Providing security in wireless sensor networks is
a very crucial task. Because of its dynamic nature (no fixed
topology) and resource constraint devices. Which has limited
computational abilities, memory storage and physical
restrictions. Advancement in the field of intrusion and
evaesdroping has increased challenges for a secure
communication between nodes. So, establishments of pair wise
keys in a wireless network becomes a vital issue. Hence,
securely distributing keys among sensor nodes is a
fundamental challenge for providing seamless transmission
and security services. Having little resources in our hand,
it is always a tough task to design and implement protocols.
But this paper proposes a new robust key pre-distribution
scheme which resolves this issue without compromising
security. This paper presents a new mechanism to achieve
pair wise keys between two sensor nodes by using the
algebraic, exponential, logarithm functions and prime
numbers. The resilience method under this scheme is based
on discontinuous functions which is hard to be spoofed.
Cryptography using artificial neural networkMahira Banu
This document proposes using artificial neural networks for cryptography. It describes using a backpropagation neural network for decryption, where the network is trained on encrypted-decrypted message pairs. Boolean algebra is used for encryption, permuting messages and "doping" with additional bits. The neural network can then be used as a public key for decryption, with a private key for encryption. Simulation results showed the neural network approach weakened key guessing compared to other methods.
NeuroCrypto: C++ Implementation of Neural Cryptography with Rijndael CipherSagun Man Singh Shrestha
This work is the software implementation of the concept of neural cryptography, which is a communication of two tree parity machines for agreement on a common key over a public channel. This key is utilized to encrypt a sensitive message to be transmitted over an insecure channel using Rijndael cipher. This is a new potential source for public key cryptography schemes which are not based on number theoretic functions, and have small time and memory complexities. This paper will give a brief introduction to artificial neural networks, cryptography and its types, which will help explain why the two communicating terminals converge to a common key in neural cryptography and will also cover the Rijndael (AES) cipher. This paper is intended to show that such neural key exchange protocol and AES encryption can be practically implemented in a high-level programming language viz. C++, which could be further extended in higher-level applications. Both CLI and GUI implementations of the software created using Visual C++ (.NET framework) are presented.
The document discusses artificial neural networks and their application to cryptography. It begins by explaining that artificial neural networks are designed to model the way the brain performs tasks in a massively parallel manner. It then provides details on the basic structure of artificial neural networks, including processing units, weighted connections, and learning rules. The document next discusses using artificial neural networks for cryptography, including implementing a sequential machine with a Jordan network for encryption/decryption and using a chaotic neural network to encrypt digital signals in a secure manner. It concludes that artificial neural networks provide a novel approach for encrypting and decrypting data.
Secured transmission through multi layer perceptron in wireless communication...ijmnct
In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication
has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both
ends generate an identical output bit and the network are trained based on the output which is used to
synchronize the network at both ends and thus forms a secret-key at end of synchronizations of the
networks. Weights or hidden units of the hidden layer help to form a secret session key. The plain text is
encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the
final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use
identical multilayer perceptron generated session key for performing deciphering process for getting the
plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response
time in transmission with some existing classical techniques, which shows comparable results for the
proposed technique. Variation numbers of input vectors and hidden layers will increase the confusion
/diffusion of the schemeand hence increase the security. As a result variable energy based techniques may
be achieved which may be applicable devices/interface of the heterogeneous sizes of the network/device.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
This document provides information about the CS407 Neural Computation course. It outlines the lecturer, timetable, assessment, textbook recommendations, and covers topics from today's lecture including an introduction to neural networks, their inspiration from the brain, a brief history, applications, and an overview of topics to be covered in the course.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Cryptography using artificial neural networkMahira Banu
This document proposes using artificial neural networks for cryptography. It describes using a backpropagation neural network for decryption, where the network is trained on encrypted-decrypted message pairs. Boolean algebra is used for encryption, permuting messages and "doping" with additional bits. The neural network can then be used as a public key for decryption, with a private key for encryption. Simulation results showed the neural network approach weakened key guessing compared to other methods.
NeuroCrypto: C++ Implementation of Neural Cryptography with Rijndael CipherSagun Man Singh Shrestha
This work is the software implementation of the concept of neural cryptography, which is a communication of two tree parity machines for agreement on a common key over a public channel. This key is utilized to encrypt a sensitive message to be transmitted over an insecure channel using Rijndael cipher. This is a new potential source for public key cryptography schemes which are not based on number theoretic functions, and have small time and memory complexities. This paper will give a brief introduction to artificial neural networks, cryptography and its types, which will help explain why the two communicating terminals converge to a common key in neural cryptography and will also cover the Rijndael (AES) cipher. This paper is intended to show that such neural key exchange protocol and AES encryption can be practically implemented in a high-level programming language viz. C++, which could be further extended in higher-level applications. Both CLI and GUI implementations of the software created using Visual C++ (.NET framework) are presented.
