—This paper proposes a technique uses decision tree for dataset and to find the basic
parameters for creating the membership functions of fuzzy inference system for Intrusion Detection and
Forensics. Approach of generating rules using clustering methods is limited to the problems of
clustering techniques. To trait to solve this problem, several solutions have been proposed using
various Techniques. One such Technique is proposed to be applied here, for an analysis to Intrusion
Detection and Forensics. . Fuzzy Inference approach and decision algorithms are investigated in this
work. Decision tree is used to identify the parameters to create the fuzzy inference system. Fuzzy
inference system used as an input and the final ANFIS structure is generated for intrusion detection
and forensics. The experiments and evaluations of the proposed method were done with NSL-KDD
intrusion detection dataset.
PERFORMANCE EVALUATION OF DIFFERENT KERNELS FOR SUPPORT VECTOR MACHINE USED I...IJCNCJournal
The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with numerous features. As a result, in order to
get rid of this problem, several types of intrusion detection methods with different levels of accuracy have been proposed which leads the choice of an effective and robust method for IDS as a very important topic in information security. In this regard, the support vector machine (SVM) has been playing an important role to provide potential solutions for the IDS problem. However, the practicability of introducing SVM is
affected by the difficulties in selecting appropriate kernel and its parameters. From this viewpoint, this paper presents the work to apply different kernels for SVM in ID Son the KDD’99 Dataset and NSL-KDD dataset as well as to find out which kernel is the best for SVM. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99train and test dataset prior to apply different kernel for SVM. This RRE-KDD consists of both KDD99Train+ and KDD99 Test+ dataset for training and testing purposes, respectively. The way to derive RRE-KDD data set is different from that of NSL-KDD
data set. The experimental results indicate that Laplace kernel can achieve higher detection rate and lower false positive rate with higher precision than other kernel son both RRE-KDD and NSL-KDD datasets. It is also found that the performances of other kernels are dependent on datasets.
Randomness evaluation framework of cryptographic algorithmsijcisjournal
Nowadays, computer systems are developing very rapidly and become more and more complex, which
leads to the necessity to provide security for them. This paper is intended to present software for testing
and evaluating cryptographic algorithms. When evaluating block and stream ciphers one of the most basic
property expected from them is to pass statistical randomness testing, demonstrating in this way their
suitability to be random number generators. The primary goal of this paper is to propose a new framework
to evaluate the randomness of cryptographic algorithms: based only on a .dll file which offers access to the
encryption function, the decryption function and the key schedule function of the cipher that has to be tested
(block cipher or stream cipher), the application evaluates the randomness and provides an interpretation of
the results. For this, all nine tests used for evaluation of AES candidate block ciphers and three NIST
statistical tests are applied to the algorithm being tested. In this paper, we have evaluated Tiny Encryption
Algorithm (block cipher), Camellia (block cipher) and LEX (stream cipher) to determine if they pass
statistical randomness testing.
A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
Network Anomaly detection based on fuzzy logic and Genetic AlgorithmYatindra shashi
The document discusses different approaches for anomaly-based network intrusion detection systems (NIDS), including machine learning, fuzzy logic, and genetic algorithm approaches. Specifically, it summarizes:
1) Fuzzy logic based NIDS that uses a strategy to automatically generate fuzzy rules from training data to define normal network behavior and detect anomalies. Experiments using the KDD Cup 99 dataset showed it achieved 90% accuracy.
2) The use of genetic algorithms for NIDS, which encode network data as chromosomes and use selection/evolution principles to optimize detection of intrusions based on fitness metrics like packet drop rates.
3) Research applying fuzzy logic and genetic algorithm techniques to detect packet dropping attacks by malicious nodes in mobile ad hoc
This document presents a multi-classification approach for detecting network attacks using a layered model. The proposed system consists of two stages - the first stage classifies network records as normal or an attack, while the second stage further classifies any detected attacks into four categories (DoS, Probe, R2L, U2R) using separate layers. Experimental results on the NSL-KDD dataset show the layered approach using the JRip classifier achieved very high classification accuracy of over 99% for each attack category, outperforming existing approaches. The multi-layered model is effective for improving detection of minority attack classes without reducing performance on majority classes.
A survey of Network Intrusion Detection using soft computing Techniqueijsrd.com
with the impending era of internet, the network security has become the key foundation for lot of financial and business application. Intrusion detection is one of the looms to resolve the problem of network security. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. Here we propose a new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithm for higher rate of detection.
Layered Approach & HMM for Network Based Intrusion DectionIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PERFORMANCE EVALUATION OF DIFFERENT KERNELS FOR SUPPORT VECTOR MACHINE USED I...IJCNCJournal
The success of any Intrusion Detection System (IDS) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with numerous features. As a result, in order to
get rid of this problem, several types of intrusion detection methods with different levels of accuracy have been proposed which leads the choice of an effective and robust method for IDS as a very important topic in information security. In this regard, the support vector machine (SVM) has been playing an important role to provide potential solutions for the IDS problem. However, the practicability of introducing SVM is
affected by the difficulties in selecting appropriate kernel and its parameters. From this viewpoint, this paper presents the work to apply different kernels for SVM in ID Son the KDD’99 Dataset and NSL-KDD dataset as well as to find out which kernel is the best for SVM. The important deficiency in the KDD’99 data set is the huge number of redundant records as observed earlier. Therefore, we have derived a data set RRE-KDD by eliminating redundant record from KDD’99train and test dataset prior to apply different kernel for SVM. This RRE-KDD consists of both KDD99Train+ and KDD99 Test+ dataset for training and testing purposes, respectively. The way to derive RRE-KDD data set is different from that of NSL-KDD
data set. The experimental results indicate that Laplace kernel can achieve higher detection rate and lower false positive rate with higher precision than other kernel son both RRE-KDD and NSL-KDD datasets. It is also found that the performances of other kernels are dependent on datasets.
Randomness evaluation framework of cryptographic algorithmsijcisjournal
Nowadays, computer systems are developing very rapidly and become more and more complex, which
leads to the necessity to provide security for them. This paper is intended to present software for testing
and evaluating cryptographic algorithms. When evaluating block and stream ciphers one of the most basic
property expected from them is to pass statistical randomness testing, demonstrating in this way their
suitability to be random number generators. The primary goal of this paper is to propose a new framework
to evaluate the randomness of cryptographic algorithms: based only on a .dll file which offers access to the
encryption function, the decryption function and the key schedule function of the cipher that has to be tested
(block cipher or stream cipher), the application evaluates the randomness and provides an interpretation of
the results. For this, all nine tests used for evaluation of AES candidate block ciphers and three NIST
statistical tests are applied to the algorithm being tested. In this paper, we have evaluated Tiny Encryption
Algorithm (block cipher), Camellia (block cipher) and LEX (stream cipher) to determine if they pass
statistical randomness testing.
A Novel Classification via Clustering Method for Anomaly Based Network Intrus...IDES Editor
Intrusion detection in the internet is an active
area of research. Intruders can be classified into two
types, namely; external intruders who are unauthorized
users of the computers they attack, and internal
intruders, who have permission to access the system but
with some restrictions. The aim of this paper is to present
a methodology to recognize attacks during the normal
activities in a system. A novel classification via sequential
information bottleneck (sIB) clustering algorithm has
been proposed to build an efficient anomaly based
network intrusion detection model. We have compared
our proposed method with other clustering algorithms
like X-Means, Farthest First, Filtered clusters, DBSCAN,
K-Means, and EM (Expectation-Maximization)
clustering in order to find the suitability of our proposed
algorithm. A subset of KDDCup 1999 intrusion detection
benchmark dataset has been used for the experiment.
Results show that the proposed method is efficient in
terms of detection accuracy, low false positive rate in
comparison to the other existing methods.
Network Anomaly detection based on fuzzy logic and Genetic AlgorithmYatindra shashi
The document discusses different approaches for anomaly-based network intrusion detection systems (NIDS), including machine learning, fuzzy logic, and genetic algorithm approaches. Specifically, it summarizes:
1) Fuzzy logic based NIDS that uses a strategy to automatically generate fuzzy rules from training data to define normal network behavior and detect anomalies. Experiments using the KDD Cup 99 dataset showed it achieved 90% accuracy.
2) The use of genetic algorithms for NIDS, which encode network data as chromosomes and use selection/evolution principles to optimize detection of intrusions based on fitness metrics like packet drop rates.
3) Research applying fuzzy logic and genetic algorithm techniques to detect packet dropping attacks by malicious nodes in mobile ad hoc
This document presents a multi-classification approach for detecting network attacks using a layered model. The proposed system consists of two stages - the first stage classifies network records as normal or an attack, while the second stage further classifies any detected attacks into four categories (DoS, Probe, R2L, U2R) using separate layers. Experimental results on the NSL-KDD dataset show the layered approach using the JRip classifier achieved very high classification accuracy of over 99% for each attack category, outperforming existing approaches. The multi-layered model is effective for improving detection of minority attack classes without reducing performance on majority classes.
A survey of Network Intrusion Detection using soft computing Techniqueijsrd.com
with the impending era of internet, the network security has become the key foundation for lot of financial and business application. Intrusion detection is one of the looms to resolve the problem of network security. An Intrusion Detection System (IDS) is a program that analyses what happens or has happened during an execution and tries to find indications that the computer has been misused. Here we propose a new approach by utilizing neuro fuzzy and support vector machine with fuzzy genetic algorithm for higher rate of detection.
Layered Approach & HMM for Network Based Intrusion DectionIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
A new clutering approach for anomaly intrusion detectionIJDKP
Recent advances in technology have made our work easier compare to earlier times. Computer network is
growing day by day but while discussing about the security of computers and networks it has always been a
major concerns for organizations varying from smaller to larger enterprises. It is true that organizations
are aware of the possible threats and attacks so they always prepare for the safer side but due to some
loopholes attackers are able to make attacks.
Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms
for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting
possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion
detection by using the approach of K-medoids method of clustering and its certain modifications. The
proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
The document discusses feature selection and classification methods for detecting denial-of-service (DoS) attacks in intrusion detection systems. It proposes using Random Forests for feature selection and k-Nearest Neighbors for classification on the KDD99 dataset. Experimental results show that the proposed approach of using Random Forests to select important features before classifying with k-Nearest Neighbors increases detection accuracy while decreasing false positives compared to other algorithms.
Hardware Implementation of Algorithm for Cryptanalysisijcisjournal
Cryptanalysis of block ciphers involves massive computations which are independent of each other and can
be instantiated simultaneously so that the solution space is explored at a faster rate. With the advent of low
cost Field Programmable Gate Arrays (FPGA’s), building special purpose hardware for computationally
intensive applications has now become possible. For this the Data Encryption Standard (DES) is used as a
proof of concept. This paper presents the design for Hardware implementation of DES cryptanalysis on
FPGA using exhaustive key search. Two architectures viz. Rolled and Unrolled DES architecture are compared
and based on experimental result the Rolled architecture is implemented on FPGA. The aim of this
work is to make cryptanalysis faster and better.
IRJET- Study and Performance Evaluation of Different Symmetric Key Crypto...IRJET Journal
This document summarizes a study that evaluates the performance of four symmetric key cryptography algorithms: DES, 3DES, Blowfish, and AES. The study considers criteria like file size, file type, encryption and decryption time, and block size. It finds that Blowfish has the best performance, encrypting and decrypting data faster than the other algorithms. AES also performs well, while 3DES has the lowest performance due to its longer key length. The document reviews related literature comparing the performance of symmetric key cryptography algorithms and techniques that combine cryptography with steganography for enhanced security.
Secure Checkpointing Approach for Mobile Environmentidescitation
The document describes a secure checkpointing approach for mobile environments. It proposes using elliptic curve cryptography combined with checkpointing to provide a low overhead, secure, fault tolerant system. Key points:
- Checkpointing is used to save system states to allow recovery from failures. Elliptic curve cryptography provides security by encrypting communication and generating digital signatures.
- The approach shifts cryptographic calculations to base stations to reduce mobile node overhead. Checkpoints and recovery information are stored at base stations.
- Mobile nodes save checkpoints and transfer them to the current base station they are connected to. A recovery algorithm allows processes to rollback and resume from the last saved checkpoint if a failure occurs.
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
Detection of Replica Nodes in Wireless Sensor Network: A SurveyIOSR Journals
This document summarizes research on detecting replica nodes in wireless sensor networks. It first discusses how adversaries can create replica nodes by compromising sensor nodes and stealing their keying materials. It then reviews various approaches for replica node detection, including using tamper-resistant hardware, randomized efficient distributed protocols, and leveraging knowledge of sensor deployment locations. The document proposes a new scheme using sequential probability ratio testing to detect mobile replica nodes based on analyzing the speeds between consecutive location claims from sensor nodes. This approach aims to quickly detect replica nodes compared to existing techniques.
Secured Paillier Homomorphic Encryption Scheme Based on the Residue Number Sy...ijcisjournal
In this paper, we present an improved Paillier Cryptosystem for a secured data transmission based on the
Residue Number System (RNS). The current state of Paillier Cryptosystem allows the computation of the
plaintext from the cipher text without solving its security assumption of Decisional Composite Residuosity
or the knowledge of its private keys under mathematical attacks
This document provides an overview of Mahdi Hosseini Moghaddam's background and work applying machine learning and cognitive computing for intrusion detection. It discusses his education in computer science and engineering and awards. It then outlines the goals of the presentation to discuss real-world applications of machine learning rather than scientific details. The document proceeds to discuss problems with current intrusion detection systems, introduce concepts in machine learning and cognitive computing, and describe Mahdi's methodology and architecture for a hardware-based machine learning system using a cognitive processor to enable fast intrusion detection.
TOWARDS A DEEPER NTRU ANALYSIS: A MULTI MODAL ANALYSISijcisjournal
NTRU is being considered as part of the NIST quantum resistant cryptography standard. While NIST has received substantial attention in the literature, more analysis is needed. This current study uses a unique approach. The team of researchers divided into -sub-teams. Each is using a separate analysis technique on NTRU. Then the diverse -sub-teams' work was brought together into a single cohesive statistical analysis to provide well-founded conclusions regarding NTRU.
Lightweight secure scheme for detecting provenance forgery and packet drop at...Pvrtechnologies Nellore
The document proposes a lightweight secure scheme to detect packet forgery and loss attacks in wireless sensor networks. It relies on in-packet Bloom filters to encode data provenance. The scheme introduces efficient mechanisms for provenance verification and reconstruction at the base station. Key generation, encryption, and signature techniques are used to securely transmit provenance information and detect suspicious data packets. The proposed technique is evaluated analytically and empirically, proving its effectiveness and efficiency in detecting attacks.
This document summarizes a research paper that proposes a new block cipher cryptographic symmetric key algorithm called the "TACIT Encryption Technique." The algorithm uses a unique mathematical logic approach along with a new key distribution system. It is implemented in a hardware chip using VHDL programming language on a Network-on-Chip model for secure data transmission. Experimental results show encryption and decryption of 128-bit blocks using an 8-bit key through simulation in Modelsim. The algorithm aims to provide better security, speed and flexibility than existing algorithms like DES, AES and RSA.
The document discusses intrusion alert correlation. It defines key terms like correlation, event, alert, and alert correlation. It outlines that the goals of correlation are to address weaknesses in individual intrusion detection systems like alert flooding, lack of context, and false positives/negatives. The main steps of the correlation process include alert collection, normalization, aggregation, verification, and producing high-level alert structures. Specific correlation techniques are also discussed.
A lightweight secure scheme for detecting provenance forgery and packet drop ...Pvrtechnologies Nellore
This document proposes a lightweight scheme for securely transmitting provenance (data history) in wireless sensor networks to detect packet forgery and loss attacks. It introduces an in-packet Bloom filter technique to encode provenance within each data packet in an efficient way. As sensor nodes forward packets, they embed their node IDs into the Bloom filter to record the forwarding path. The base station can then extract and verify the provenance from the Bloom filter to identify compromised or malicious nodes that drop packets. The scheme aims to securely transmit provenance using only low-cost operations like hash functions and message authentication codes, while previous solutions used intensive cryptography. It is evaluated analytically and experimentally to prove its effectiveness and efficiency.
A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet Drop ...1crore projects
This document proposes a lightweight scheme for securely transmitting provenance information in wireless sensor networks. It uses Bloom filters to encode provenance data within data packets in an efficient manner. The scheme allows a base station to extract and verify provenance upon receiving packets, and detect if packet drop attacks occurred. The proposed technique is evaluated analytically and experimentally, demonstrating its effectiveness and efficiency compared to traditional provenance security solutions.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document proposes a new Randomized, Efficient, and Distributed (RED) protocol for detecting clone attacks in wireless sensor networks. It analyzes existing solutions and identifies their shortcomings. The key contributions are:
1) Analyzing desirable properties for distributed clone detection mechanisms.
2) Showing that existing solutions like LSM do not fully meet these requirements.
3) Proposing the new RED protocol and proving it satisfies the requirements. Extensive simulations show RED is more efficient and effective at clone detection than other distributed protocols.
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
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.
The Effect of insertion of different geometries on heat transfer performance ...IJMER
Under turbulent flow conditions, the increase in heat transfer rate is more significant than
that under laminar flow conditions. The turbulent effects become a dominant factor over secondary flow
at higher Reynolds number. The turbulent flow can be produced by inserting different geometries in the
circular pipe. This study focuses on the various methods or geometries used to produce turbulent
geometries and its effect on the heat transfer. The turbulent generators with different geometrical
configurations have been used as one of the passive heat transfer enhancement techniques and are the
most widely used in tubes in several heat transfer applications. Insertion of such geometries may lead to
increase the friction factor and pressure drop which directly enhances the heat transfer characteristics.
Intrusion Detection and Forensics based on decision tree and Association rule...IJMER
This paper present an approach based on the combination of, two techniques using
decision tree and Association rule mining for Probe attack detection. This approach proves to be
better than the traditional approach of generating rules for fuzzy expert system by clustering methods.
Association rule mining for selecting the best attributes together and decision tree for identifying the
best parameters together to create the rules for fuzzy expert system. After that rules for fuzzy expert
system are generated using association rule mining and decision trees. Decision trees is generated for
dataset and to find the basic parameters for creating the membership functions of fuzzy inference
system. Membership functions are generated for the probe attack. Based on these rules we have
created the fuzzy inference system that is used as an input to neuro-fuzzy system. Fuzzy inference
system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS structure is generated for
outcome of neuro-fuzzy approach. The experiments and evaluations of the proposed method were
done with NSL-KDD intrusion detection dataset. As the experimental results, the proposed approach
based on the combination of, two techniques using decision tree and Association rule mining
efficiently detected probe attacks. Experimental results shows better results for detecting intrusions as
compared to others existing methods
A new clutering approach for anomaly intrusion detectionIJDKP
Recent advances in technology have made our work easier compare to earlier times. Computer network is
growing day by day but while discussing about the security of computers and networks it has always been a
major concerns for organizations varying from smaller to larger enterprises. It is true that organizations
are aware of the possible threats and attacks so they always prepare for the safer side but due to some
loopholes attackers are able to make attacks.
Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms
for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting
possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion
detection by using the approach of K-medoids method of clustering and its certain modifications. The
proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means
algorithm.
The document discusses feature selection and classification methods for detecting denial-of-service (DoS) attacks in intrusion detection systems. It proposes using Random Forests for feature selection and k-Nearest Neighbors for classification on the KDD99 dataset. Experimental results show that the proposed approach of using Random Forests to select important features before classifying with k-Nearest Neighbors increases detection accuracy while decreasing false positives compared to other algorithms.
Hardware Implementation of Algorithm for Cryptanalysisijcisjournal
Cryptanalysis of block ciphers involves massive computations which are independent of each other and can
be instantiated simultaneously so that the solution space is explored at a faster rate. With the advent of low
cost Field Programmable Gate Arrays (FPGA’s), building special purpose hardware for computationally
intensive applications has now become possible. For this the Data Encryption Standard (DES) is used as a
proof of concept. This paper presents the design for Hardware implementation of DES cryptanalysis on
FPGA using exhaustive key search. Two architectures viz. Rolled and Unrolled DES architecture are compared
and based on experimental result the Rolled architecture is implemented on FPGA. The aim of this
work is to make cryptanalysis faster and better.
IRJET- Study and Performance Evaluation of Different Symmetric Key Crypto...IRJET Journal
This document summarizes a study that evaluates the performance of four symmetric key cryptography algorithms: DES, 3DES, Blowfish, and AES. The study considers criteria like file size, file type, encryption and decryption time, and block size. It finds that Blowfish has the best performance, encrypting and decrypting data faster than the other algorithms. AES also performs well, while 3DES has the lowest performance due to its longer key length. The document reviews related literature comparing the performance of symmetric key cryptography algorithms and techniques that combine cryptography with steganography for enhanced security.
Secure Checkpointing Approach for Mobile Environmentidescitation
The document describes a secure checkpointing approach for mobile environments. It proposes using elliptic curve cryptography combined with checkpointing to provide a low overhead, secure, fault tolerant system. Key points:
- Checkpointing is used to save system states to allow recovery from failures. Elliptic curve cryptography provides security by encrypting communication and generating digital signatures.
- The approach shifts cryptographic calculations to base stations to reduce mobile node overhead. Checkpoints and recovery information are stored at base stations.
- Mobile nodes save checkpoints and transfer them to the current base station they are connected to. A recovery algorithm allows processes to rollback and resume from the last saved checkpoint if a failure occurs.
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
Detection of Replica Nodes in Wireless Sensor Network: A SurveyIOSR Journals
This document summarizes research on detecting replica nodes in wireless sensor networks. It first discusses how adversaries can create replica nodes by compromising sensor nodes and stealing their keying materials. It then reviews various approaches for replica node detection, including using tamper-resistant hardware, randomized efficient distributed protocols, and leveraging knowledge of sensor deployment locations. The document proposes a new scheme using sequential probability ratio testing to detect mobile replica nodes based on analyzing the speeds between consecutive location claims from sensor nodes. This approach aims to quickly detect replica nodes compared to existing techniques.
Secured Paillier Homomorphic Encryption Scheme Based on the Residue Number Sy...ijcisjournal
In this paper, we present an improved Paillier Cryptosystem for a secured data transmission based on the
Residue Number System (RNS). The current state of Paillier Cryptosystem allows the computation of the
plaintext from the cipher text without solving its security assumption of Decisional Composite Residuosity
or the knowledge of its private keys under mathematical attacks
This document provides an overview of Mahdi Hosseini Moghaddam's background and work applying machine learning and cognitive computing for intrusion detection. It discusses his education in computer science and engineering and awards. It then outlines the goals of the presentation to discuss real-world applications of machine learning rather than scientific details. The document proceeds to discuss problems with current intrusion detection systems, introduce concepts in machine learning and cognitive computing, and describe Mahdi's methodology and architecture for a hardware-based machine learning system using a cognitive processor to enable fast intrusion detection.
TOWARDS A DEEPER NTRU ANALYSIS: A MULTI MODAL ANALYSISijcisjournal
NTRU is being considered as part of the NIST quantum resistant cryptography standard. While NIST has received substantial attention in the literature, more analysis is needed. This current study uses a unique approach. The team of researchers divided into -sub-teams. Each is using a separate analysis technique on NTRU. Then the diverse -sub-teams' work was brought together into a single cohesive statistical analysis to provide well-founded conclusions regarding NTRU.
Lightweight secure scheme for detecting provenance forgery and packet drop at...Pvrtechnologies Nellore
The document proposes a lightweight secure scheme to detect packet forgery and loss attacks in wireless sensor networks. It relies on in-packet Bloom filters to encode data provenance. The scheme introduces efficient mechanisms for provenance verification and reconstruction at the base station. Key generation, encryption, and signature techniques are used to securely transmit provenance information and detect suspicious data packets. The proposed technique is evaluated analytically and empirically, proving its effectiveness and efficiency in detecting attacks.
This document summarizes a research paper that proposes a new block cipher cryptographic symmetric key algorithm called the "TACIT Encryption Technique." The algorithm uses a unique mathematical logic approach along with a new key distribution system. It is implemented in a hardware chip using VHDL programming language on a Network-on-Chip model for secure data transmission. Experimental results show encryption and decryption of 128-bit blocks using an 8-bit key through simulation in Modelsim. The algorithm aims to provide better security, speed and flexibility than existing algorithms like DES, AES and RSA.
The document discusses intrusion alert correlation. It defines key terms like correlation, event, alert, and alert correlation. It outlines that the goals of correlation are to address weaknesses in individual intrusion detection systems like alert flooding, lack of context, and false positives/negatives. The main steps of the correlation process include alert collection, normalization, aggregation, verification, and producing high-level alert structures. Specific correlation techniques are also discussed.
A lightweight secure scheme for detecting provenance forgery and packet drop ...Pvrtechnologies Nellore
This document proposes a lightweight scheme for securely transmitting provenance (data history) in wireless sensor networks to detect packet forgery and loss attacks. It introduces an in-packet Bloom filter technique to encode provenance within each data packet in an efficient way. As sensor nodes forward packets, they embed their node IDs into the Bloom filter to record the forwarding path. The base station can then extract and verify the provenance from the Bloom filter to identify compromised or malicious nodes that drop packets. The scheme aims to securely transmit provenance using only low-cost operations like hash functions and message authentication codes, while previous solutions used intensive cryptography. It is evaluated analytically and experimentally to prove its effectiveness and efficiency.
A Lightweight Secure Scheme for Detecting Provenance Forgery and Packet Drop ...1crore projects
This document proposes a lightweight scheme for securely transmitting provenance information in wireless sensor networks. It uses Bloom filters to encode provenance data within data packets in an efficient manner. The scheme allows a base station to extract and verify provenance upon receiving packets, and detect if packet drop attacks occurred. The proposed technique is evaluated analytically and experimentally, demonstrating its effectiveness and efficiency compared to traditional provenance security solutions.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document proposes a new Randomized, Efficient, and Distributed (RED) protocol for detecting clone attacks in wireless sensor networks. It analyzes existing solutions and identifies their shortcomings. The key contributions are:
1) Analyzing desirable properties for distributed clone detection mechanisms.
2) Showing that existing solutions like LSM do not fully meet these requirements.
3) Proposing the new RED protocol and proving it satisfies the requirements. Extensive simulations show RED is more efficient and effective at clone detection than other distributed protocols.
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
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.
The Effect of insertion of different geometries on heat transfer performance ...IJMER
Under turbulent flow conditions, the increase in heat transfer rate is more significant than
that under laminar flow conditions. The turbulent effects become a dominant factor over secondary flow
at higher Reynolds number. The turbulent flow can be produced by inserting different geometries in the
circular pipe. This study focuses on the various methods or geometries used to produce turbulent
geometries and its effect on the heat transfer. The turbulent generators with different geometrical
configurations have been used as one of the passive heat transfer enhancement techniques and are the
most widely used in tubes in several heat transfer applications. Insertion of such geometries may lead to
increase the friction factor and pressure drop which directly enhances the heat transfer characteristics.
Intrusion Detection and Forensics based on decision tree and Association rule...IJMER
This paper present an approach based on the combination of, two techniques using
decision tree and Association rule mining for Probe attack detection. This approach proves to be
better than the traditional approach of generating rules for fuzzy expert system by clustering methods.
Association rule mining for selecting the best attributes together and decision tree for identifying the
best parameters together to create the rules for fuzzy expert system. After that rules for fuzzy expert
system are generated using association rule mining and decision trees. Decision trees is generated for
dataset and to find the basic parameters for creating the membership functions of fuzzy inference
system. Membership functions are generated for the probe attack. Based on these rules we have
created the fuzzy inference system that is used as an input to neuro-fuzzy system. Fuzzy inference
system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS structure is generated for
outcome of neuro-fuzzy approach. The experiments and evaluations of the proposed method were
done with NSL-KDD intrusion detection dataset. As the experimental results, the proposed approach
based on the combination of, two techniques using decision tree and Association rule mining
efficiently detected probe attacks. Experimental results shows better results for detecting intrusions as
compared to others existing methods
Design and Development of Gate Signal for 36 Volt 1000Hz Three Phase InverterIJMER
The sinusoidal PWM gating signals generation is most popular PWM method, which reduce
harmonic reduction in output. SPWM can be generated by FPGA, micro controller and micro processor but
this kind of device needs programming and coding hence avoided in using power system of aircraft. This
paper present an experiment using SPWM method to generate 1000 Hz gating signals suitable for 36 Volt ,
1000 Hz, 3 phase, three wire supply. Discrete components design approach is chosen to provide noise
immunity at higher amplitude level of signal and a large flexibility to adjust and process various operating
parameters of signals. The circuit is proved with commercial components however MIL version of
components can be easily incorporated in design in later stage
This document analyzes public interest and industry satisfaction regarding houses on stilts in Woloan, Tomohon, Indonesia. It uses an Importance Performance Analysis tool to measure satisfaction and importance among the industrial community and those living nearby. The results indicate attention is needed to improve attributes like industrial area conditions, drainage infrastructure, waste treatment, and communications networks. For nearby residents, drainage, waste management, pollution, and community impacts need improvement. The study aims to assess clean production applied in construction to make it more cost-effective and environmentally friendly.
This document summarizes a study on the effect of fuel and air injection patterns on combustion dynamics in confined and free diffusion flames. A new burner design called the circumferential alternative air and fuel burner (CAFB) was developed that allows for swirling and circumferentially alternating fuel and air injection. Experiments were conducted to examine the effects of varying the injection angle of the fuel and air jets from 0 to 60 degrees. Flame characteristics such as height, temperature distribution, and emissions were measured. The results showed that increasing the injection angle improved mixing and generated a more intense combustion zone, shortening the flame length. A new "injection swirl number" parameter was introduced to characterize the burner based on the injection angle.
This document summarizes a study on the performance and emissions of a single cylinder diesel engine operating with biodiesel-diesel blends containing 10% isobutanol additive at different injection pressures ranging from 200 to 275 bars. The following key points are made:
1. Brake thermal efficiency improved up to an injection pressure of 250 bars but decreased slightly at 275 bars. Emissions of carbon monoxide and smoke were significantly reduced with the blends while nitrogen oxides increased marginally.
2. Cylinder pressure was lower for the blends compared to diesel at all injection pressures due to the lower cetane number and heating value of isobutanol.
3. Brake specific fuel consumption
The document describes a fuzzy logic hysteresis current controlled shunt active power filter (SAPF) for power quality improvement. A SAPF uses a voltage source inverter to inject compensating currents to correct power factor, balance loads, and eliminate harmonics. The paper compares a conventional PI controller to a fuzzy logic controller for regulating the DC bus voltage and generating PWM signals. Simulation results using Matlab/Simulink show the fuzzy controller has better dynamic performance than the PI controller, maintaining steady state conditions and responding faster to transients or load changes.
The document provides a review of the Kindle Fire HD 8.9 tablet. It summarizes the tablet's hardware specifications including its 1920x1200 resolution screen, dual-core processor, 1GB of RAM, 32GB of storage, and 1.3MP camera. It discusses the tablet's picture quality, performance, battery life, software experience, and overall value. An additional video review is also embedded. The reviewer gives positive feedback and recommends the tablet for reading, watching videos, playing games and general media consumption, especially at its $300 price point.
Token Based Packet Loss Control Mechanism for NetworksIJMER
This document summarizes a research paper that proposes a new congestion control mechanism using tokens. It begins with background on congestion control and modern IP networks. The proposed approach uses edge and core routers to write quality of service measures in packet headers as tokens. Tokers are interpreted by routers to gauge congestion, especially at edge routers. Based on tokens, edge routers can shape traffic from sources to reduce congestion. The mechanism aims to provide fairness while controlling packet loss. Key aspects discussed include stable token limit congestion control, core routers, edge routers, and how the approach compares to related work like CSFQ.
Hopf-Bifurcation Ina Two Dimensional Nonlinear Differential EquationIJMER
This document discusses Hopf bifurcation in a two-dimensional nonlinear differential equation. It contains the following key points:
1. The paper investigates the stability of Hopf bifurcation in a two-dimensional nonlinear differential equation, finding both supercritical and subcritical Hopf bifurcation depending on parameter values.
2. The center manifold theorem and normal forms are used to study the behavior of limit cycles created or destroyed through Hopf bifurcations.
3. Hopf bifurcation refers to the creation or destruction of a periodic solution emanating from an equilibrium point as a parameter crosses a critical value. It is important for studying oscillatory behavior in nonlinear systems.
A Unique Application to Reserve Doctor Appointment by Using Wireless Applicat...IJMER
WAP is a standardized technology for cross-platform, distributed computing, very similar to the Internet’s combination of Hypertext Markup Language (HTML) and Hypertext Transfer Protocol (HTTP). WAP could be described as a set of protocols that has inherited its characteristics and functionality from Internet standards and from standards developed for wireless services by some of the world’s leading companies in the business of wireless telecommunications. This application will help patients, the normal doctor and the medical director. The patient can reserve an appointment. The normal doctor can view and print the lists of patient appointment under his responsibility. The medical director can add new departments, add new doctors, and also can change the password to access the database. He can also modify data and working schedules of doctors assigned. He can add new patients and can have privilege access to transfer any patient appointment to another doctor. This Application which has been developed by using WAP was the first of its kind here, where software has been developed.
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
Application of Neuro-Fuzzy System to Evaluate Sustainability in Highway DesignIJMER
1. The document describes using an adaptive neuro-fuzzy inference system (ANFIS) to evaluate the sustainability of highway design projects in Thailand.
2. ANFIS uses 60 input variables across 14 activities associated with highway design, including geometrics, earthworks, pavement, etc. to evaluate sustainability.
3. The ANFIS model was trained on data from 50 highway design scenarios rated by a decision team. It was then tested on the remaining 15 scenarios to validate the model's ability to assess sustainability. The ANFIS approach provided reasonably accurate sustainability evaluations compared to expert ratings.
This document analyzes drought conditions in the Gagar watershed located in Uttarakhand, India based on weekly rainfall data from 1998-2007. Key findings include:
1) About 35% of months during this period experienced drought conditions according to the defined criteria. Drought months were most common in October-May prior to the monsoon season.
2) 20% of years (1999 and 2001) received annual rainfall more than one standard deviation below the mean and were classified as drought years.
3) The Rabi crop season (October-February) experienced drought in 48% of months, indicating a risk of crop failure under rainfed conditions during this period.
This document summarizes research on the analysis of swing jaw plates in jaw crushers. It discusses various studies that have performed kinematic and dynamic analysis to better understand stresses on plates and improve plate design. Researchers have analyzed plate deformation under loading, modeled plate-particle interaction, incorporated plate stiffeners, and performed 3D modeling and simulation to optimize crusher design and reduce energy consumption. Understanding the kinetic characteristics of plates is important for designing crushers suited for specific rock types and applications.
Signal Processing Algorithm of Space Time Coded Waveforms for Coherent MIMO R...IJMER
ABSTRACT: Space-time coding (STC) has been shown to play a key role in the design of MIMO radars with closely
spaced antennas. Multiple-input–multiple-output (MIMO) radar is emerging technology for target detection, parameter
identification, and target classification due to diversity of waveform and perspective. First, it turns out that a joint waveform
optimization problem can be decoupled into a set of individual waveform design problems. Second, a number of mono-static
waveforms can be directly used in a MIMO radar system, which offers flexibility in waveform selection. We provide
conditions for the elimination of waveform cross correlation. However, the mutual interference among the waveforms may
lead to performance degradation in resolving spatially close returns. We consider the use of space–time coding (STC) to
mitigate the waveform cross-correlation effects in MIMO radar. In addition, we also extend the model to partial waveform
cross-correlation removal based on waveform set division. Numerical results demonstrate the effectiveness of STC in MIMO
radar for waveform de-correlation. This paper introduces the signal processing issued for the coherent MIMO radar without
and with STC waveforms and also studied signal processing algorithms of coherent MIMO radar with STC waveforms for
improvement of target detection and recognition performance for real life scenario.
Keywords: STC, coherent, Probability detection, MIMO and SNR.
A combined approach to search for evasion techniques in network intrusion det...eSAT Journals
Abstract Network Intrusion Detection Systems (NIDS) whose base is signature, works on the signature of attacks. They must be updated quickly in order to prevent the system from new attacks. The attacker finds out new evasion techniques so that he should remain undetected. As the new evasion techniques are being developed it becomes difficult for NIDS to give accurate results and NIDS may fail. The key aspect of our paper is to develop a network intrusion detection system using C4.5 algorithm where Adaboost algorithm is used to classify the packet as normal packet or attack packet and also to further classify different types of attack. Apriori algorithm is used to find real time evasion and to generate rules to find intrusion These rules are further given as input to Snort intrusion detection system for detecting different attacks. Keywords: NIDS, Evasion, Apriori Algorithm, Adaboost Algorithm, Snort
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.
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.
The document discusses using machine learning algorithms like Random Forest and k-Nearest Neighbors for intrusion detection. It analyzes the KDD Cup 1999 intrusion detection dataset to classify network traffic as normal or different types of attacks. The proposed model uses Random Forest for feature selection and k-Nearest Neighbors for classification to more accurately detect known and unknown attacks. Experimental results show the combined approach achieves better detection rates than other algorithms alone, especially for novel attacks not in the training data. Further combining the algorithms into a two-stage model is suggested to improve performance.
The document discusses using machine learning algorithms like Random Forest and k-Nearest Neighbors for intrusion detection. It analyzes the KDD Cup 1999 intrusion detection dataset to classify network traffic as normal or different types of attacks. The proposed model uses Random Forest for feature selection and k-Nearest Neighbors for classification to more accurately detect known and unknown attacks. Experimental results show the combined approach achieves better detection rates than other algorithms alone, especially for novel attacks not present in training data. Further combining the algorithms into a two-stage process may yield even higher accuracy.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
An Approach of Automatic Data Mining Algorithm for Intrusion Detection and P...IOSR Journals
This document summarizes an approach for using data mining algorithms to detect network intrusions and prevent security threats. It analyzes two datasets - one containing 997 records and another containing 11,438 records - using various classification algorithms in Weka to determine the best performing ones. The algorithms examined include PART, SMO, HyperPipes, Filtered Classifier, Random Forest, Naive Bayes Updateable and KStar. Classification rate and false positive rate are used to evaluate performance. The document also discusses related work on intrusion detection using neural networks, genetic algorithms and other approaches.
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.
IRJET- Review on Intrusion Detection System using Recurrent Neural Network wi...IRJET Journal
This document summarizes research on using recurrent neural networks and deep learning for intrusion detection. It first provides background on intrusion detection systems and their types. It then discusses how deep learning approaches like recurrent neural networks can help address limitations of traditional machine learning methods for intrusion detection. The document reviews several related studies on intrusion detection using techniques like autoencoders, decision trees, and LSTM-RNNs. It proposes using a recurrent neural network model trained on the NSL-KDD dataset for intrusion detection, claiming this deep learning approach can improve classification accuracy compared to traditional machine learning methods.
2 14-1346479656-1- a study of feature selection methods in intrusion detectio...Dr. Amrita .
This document provides a survey of feature selection methods for intrusion detection systems. It discusses three categories of feature selection approaches: filter, wrapper, and hybrid. The KDD Cup 99 dataset is commonly used to evaluate feature selection methods, which contains 41 features divided into four categories. Performance is typically evaluated based on metrics like detection rate, false alarm rate, and accuracy rates derived from a confusion matrix. Feature selection aims to select a minimal subset of relevant features to improve detection performance while reducing computational requirements.
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.
This document presents a proposed hybrid intrusion detection system that combines k-means clustering, k-nearest neighbor classification, and decision table majority rule-based approaches. The system is evaluated on the KDD-99 dataset to detect intrusions and classify them into four categories: R2L, DoS, Probe, and U2R. The goal is to decrease the false alarm rate and increase accuracy and detection rate compared to existing intrusion detection systems. The proposed system applies k-means clustering first, then k-nearest neighbor classification, and finally decision table majority rules. Results show the proposed hybrid approach improves performance metrics compared to existing techniques.
A Comparative Study of Deep Learning Approaches for Network Intrusion Detecti...IRJET Journal
This document presents a comparative study of deep learning approaches for network intrusion detection. It employs deep neural networks to predict attacks on network intrusion detection systems using the KDD Cup-99 dataset. A DNN with 3 layers demonstrated superior performance compared to other machine learning algorithms and DNNs with varying layers. The study finds that deep learning techniques can function at a superhuman level when combined with intrusion detection systems due to their ability to adapt to new data and detect novel attacks.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
This document discusses using a random forest classifier with feature selection to improve intrusion detection. It begins with background on intrusion detection systems and challenges. It then proposes using genetic algorithms for feature selection to identify the most important features from a dataset. A random forest classifier is used for classification, which combines decision trees to improve accuracy. The methodology involves feature selection, classification with random forest, and detection. Feature weights are calculated and cross-validation is used to analyze detection rates for individual attacks. The goal is to improve accuracy, reduce training time, and better detect minority attacks through this approach.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
SURVEY OF NETWORK ANOMALY DETECTION USING MARKOV CHAINijcseit
Recently an internet threat has been increased. Our motive is detect the intrusion in the network in concise.
The real time issue such as DoS attack in banking, companies, industries and organization have been
increased significantly IDS has been used in both server and host side. The major challenge is to effectively
predict the periods of threats and protect the server from the unauthorized user. In this study, a novel
probabilistic approach is proposed effectively to detect the network intrusions. It uses a Markov chain for
probabilistic modelling of abnormal events in network systems. The degree of abnormality of the incoming
data is performed on the basis of the network states.
Survey of network anomaly detection using markov chainijcseit
This document summarizes research on using Markov chain models for anomaly detection in network intrusion detection systems. It first provides background on intrusion detection and different approaches. It then reviews several related works that use techniques like k-means clustering, decision trees, genetic algorithms, and Markov chains. The paper proposes using a k-means algorithm combined with a Markov chain model to detect attacks. The Markov chain model analyzes system state transitions over time to probabilistically model normal behavior and detect anomalies. The existing system has low detection and prevention rates. Future work could explore using eigenvectors and thresholds to improve performance.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Recently an internet threat has been increased. Our motive is detect the intrusion in the network in concise.
The real time issue such as DoS attack in banking, companies, industries and organization have been
increased significantly IDS has been used in both server and host side. The major challenge is to effectively
predict the periods of threats and protect the server from the unauthorized user. In this study, a novel
probabilistic approach is proposed effectively to detect the network intrusions. It uses a Markov chain for
probabilistic modelling of abnormal events in network systems. The degree of abnormality of the incoming
data is performed on the basis of the network states.
This document discusses using an improved ID3 algorithm with Havrda and Charvat entropy instead of Shannon entropy for intrusion detection and classification. The ID3 decision tree algorithm is used to build an effective decision tree from network data to detect known and unknown attacks. Experiments on the KDD99 intrusion detection dataset show the improved algorithm achieves over 97% detection accuracy while reducing false positives compared to other methods.
Similar to A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics (20)
A Study on Translucent Concrete Product and Its Properties by Using Optical F...IJMER
- Translucent concrete is a concrete based material with light-transferring properties,
obtained due to embedded light optical elements like Optical fibers used in concrete. Light is conducted
through the concrete from one end to the other. This results into a certain light pattern on the other
surface, depending on the fiber structure. Optical fibers transmit light so effectively that there is
virtually no loss of light conducted through the fibers. This paper deals with the modeling of such
translucent or transparent concrete blocks and panel and their usage and also the advantages it brings
in the field. The main purpose is to use sunlight as a light source to reduce the power consumption of
illumination and to use the optical fiber to sense the stress of structures and also use this concrete as an
architectural purpose of the building
Developing Cost Effective Automation for Cotton Seed DelintingIJMER
A low cost automation system for removal of lint from cottonseed is to be designed and
developed. The setup consists of stainless steel drum with stirrer in which cottonseeds having lint is mixed
with concentrated sulphuric acid. So lint will get burn. This lint free cottonseed treated with lime water to
neutralize acidic nature. After water washing this cottonseeds are used for agriculter purpose
Study & Testing Of Bio-Composite Material Based On Munja FibreIJMER
The incorporation of natural fibres such as munja fiber composites has gained
increasing applications both in many areas of Engineering and Technology. The aim of this study is to
evaluate mechanical properties such as flexural and tensile properties of reinforced epoxy composites.
This is mainly due to their applicable benefits as they are light weight and offer low cost compared to
synthetic fibre composites. Munja fibres recently have been a substitute material in many weight-critical
applications in areas such as aerospace, automotive and other high demanding industrial sectors. In
this study, natural munja fibre composites and munja/fibreglass hybrid composites were fabricated by a
combination of hand lay-up and cold-press methods. A new variety in munja fibre is the present work
the main aim of the work is to extract the neat fibre and is characterized for its flexural characteristics.
The composites are fabricated by reinforcing untreated and treated fibre and are tested for their
mechanical, properties strictly as per ASTM procedures.
Hybrid Engine (Stirling Engine + IC Engine + Electric Motor)IJMER
Hybrid engine is a combination of Stirling engine, IC engine and Electric motor. All these 3 are
connected together to a single shaft. The power source of the Stirling engine will be a Solar Panel. The aim of
this is to run the automobile using a Hybrid engine
Fabrication & Characterization of Bio Composite Materials Based On Sunnhemp F...IJMER
This document summarizes research on the fabrication and characterization of bio-composite materials using sunnhemp fibre. The document discusses how sunnhemp fibre was used to reinforce an epoxy matrix through hand lay-up methods. Various mechanical properties of the bio-composites were tested, including tensile, flexural, and impact properties. The results of the mechanical tests on the bio-composite specimens are presented. Potential applications of the sunnhemp fibre bio-composites are also suggested, such as in fall ceilings, partitions, packaging, automotive interiors, and toys.
Geochemistry and Genesis of Kammatturu Iron Ores of Devagiri Formation, Sandu...IJMER
The Greenstone belts of Karnataka are enriched in BIFs in Dharwar craton, where Iron
formations are confined to the basin shelf, clearly separated from the deeper-water iron formation that
accumulated at the basin margin and flanking the marine basin. Geochemical data procured in terms of
major, trace and REE are plotted in various diagrams to interpret the genesis of BIFs. Al2O3, Fe2O3 (T),
TiO2, CaO, and SiO2 abundances and ratios show a wide variation. Ni, Co, Zr, Sc, V, Rb, Sr, U, Th,
ΣREE, La, Ce and Eu anomalies and their binary relationships indicate that wherever the terrigenous
component has increased, the concentration of elements of felsic such as Zr and Hf has gone up. Elevated
concentrations of Ni, Co and Sc are contributed by chlorite and other components characteristic of basic
volcanic debris. The data suggest that these formations were generated by chemical and clastic
sedimentary processes on a shallow shelf. During transgression, chemical precipitation took place at the
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A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics
1. International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 5 | Iss.3| Mar. 2015 | 24|
A Technique by using Neuro-Fuzzy Inference System for
Intrusion Detection and Forensics
Abhishek choudhary1
, