromised or not . Finally the accuracy of their detection is compared to choose the best suited classifier for this purpose. The outcome thus produced may be useful to offer real time detection while exchanging sensitive information such as credit card details.
Power System State Estimation - A ReviewIDES Editor
The aim of this article is to provide a comprehensive
survey on power system state estimation techniques. The
algorithms used for finding the system states under both static
and dynamic state estimations are discussed in brief. The
authors are opinion that the scope of pursuing research in the
area of state estimation with PMU and SCADA measurements
is the state of the art and timely.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
Reactive Power Planning is a major concern in the
operation and control of power systems This paper compares
the effectiveness of Evolutionary Programming (EP) and
New Improved Differential Evolution (NIMDE) to solve
Reactive Power Planning (RPP) problem incorporating
FACTS Controllers like Static VAR Compensator (SVC),
Thyristor Controlled Series Capacitor (TCSC) and Unified
power flow controller (UPFC) considering voltage stability.
With help of Fast Voltage Stability Index (FVSI), the critical
lines and buses are identified to install the FACTS controllers.
The optimal settings of the control variables of the generator
voltages,transformer tap settings and allocation and parameter
settings of the SVC,TCSC,UPFC are considered for reactive
power planning. The test and Validation of the proposed
algorithm are conducted on IEEE 30–bus system and 72-bus
Indian system.Simulation results shows that the UPFC gives
better results than SVC and TCSC and the FACTS controllers
reduce the system losses.
Design and Performance Analysis of Genetic based PID-PSS with SVC in a Multi-...IDES Editor
Damping of power system oscillations with the help
of proposed optimal Proportional Integral Derivative Power
System Stabilizer (PID-PSS) and Static Var Compensator
(SVC)-based controllers are thoroughly investigated in this
paper. This study presents robust tuning of PID-PSS and
SVC-based controllers using Genetic Algorithms (GA) in
multi machine power systems by considering detailed model
of the generators (model 1.1). The effectiveness of FACTSbased
controllers in general and SVC-based controller in
particular depends upon their proper location. Modal
controllability and observability are used to locate SVC–based
controller. The performance of the proposed controllers is
compared with conventional lead-lag power system stabilizer
(CPSS) and demonstrated on 10 machines, 39 bus New England
test system. Simulation studies show that the proposed genetic
based PID-PSS with SVC based controller provides better
performance.
Optimal Placement of DG for Loss Reduction and Voltage Sag Mitigation in Radi...IDES Editor
This paper presents the need to operate the power
system economically and with optimum levels of voltages has
further led to an increase in interest in Distributed
Generation. In order to reduce the power losses and to improve
the voltage in the distribution system, distributed generators
(DGs) are connected to load bus. To reduce the total power
losses in the system, the most important process is to identify
the proper location for fixing and sizing of DGs. It presents a
new methodology using a new population based meta heuristic
approach namely Artificial Bee Colony algorithm(ABC) for
the placement of Distributed Generators(DG) in the radial
distribution systems to reduce the real power losses and to
improve the voltage profile, voltage sag mitigation. The power
loss reduction is important factor for utility companies because
it is directly proportional to the company benefits in a
competitive electricity market, while reaching the better power
quality standards is too important as it has vital effect on
customer orientation. In this paper an ABC algorithm is
developed to gain these goals all together. In order to evaluate
sag mitigation capability of the proposed algorithm, voltage
in voltage sensitive buses is investigated. An existing 20KV
network has been chosen as test network and results are
compared with the proposed method in the radial distribution
system.
Line Losses in the 14-Bus Power System Network using UPFCIDES Editor
Controlling power flow in modern power systems
can be made more flexible by the use of recent developments
in power electronic and computing control technology. The
Unified Power Flow Controller (UPFC) is a Flexible AC
transmission system (FACTS) device that can control all the
three system variables namely line reactance, magnitude and
phase angle difference of voltage across the line. The UPFC
provides a promising means to control power flow in modern
power systems. Essentially the performance depends on proper
control setting achievable through a power flow analysis
program. This paper presents a reliable method to meet the
requirements by developing a Newton-Raphson based load
flow calculation through which control settings of UPFC can
be determined for the pre-specified power flow between the
lines. The proposed method keeps Newton-Raphson Load Flow
(NRLF) algorithm intact and needs (little modification in the
Jacobian matrix). A MATLAB program has been developed to
calculate the control settings of UPFC and the power flow
between the lines after the load flow is converged. Case studies
have been performed on IEEE 5-bus system and 14-bus system
to show that the proposed method is effective. These studies
indicate that the method maintains the basic NRLF properties
such as fast computational speed, high degree of accuracy and
good convergence rate.
Study of Structural Behaviour of Gravity Dam with Various Features of Gallery...IDES Editor
The size and shape of opening in dam causes the
stress concentration, it also causes the stress variation in the
rest of the dam cross section. The gravity method of the analysis
does not consider the size of opening and the elastic property
of dam material. Thus the objective of study is comprises of
the Finite Element Method which considers the size of
opening, elastic property of material, and stress distribution
because of geometric discontinuity in cross section of dam.
Stress concentration inside the dam increases with the opening
in dam which results in the failure of dam. Hence it is
necessary to analyses large opening inside the dam. By making
the percentage area of opening constant and varying size and
shape of opening the analysis is carried out. For this purpose
a section of Koyna Dam is considered. Dam is defined as a
plane strain element in FEM, based on geometry and loading
condition. Thus this available information specified our path
of approach to carry out 2D plane strain analysis. The results
obtained are then compared mutually to get most efficient
way of providing large opening in the gravity dam.
Assessing Uncertainty of Pushover Analysis to Geometric ModelingIDES Editor
Pushover Analysis a popular tool for seismic
performance evaluation of existing and new structures and is
nonlinear Static procedure where in monotonically increasing
loads are applied to the structure till the structure is unable
to resist the further load .During the analysis, whatever the
strength of concrete and steel is adopted for analysis of
structure may not be the same when real structure is
constructed and the pushover analysis results are very sensitive
to material model adopted, geometric model adopted, location
of plastic hinges and in general to procedure followed by the
analyzer. In this paper attempt has been made to assess
uncertainty in pushover analysis results by considering user
defined hinges and frame modeled as bare frame and frame
with slab modeled as rigid diaphragm and results compared
with experimental observations. Uncertain parameters
considered includes the strength of concrete, strength of steel
and cover to the reinforcement which are randomly generated
and incorporated into the analysis. The results are then
compared with experimental observations.
Secure Multi-Party Negotiation: An Analysis for Electronic Payments in Mobile...IDES Editor
This paper is an attempt to base on auctions which
presents a frame work for the secure multi-party decision
protocols. In addition to the implementations which are very
light weighted, the main focus is on synchronizing security
features for avoiding agreements manipulations and reducing
the user traffic. Through this paper one can understand that
this different auction protocols on top of the frame work can
be collaborated using mobile devices. This paper present the
negotiation between auctioneer and the proffered and this
negotiation shows that multiparty security is far better than
the existing system.
Selfish Node Isolation & Incentivation using Progressive ThresholdsIDES Editor
The problems associated with selfish nodes in
MANET are addressed by a collaborative watchdog approach
which reduces the detection time for selfish nodes thereby
improves the performance and accuracy of watchdogs[1]. In
the related works they make use of credit based systems, reputation
based mechanisms, pathrater and watchdog mechanism
to detect such selfish nodes. In this paper we follow an approach
of collaborative watchdog which reduces the detection
time for selfish nodes and also involves the removal of such
selfish nodes based on some progressively assessed thresholds.
The threshold gives the nodes a chance to stop misbehaving
before it is permanently deleted from the network.
The node passes through several isolation processes before it
is permanently removed. Another version of AODV protocol
is used here which allows the simulation of selfish nodes in
NS2 by adding or modifying log files in the protocol.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Responsive Parameter based an AntiWorm Approach to Prevent Wormhole Attack in...IDES Editor
The recent advancements in the wireless technology
and their wide-spread deployment have made remarkable
enhancements in efficiency in the corporate and industrial
and Military sectors The increasing popularity and usage of
wireless technology is creating a need for more secure wireless
Ad hoc networks. This paper aims researched and developed
a new protocol that prevents wormhole attacks on a ad hoc
network. A few existing protocols detect wormhole attacks but
they require highly specialized equipment not found on most
wireless devices. This paper aims to develop a defense against
wormhole attacks as an Anti-worm protocol which is based on
responsive parameters, that does not require as a significant
amount of specialized equipment, trick clock synchronization,
no GPS dependencies.
Cloud Security and Data Integrity with Client Accountability FrameworkIDES Editor
The Cloud based services provide much efficient
and seamless ways for data sharing across the cloud. The fact
that the data owners no longer possess data makes it very
difficult to assure data confidentiality and to enable secure
data sharing in the cloud. Despite of all its advantages this
will remain a major limitation that acts as a barrier to the
wider deployment of cloud based services. One of the possible
ways for ensuring trust in this aspect is the introduction of
accountability feature in the cloud computing scenario. The
Cloud framework requires promotion of distributed
accountability for such dynamic environment[1]. In some
works, there‘s an accountable framework suggested to ensure
distributed accountability for data sharing by the generation
of only a log of data access, but without any embedded feedback
mechanism for owner permission towards data
protection[2].The proposed system is an enhanced client
accountability framework which provides an additional client
side verification for each access towards enhanced security of
data. The integrity of content of data which resides in the
cloud service provider is also maintained by secured
outsourcing. Besides, the authentication of JAR(Java Archive)
files are done to ensure file protection and to maintain a safer
environment for data sharing. The analysis of various
functionalities of the framework depicts both the
accountability and security feature in an efficient manner.
Genetic Algorithm based Layered Detection and Defense of HTTP BotnetIDES Editor
A System state in HTTP botnet uses HTTP protocol
for the creation of chain of Botnets thereby compromising
other systems. By using HTTP protocol and port number 80,
attacks can not only be hidden but also pass through the
firewall without being detected. The DPR based detection
leads to better analysis of botnet attacks [3]. However, it
provides only probabilistic detection of the attacker and also
time consuming and error prone. This paper proposes a Genetic
algorithm based layered approach for detecting as well as
preventing botnet attacks. The paper reviews p2p firewall
implementation which forms the basis of filtering.
Performance evaluation is done based on precision, F-value
and probability. Layered approach reduces the computation
and overall time requirement [7]. Genetic algorithm promises
a low false positive rate.
Enhancing Data Storage Security in Cloud Computing Through SteganographyIDES Editor
in cloud computing data storage is a significant issue
because the entire data reside over a set of interconnected
resource pools that enables the data to be accessed through
virtual machines. It moves the application software’s and
databases to the large data centers where the management of
data is actually done. As the resource pools are situated over
various corners of the world, the management of data and
services may not be fully trustworthy. So, there are various
issues that need to be addressed with respect to the
management of data, service of data, privacy of data, security
of data etc. But the privacy and security of data is highly
challenging. To ensure privacy and security of data-at-rest in
cloud computing, we have proposed an effective and a novel
approach to ensure data security in cloud computing by means
of hiding data within images following is the concept of
steganography. The main objective of this paper is to prevent
data access from cloud data storage centers by unauthorized
users. This scheme perfectly stores data at cloud data storage
centers and retrieves data from it when it is needed.
The main tasks of a Wireless Sensor Network
(WSN) are data collection from its nodes and communication
of this data to the base station (BS). The protocols used for
communication among the WSN nodes and between the WSN
and the BS, must consider the resource constraints of nodes,
battery energy, computational capabilities and memory. The
WSN applications involve unattended operation of the network
over an extended period of time. In order to extend the lifetime
of a WSN, efficient routing protocols need to be adopted. The
proposed low power routing protocol based on tree-based
network structure reliably forwards the measured data towards
the BS using TDMA. An energy consumption analysis of the
WSN making use of this protocol is also carried out. It is
found that the network is energy efficient with an average
duty cycle of 0:7% for the WSN nodes. The OmNET++
simulation platform along with MiXiM framework is made
use of.
Permutation of Pixels within the Shares of Visual Cryptography using KBRP for...IDES Editor
The security of authentication of internet based
co-banking services should not be susceptible to high risks.
The passwords are highly vulnerable to virus attacks due to
the lack of high end embedding of security methods. In order
for the passwords to be more secure, people are generally
compelled to select jumbled up character based passwords
which are not only less memorable but are also equally prone
to insecurity. Multiple use of distributed shares has been
studied to solve the problem of authentication by algorithms
based on thresholding of pixels in image processing and visual
cryptography concepts where the subset of shares is considered
for the recovery of the original image for authentication using
correlation function[1][2].The main disadvantage in the above
study is the plain storage of shares and also one of the shares
is being supplied to the customer, which will lead to the
possibility of misuse by a third party. This paper proposes a
technique for scrambling of pixels by key based random
permutation (KBRP) within the shares before the
authentication has been attempted. Total number of shares to
be created is dependent on the multiplicity of ownership of
the account. By this method the problem of uncertainty among
the customers with regard to security, storage, retrieval of
holding of half of the shares is minimized.
This paper presents a trifocal Rotman Lens Design
approach. The effects of focal ratio and element spacing on
the performance of Rotman Lens are described. A three beam
prototype feeding 4 element antenna array working in L-band
has been simulated using RLD v1.7 software. Simulated
results show that the simulated lens has a return loss of –
12.4dB at 1.8GHz. Beam to array port phase error variation
with change in the focal ratio and element spacing has also
been investigated.
Band Clustering for the Lossless Compression of AVIRIS Hyperspectral ImagesIDES Editor
Hyperspectral images can be efficiently compressed
through a linear predictive model, as for example the one
used in the SLSQ algorithm. In this paper we exploit this
predictive model on the AVIRIS images by individuating,
through an off-line approach, a common subset of bands, which
are not spectrally related with any other bands. These bands
are not useful as prediction reference for the SLSQ 3-D
predictive model and we need to encode them via other
prediction strategies which consider only spatial correlation.
We have obtained this subset by clustering the AVIRIS bands
via the clustering by compression approach. The main result
of this paper is the list of the bands, not related with the
others, for AVIRIS images. The clustering trees obtained for
AVIRIS and the relationship among bands they depict is also
an interesting starting point for future research.
Microelectronic Circuit Analogous to Hydrogen Bonding Network in Active Site ...IDES Editor
A microelectronic circuit of block-elements
functionally analogous to two hydrogen bonding networks is
investigated. The hydrogen bonding networks are extracted
from â-lactamase protein and are formed in its active site.
Each hydrogen bond of the network is described in equivalent
electrical circuit by three or four-terminal block-element.
Each block-element is coded in Matlab. Static and dynamic
analyses are performed. The resultant microelectronic circuit
analogous to the hydrogen bonding network operates as
current mirror, sine pulse source, triangular pulse source as
well as signal modulator.
Texture Unit based Monocular Real-world Scene Classification using SOM and KN...IDES Editor
In this paper a method is proposed to discriminate
real world scenes in to natural and manmade scenes of similar
depth. Global-roughness of a scene image varies as a function
of image-depth. Increase in image depth leads to increase in
roughness in manmade scenes; on the contrary natural scenes
exhibit smooth behavior at higher image depth. This particular
arrangement of pixels in scene structure can be well explained
by local texture information in a pixel and its neighborhood.
Our proposed method analyses local texture information of a
scene image using texture unit matrix. For final classification
we have used both supervised and unsupervised learning using
K-Nearest Neighbor classifier (KNN) and Self Organizing
Map (SOM) respectively. This technique is useful for online
classification due to very less computational complexity.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
КАТЕРИНА АБЗЯТОВА «Ефективне планування тестування ключові аспекти та практ...QADay
Lviv Direction QADay 2024 (Professional Development)
КАТЕРИНА АБЗЯТОВА
«Ефективне планування тестування ключові аспекти та практичні поради»
https://linktr.ee/qadayua
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.