The document discusses artificial neural networks and their application to cryptography. It begins by explaining that artificial neural networks are designed to model the way the brain performs tasks in a massively parallel manner. It then provides details on the basic structure of artificial neural networks, including processing units, weighted connections, and learning rules. The document next discusses using artificial neural networks for cryptography, including implementing a sequential machine with a Jordan network for encryption/decryption and using a chaotic neural network to encrypt digital signals in a secure manner. It concludes that artificial neural networks provide a novel approach for encrypting and decrypting data.
Secured transmission through multi layer perceptron in wireless communication...ijmnct
In this paper, a multilayer perceptron guided encryption/decryption (STMLP) in wireless communication
has been proposed for exchange of data/information. Multilayer perceptron transmitting systems at both
ends generate an identical output bit and the network are trained based on the output which is used to
synchronize the network at both ends and thus forms a secret-key at end of synchronizations of the
networks. Weights or hidden units of the hidden layer help to form a secret session key. The plain text is
encrypted through chaining , cascaded xoring of multilayer perceptron generated session key. If size of the
final block of plain text is less than the size of the key then this block is kept unaltered. Receiver will use
identical multilayer perceptron generated session key for performing deciphering process for getting the
plain text. Parametric tests have been done and results are compared in terms of Chi-Square test, response
time in transmission with some existing classical techniques, which shows comparable results for the
proposed technique. Variation numbers of input vectors and hidden layers will increase the confusion
/diffusion of the schemeand hence increase the security. As a result variable energy based techniques may
be achieved which may be applicable devices/interface of the heterogeneous sizes of the network/device.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
This document provides information about the CS407 Neural Computation course. It outlines the lecturer, timetable, assessment, textbook recommendations, and covers topics from today's lecture including an introduction to neural networks, their inspiration from the brain, a brief history, applications, and an overview of topics to be covered in the course.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses privacy-preserving decision tree learning using the Gini index over horizontally partitioned data. It proposes a main protocol and sub-protocols to compute the Gini index in a privacy-preserving manner among multiple parties. The protocol allows the largest class to be isolated in one node while distributing other classes among other nodes. It analyzes the computational and communication complexity of the proposed approach. Future work includes implementing the protocol and comparing it to other techniques.
A Modified Pair Wise Key Distribution Schemes and There Effect On Network Per...IJERA Editor
Key distribution schemes has always played a pivotal role in the security of wireless sensor networks. In this research work we focus mainly on the security aspect of WSN . We have developed a modified key distribution scheme which uses the concepts of post as well as pre distribution scheme and thus he proved to be a better alternative then the rest of two schemes. Simulation study has been carried out using matlab. The effort turned out to be fruitful s our modified scheme showed less dead nodes per round of data transfer as compared to post deployment scheme.
This document provides an overview and literature review of unsupervised feature learning techniques. It begins with background on machine learning and the challenges of feature engineering. It then discusses unsupervised feature learning as a framework to learn representations from unlabeled data. The document specifically examines sparse autoencoders, PCA, whitening, and self-taught learning. It provides details on the mathematical concepts and implementations of these algorithms, including applying them to learn features from images. The goal is to use unsupervised learning to extract features that can enhance supervised models without requiring labeled training data.
Full Communication in a Wireless Sensor Network by Merging Blocks of a Key Pr...cscpconf
Wireless Sensor Networks (WSN) are constraint by the limited resources available to its
constituting sensors. Thus the use of public-key cryptographyduring message exchange gets
forbidden. One has to invoke symmetric key techniques. This leads to key distribution in the
sensors which in itself is a major challenge. Again due to resource constraints, Key
Predistrubution (KPD) methods are preferred to other distribution techniques. It requires
predistribution of keys in nodes prior to deployment and establishing immediately once
deployed. However there are certain weaknesses in various existing KPD schemes. For
instance, often it is not guaranteed that any given pair of nodes communicate directly. This
leads one to revert to multi-hop communication involving intermediate sensor nodes resulting
in increased cost of communication. In this work a key predistribution technique using ReedSolomon
codes is considered which is faced with the above weakness. The authors suggests a
novel technique of merging certain number of sensors into blocks ensuring that the blocks
have full connectivity amongst themselves. Here the blocks are chosen in such a way that it
ensures no intra-node communication. Further this approach improves both time and space
complexity of the system
Design of Efficient 4×4 Quaternary Vedic Multiplier Using Current-Mode Multi-...idescitation
Vedic multiplier is based on ancient Indian Vedic mathematics that offers
simpler and hierarchical structure. Multi-valued logic results in the effective utilization of
interconnections, which reduces the chip size and delay. This paper proposes a new design
of 4×4 Vedic multiplier using quaternary current-mode multi-valued logic, equivalent to
iplier has
very low transistor-count and consumes very low power as compared to other multiplier
designs. Since the performance of a digital signal processor depends mainly on the
multipliers used, the proposed approach can greatly enhance the performance of a digital
signal processing system.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Thanks to Deep Learning, Artificial Intelligence is now getting smart. Deep Learning models attempt to mimic the activity of the neocortex. It is understood that the activity of these layers of neurons is what constitutes a brain to be able to "think". These models learn to recognize patterns in digital representations of data in a very similar sense to humans. In this survey report, we introduce the most important concepts of Deep Learning along with the state of the art models that are now widely adopted in commercial products.
The document discusses various unsupervised learning networks including Kohonen self-organizing feature maps (KSOFM), competitive learning networks like Max Net and Mexican Hat networks, and other networks like Hamming networks, counterpropagation networks, and adaptive resonance theory networks. It provides details on the algorithms and mechanisms of KSOFM, including competition, cooperation, and adaptation between neurons. It also summarizes learning vector quantization and discusses counterpropagation networks and their training process.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
A FAST FAULT TOLERANT PARTITIONING ALGORITHM FOR WIRELESS SENSOR NETWORKScsandit
The document describes a distributed algorithm for partitioning wireless sensor networks into connected partitions to maximize network lifetime. The algorithm finds the maximum number of partitions where each partition is connected and covers the monitoring area. It does this efficiently with less computation time and message overhead compared to previous works. The algorithm also includes a distributed fault recovery method that can locally rearrange an affected partition to tolerate single node failures and extend network lifetime further. Simulation results show the partitioning algorithm is faster and creates better topology partitions, while the fault recovery enhances lifetime by over 50%.
The document proposes a novel secure scheme for computing the cosine similarity between two integer vectors with malicious adversaries. The scheme uses distributed ElGamal encryption and zero-knowledge proofs to privately compute the cosine coefficient between two parties' vector inputs while preserving privacy. Security analysis shows the scheme can resist attacks from malicious adversaries by simulating the ideal functionality using the encryption scheme and zero-knowledge proofs.
This document provides a summary of a study on deep learning. It introduces artificial neural networks as the building blocks of deep learning architectures. Neural networks are modeled after the human brain and consist of interconnected nodes that learn patterns in data. Deep learning aims to develop human-level artificial intelligence. The document explains key concepts like activation functions, which introduce non-linearity, and backpropagation, which is used to train neural networks by minimizing error. It surveys popular deep learning models and their objectives, like convolutional neural networks for computer vision and recurrent neural networks for language.
Applying Deep Learning Machine Translation to Language ServicesYannis Flet-Berliac
Recurrent neural networks (RNNs) have been performing well for learning tasks for several decades now. The most useful benefit they present for this paper is their ability to use contextual information when mapping between input and output sequences.
A deep neural network for machine translation implies the use of a sequence-to-sequence model, consisting of two RNNs: an encoder that processes the input and a decoder that generates the output.
To meaningfully assess the model’s performances, texts from a translation company and thoughts from skilled experts about specialized topics will be tested.
Modified Koblitz Encoding Method for ECCidescitation
Extensive use of Wireless Sensor Networks is giving
rise to different types of threats in certain commercial and
military applications. To protect the WSN data communication
against various threats appropriate security schemes are
needed. However, WSN nodes are resource constrained, with
respect to limited battery energy, and limited computational
and memory available with each WSN node. Hence, the
security model to be used in WSN’s should use minimal
resources to the extent possible and it should also provide
good security. Elliptic curve cryptography (ECC) is the best
suited algorithm for WSNs, as it offers better security for
smaller key sizes compared to the popular RSA algorithm. In
ECC, encoding of message data to a point lying on the give
Elliptic Curve is a major problem as the encoding consumes
more resources. This paper provides a novel encoding
procedure to overcome these problems to a large extent. This
paper also describes implementation aspects of the proposed
encoding and decoding methods.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
The document discusses image captioning using deep neural networks. It begins by providing examples of how humans can easily describe images but generating image captions with a computer program was previously very difficult. Recent advances in deep learning, specifically using convolutional neural networks (CNNs) to recognize objects in images and recurrent neural networks (RNNs) to generate captions, have enabled automated image captioning. The document discusses CNN and RNN architectures for image captioning and provides examples of pre-trained models that can be used, such as VGG-16.
Implementation Secure Authentication Using Elliptic Curve CryptographyAM Publications
Elliptic curve cryptography is the most efficient public key encryption scheme based on the elliptic curve concepts that
can be used to create faster, smaller, and efficient cryptographic keys. As a use of network increase for critical transaction, so
huge damages are caused by intrusion attacks hence there is the need of computer network security. To protect network against
various active and passive attack, various technique have been suggested. Mobile devices have many differences in their
capabilities, computational powers and security requirements. The security of mobile communication has stopped the list of
concerns for mobile phone users. Confidentiality, Authentication, Integrity and Non-repudiation are required security services for mobile communication.
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY IJNSA Journal
Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Lattice reduction has been successfully utilizing in Number Theory, Linear algebra and Cryptology. Not only the existence of lattice based cryptosystems of hard in nature, but also has vulnerabilities by lattice reduction techniques. In this survey paper, we are focusing on point lattices and then describing an introduction to
the theoretical and practical aspects of lattice reduction. Finally, we describe the applications of lattice reduction in Number theory, Linear algebra.
“Proposed Model for Network Security Issues Using Elliptical Curve Cryptography”IOSR Journals
Abstract: Elliptic Curve Cryptography (ECC) plays an important role in today’s public key based security
systems. . ECC is a faster and more secure method of encryption as compared to other Public Key
Cryptographic algorithms. This paper focuses on the performance advantages of using ECC in the wireless
network. So in this paper its algorithm has been implemented and analyzed for various bit length inputs. The
Private key is known only to sender and receiver and hence data transmission is secure.
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
LOCATION BASED DETECTION OF REPLICATION ATTACKS AND COLLUDING ATTACKSEditor IJCATR
Wireless sensor networks gains its importance because of the critical applications in which it is involved like
industrial automation, healthcare applications, military and surveillance. Among security attacks in wireless sensor
networks we consider an active attack, NODE REPLICATION attack and COLLUDING attack. We use localized
algorithms, ((ie) replication detection is done at the node level and eliminated without the intervention of the base
station) to solve replication attacks and colluding attacks. Replication attacks are detected to using a unique key pair
and cryptographic hash function. We propose to use XED and EED algorithm[1] ( authenticates the node and tries to
reduce the replication) , with this using the Event detected location , non-beacon node is used to find the location of a
malicious node and by a simple threshold verification we identify malicious clusters
The document discusses privacy-preserving decision tree learning using the Gini index over horizontally partitioned data. It proposes a main protocol and sub-protocols to compute the Gini index in a privacy-preserving manner among multiple parties. The protocol allows the largest class to be isolated in one node while distributing other classes among other nodes. It analyzes the computational and communication complexity of the proposed approach. Future work includes implementing the protocol and comparing it to other techniques.
A Modified Pair Wise Key Distribution Schemes and There Effect On Network Per...IJERA Editor
Key distribution schemes has always played a pivotal role in the security of wireless sensor networks. In this research work we focus mainly on the security aspect of WSN . We have developed a modified key distribution scheme which uses the concepts of post as well as pre distribution scheme and thus he proved to be a better alternative then the rest of two schemes. Simulation study has been carried out using matlab. The effort turned out to be fruitful s our modified scheme showed less dead nodes per round of data transfer as compared to post deployment scheme.
This document provides an overview and literature review of unsupervised feature learning techniques. It begins with background on machine learning and the challenges of feature engineering. It then discusses unsupervised feature learning as a framework to learn representations from unlabeled data. The document specifically examines sparse autoencoders, PCA, whitening, and self-taught learning. It provides details on the mathematical concepts and implementations of these algorithms, including applying them to learn features from images. The goal is to use unsupervised learning to extract features that can enhance supervised models without requiring labeled training data.
Full Communication in a Wireless Sensor Network by Merging Blocks of a Key Pr...cscpconf
Wireless Sensor Networks (WSN) are constraint by the limited resources available to its
constituting sensors. Thus the use of public-key cryptographyduring message exchange gets
forbidden. One has to invoke symmetric key techniques. This leads to key distribution in the
sensors which in itself is a major challenge. Again due to resource constraints, Key
Predistrubution (KPD) methods are preferred to other distribution techniques. It requires
predistribution of keys in nodes prior to deployment and establishing immediately once
deployed. However there are certain weaknesses in various existing KPD schemes. For
instance, often it is not guaranteed that any given pair of nodes communicate directly. This
leads one to revert to multi-hop communication involving intermediate sensor nodes resulting
in increased cost of communication. In this work a key predistribution technique using ReedSolomon
codes is considered which is faced with the above weakness. The authors suggests a
novel technique of merging certain number of sensors into blocks ensuring that the blocks
have full connectivity amongst themselves. Here the blocks are chosen in such a way that it
ensures no intra-node communication. Further this approach improves both time and space
complexity of the system
Design of Efficient 4×4 Quaternary Vedic Multiplier Using Current-Mode Multi-...idescitation
Vedic multiplier is based on ancient Indian Vedic mathematics that offers
simpler and hierarchical structure. Multi-valued logic results in the effective utilization of
interconnections, which reduces the chip size and delay. This paper proposes a new design
of 4×4 Vedic multiplier using quaternary current-mode multi-valued logic, equivalent to
iplier has
very low transistor-count and consumes very low power as compared to other multiplier
designs. Since the performance of a digital signal processor depends mainly on the
multipliers used, the proposed approach can greatly enhance the performance of a digital
signal processing system.
With massive amounts of computational power, machines can now recognize objects and translate speech in real time. Thanks to Deep Learning, Artificial Intelligence is now getting smart. Deep Learning models attempt to mimic the activity of the neocortex. It is understood that the activity of these layers of neurons is what constitutes a brain to be able to "think". These models learn to recognize patterns in digital representations of data in a very similar sense to humans. In this survey report, we introduce the most important concepts of Deep Learning along with the state of the art models that are now widely adopted in commercial products.
The document discusses various unsupervised learning networks including Kohonen self-organizing feature maps (KSOFM), competitive learning networks like Max Net and Mexican Hat networks, and other networks like Hamming networks, counterpropagation networks, and adaptive resonance theory networks. It provides details on the algorithms and mechanisms of KSOFM, including competition, cooperation, and adaptation between neurons. It also summarizes learning vector quantization and discusses counterpropagation networks and their training process.
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Neural networks are computing systems inspired by biological neural networks. They are composed of interconnected nodes that process input data and transmit signals to each other. The document discusses various types of neural networks including feedforward, recurrent, convolutional, and modular neural networks. It also describes the basic architecture of neural networks including input, hidden, and output layers. Neural networks can be used for applications like pattern recognition, data classification, and more. They are well-suited for complex, nonlinear problems. The document provides an overview of neural networks and their functioning.
A FAST FAULT TOLERANT PARTITIONING ALGORITHM FOR WIRELESS SENSOR NETWORKScsandit
The document describes a distributed algorithm for partitioning wireless sensor networks into connected partitions to maximize network lifetime. The algorithm finds the maximum number of partitions where each partition is connected and covers the monitoring area. It does this efficiently with less computation time and message overhead compared to previous works. The algorithm also includes a distributed fault recovery method that can locally rearrange an affected partition to tolerate single node failures and extend network lifetime further. Simulation results show the partitioning algorithm is faster and creates better topology partitions, while the fault recovery enhances lifetime by over 50%.
The document proposes a novel secure scheme for computing the cosine similarity between two integer vectors with malicious adversaries. The scheme uses distributed ElGamal encryption and zero-knowledge proofs to privately compute the cosine coefficient between two parties' vector inputs while preserving privacy. Security analysis shows the scheme can resist attacks from malicious adversaries by simulating the ideal functionality using the encryption scheme and zero-knowledge proofs.
This document provides a summary of a study on deep learning. It introduces artificial neural networks as the building blocks of deep learning architectures. Neural networks are modeled after the human brain and consist of interconnected nodes that learn patterns in data. Deep learning aims to develop human-level artificial intelligence. The document explains key concepts like activation functions, which introduce non-linearity, and backpropagation, which is used to train neural networks by minimizing error. It surveys popular deep learning models and their objectives, like convolutional neural networks for computer vision and recurrent neural networks for language.
Applying Deep Learning Machine Translation to Language ServicesYannis Flet-Berliac
Recurrent neural networks (RNNs) have been performing well for learning tasks for several decades now. The most useful benefit they present for this paper is their ability to use contextual information when mapping between input and output sequences.
A deep neural network for machine translation implies the use of a sequence-to-sequence model, consisting of two RNNs: an encoder that processes the input and a decoder that generates the output.
To meaningfully assess the model’s performances, texts from a translation company and thoughts from skilled experts about specialized topics will be tested.
Modified Koblitz Encoding Method for ECCidescitation
Extensive use of Wireless Sensor Networks is giving
rise to different types of threats in certain commercial and
military applications. To protect the WSN data communication
against various threats appropriate security schemes are
needed. However, WSN nodes are resource constrained, with
respect to limited battery energy, and limited computational
and memory available with each WSN node. Hence, the
security model to be used in WSN’s should use minimal
resources to the extent possible and it should also provide
good security. Elliptic curve cryptography (ECC) is the best
suited algorithm for WSNs, as it offers better security for
smaller key sizes compared to the popular RSA algorithm. In
ECC, encoding of message data to a point lying on the give
Elliptic Curve is a major problem as the encoding consumes
more resources. This paper provides a novel encoding
procedure to overcome these problems to a large extent. This
paper also describes implementation aspects of the proposed
encoding and decoding methods.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
The document discusses image captioning using deep neural networks. It begins by providing examples of how humans can easily describe images but generating image captions with a computer program was previously very difficult. Recent advances in deep learning, specifically using convolutional neural networks (CNNs) to recognize objects in images and recurrent neural networks (RNNs) to generate captions, have enabled automated image captioning. The document discusses CNN and RNN architectures for image captioning and provides examples of pre-trained models that can be used, such as VGG-16.
Implementation Secure Authentication Using Elliptic Curve CryptographyAM Publications
Elliptic curve cryptography is the most efficient public key encryption scheme based on the elliptic curve concepts that
can be used to create faster, smaller, and efficient cryptographic keys. As a use of network increase for critical transaction, so
huge damages are caused by intrusion attacks hence there is the need of computer network security. To protect network against
various active and passive attack, various technique have been suggested. Mobile devices have many differences in their
capabilities, computational powers and security requirements. The security of mobile communication has stopped the list of
concerns for mobile phone users. Confidentiality, Authentication, Integrity and Non-repudiation are required security services for mobile communication.
LATTICE BASED TOOLS IN CRYPTANALYSIS FOR PUBLIC KEY CRYPTOGRAPHY IJNSA Journal
Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Lattice reduction has been successfully utilizing in Number Theory, Linear algebra and Cryptology. Not only the existence of lattice based cryptosystems of hard in nature, but also has vulnerabilities by lattice reduction techniques. In this survey paper, we are focusing on point lattices and then describing an introduction to
the theoretical and practical aspects of lattice reduction. Finally, we describe the applications of lattice reduction in Number theory, Linear algebra.
“Proposed Model for Network Security Issues Using Elliptical Curve Cryptography”IOSR Journals
Abstract: Elliptic Curve Cryptography (ECC) plays an important role in today’s public key based security
systems. . ECC is a faster and more secure method of encryption as compared to other Public Key
Cryptographic algorithms. This paper focuses on the performance advantages of using ECC in the wireless
network. So in this paper its algorithm has been implemented and analyzed for various bit length inputs. The
Private key is known only to sender and receiver and hence data transmission is secure.
Abstract— This presents a comprehensible neural network tree (CNNTREE). CNNTREE is a proposed general modular neural network structure, where each node in this tree is a comprehensible expert neural network (CENN). One advantage of using CNNTREE is that it is a “gray box”; because it can be interpreted easily for symbolic systems; where each node in the CNNTREE is equivalent for symbolic operator in the symbolic system. Another advantage of CNNTREE is that it could be trained as any normal multi layer feed forward neural network. An evolutionary algorithm is given for designing the CNNTREE. Back propagation is also checked as local learning algorithm that fits for real time learning constraints. The tree generalization and training performance are examined using experiments with a digit recognition problem.
Handwritten Digit Recognition using Convolutional Neural NetworksIRJET Journal
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
LOCATION BASED DETECTION OF REPLICATION ATTACKS AND COLLUDING ATTACKSEditor IJCATR
Wireless sensor networks gains its importance because of the critical applications in which it is involved like
industrial automation, healthcare applications, military and surveillance. Among security attacks in wireless sensor
networks we consider an active attack, NODE REPLICATION attack and COLLUDING attack. We use localized
algorithms, ((ie) replication detection is done at the node level and eliminated without the intervention of the base
station) to solve replication attacks and colluding attacks. Replication attacks are detected to using a unique key pair
and cryptographic hash function. We propose to use XED and EED algorithm[1] ( authenticates the node and tries to
reduce the replication) , with this using the Event detected location , non-beacon node is used to find the location of a
malicious node and by a simple threshold verification we identify malicious clusters
Research on key predistribution scheme of wireless sensor networksIAEME Publication
This document summarizes a research paper on a novel key pre-distribution scheme for wireless sensor networks. It begins with an introduction to the challenges of key management in wireless sensor networks. It then describes the proposed scheme which has three phases: setup, direct key establishment, and path key establishment. The setup phase generates a large key pool and distributes keys to each sensor node. Direct key establishment allows sensor nodes to discover if they share keys directly. Path key establishment establishes keys through intermediate nodes if direct sharing fails. Performance analysis shows the scheme has higher local connectivity and stronger resilience against node capture attacks compared to previous schemes.
Secure and efficient key pre distribution schemes for wsn using combinatorial...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Key Management Schemes for Secure Communication in Heterogeneous Sensor NetworksIDES Editor
Hierarchical Sensor Network organization is
widely used to achieve energy efficiency in Wireless Sensor
Networks(WSN). To achieve security in hierarchical WSN,
it is important to be able to encrypt the messages sent
between sensor nodes and its cluster head. The key
management task is challenging due to resource constrained
nature of WSN. In this paper we are proposing two key
management schemes for hierarchical networks which
handles various events like node addition, node compromise
and key refresh at regular intervals. The Tree-Based
Scheme ensures in-network processing by maintaining some
additional intermediate keys. Whereas the CRT-Based
Scheme performs the key management with minimum
communication and storage at each node.
REVIEW ON KEY PREDISTRIBUTION SCHEMES IN WIRELESS SENSOR NETWORKSijassn
A wireless sensor network consist distributed sensors which are used to monitor physical or environmental conditions like temperature, sound, pressure and so on. Wireless sensor network are used in future in many applications like military, investigation teams, researches and so on. Security is the main issue in wireless sensor network. Sensor network arrange several types of data packets, packets of routing protocols and packets of key management protocols. Key management is the most effective method for providing better security against several types of attacks. This paper discusses the various key pre-distribution approaches along with their advantages and disadvantages.
Review on key predistribution schemes in wireless sensor networksijassn
A wireless sensor network consist distributed sensors which are used to monitor physical or environmental
conditions like temperature, sound, pressure and so on. Wireless sensor network are used in future in many
applications like military, investigation teams, researches and so on. Security is the main issue in wireless
sensor network. Sensor network arrange several types of data packets, packets of routing protocols and
packets of key management protocols. Key management is the most effective method for providing better
security against several types of attacks. This paper discusses the various key pre-distribution approaches
along with their advantages and disadvantages.
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Recognition of handwritten digits using rbf neural networkeSAT Journals
Abstract Pattern recognition is required in many fields for different purposes. Methods based on Radial basis function (RBF) neural networks are found to be very successful in pattern classification problems. Training neural network is in general a challenging nonlinear optimization problem. Several algorithms have been proposed for choosing the RBF neural network prototypes and training the network. In this paper RBF neural network using decoupling Kalman filter method is proposed for handwritten digit recognition applications. The efficacy of the proposed method is tested on the handwritten digits of different fonts and found that it is successful in recognizing the digits. Keywords: - Neural network, RBF neural network, Decoupled kalman filter Training, Zoning method
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
WSN performance based on node placement by genetic algorithm at smart home en...TELKOMNIKA JOURNAL
Wireless sensor connectivity is one of several factors that determines the communication reliability of each node. The placement of the node depends on the area that covered by wireless coverage area, so the node placement should be optimally placed. But the other aspect is the sensor coverage area. Sensor coverage area sometimes could be different with wireless sensor coverage area. Based on that situation, it needs to optimize that situation. Genetic Algorithm is an algorithm that utilizes a heuristic approach that uses biological mechanism evolution. It used to evolution the best position of Sensor Node based on Wireless and Sensor coverage area. After the position of each node generated by Genetic Algorithm, it still needs to evaluate the wireless sensor node performance. The performance indicates that the genetic algorithm can be used to determine sensor node placement in the smart home environment. The smart home environment used to monitor event at the house such as wildfire. In this research used Quality of Services (QoS) to measure wireless sensor performance. The experimental testing scenario will be used to place several nodes that generated. The QoS performed systems reliability that produced based on 3, 4 and 5 testing nodes, the minimum and maximum of each: delay is 6.21 and 8.74 milliseconds, jitter is 0.11 and 1.59 Hz and throughput is 68.83 and 90.49 bps. Based on ETSI classification, the performance of sensor node placement is Good and acceptable in real-time systems.
This document summarizes a research paper that proposes using a Hamming network to recognize noisy numerals. Specifically:
1) The paper aims to design a system that can recognize both clean and noisy (corrupted by salt and pepper noise) numerals using a Hamming network.
2) The Hamming network contains a feedforward layer to calculate correlations between input and prototype patterns, and a recurrent layer that determines the closest prototype.
3) Test results showed the network could recognize clean numerals with 100% accuracy and noisy numerals with an average of 89% accuracy.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Formulation of modularity factor for community detection applyingIAEME Publication
This document discusses a proposed algorithm for community detection in dynamic social networks. It involves applying multi-resolution techniques to a multi-objective immune algorithm. The algorithm aims to maximize community quality and minimize temporal cost. It has three modules: 1) calculating modularity and betweenness values, 2) identifying high similarity vertex pairs, and 3) regrouping isolated vertices based on modularity values. A case study on Facebook is provided to demonstrate detecting strong and weak communities based on user activities like photos tagged, comments, and posts shared. The algorithm is presented as the first phase for community detection in dynamic networks, with the second phase still under development.
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are
more efficient, generic and highly adaptive. Neural Network based technologies have high ability of
adaption as well as generalization. As per our knowledge, very little work has been done in this field using
neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised
learning algorithms of artificial neural network by creating classifiers for the complex problem of latest
web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
Similar to Pairwise Keys Generation Using Prime Number Function in Wireless Sensor Networks (20)
Power System State Estimation - A ReviewIDES Editor
This document provides a review of power system state estimation techniques. It discusses both static and dynamic state estimation algorithms. For static state estimation, it covers weighted least squares, decoupled, and robust estimation methods. Weighted least squares is commonly used but can have numerical instability issues. Decoupled state estimation approximates the gain matrix for faster computation. Robust estimation uses M-estimators and other techniques to handle outliers and bad data. Dynamic state estimation applies Kalman filtering, leapfrog algorithms, and other methods to continuously monitor system states over time.
Artificial Intelligence Technique based Reactive Power Planning Incorporating...IDES Editor
This document summarizes a research paper that proposes using artificial intelligence techniques and FACTS controllers for reactive power planning in real-time power transmission systems. The paper formulates the reactive power planning problem and incorporates flexible AC transmission system (FACTS) devices like static VAR compensators (SVC), thyristor controlled series capacitors (TCSC), and unified power flow controllers (UPFC). Evolutionary algorithms like evolutionary programming (EP) and differential evolution (DE) are applied to find the optimal locations and settings of the FACTS controllers to minimize losses and costs. Simulation results on IEEE 30-bus and 72-bus Indian test systems show that UPFC performs best in reducing losses compared to SVC and TCSC.
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 document summarizes and analyzes secure multi-party negotiation protocols for electronic payments in mobile computing. It presents a framework for secure multi-party decision protocols using lightweight implementations. The main focus is on synchronizing security features to avoid agreement manipulation and reduce user traffic. The paper describes negotiation between an auctioneer and bidders, showing multiparty security is better than existing systems. It analyzes the performance of encryption algorithms like ECC, XTR, and RSA for use in the multiparty negotiation protocols.
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
This document summarizes a proposed cloud security and data integrity framework that provides client accountability. The framework aims to address issues like lack of user control over cloud data, need for data transparency and tracking, and ensuring data integrity. It proposes using JAR (Java Archive) files for data sharing due to benefits like portability. The framework incorporates client-side verification using MD5 hashing, digital signature-based authentication of JAR files, and use of HMAC to ensure data integrity. It also uses password-based encryption of log files to keep them tamper-proof. The framework is intended to provide both accountability and security for data sharing in cloud environments.
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
This document summarizes a research paper that proposes a method for enhancing data security in cloud computing through steganography. The method hides user data in digital images stored on cloud servers. When data needs to be accessed, it is extracted from the images. The document outlines the cloud architecture and security issues addressed. It then describes the proposed system architecture, security model, and data storage and retrieval process. Data is partitioned and hidden in multiple images to improve security. The goal is to prevent unauthorized access to user data stored on cloud servers.
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.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Scaling RAG Applications to serve millions of users", Kevin GoedeckeFwdays
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
High performance Serverless Java on AWS- GoTo Amsterdam 2024Vadym Kazulkin
Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.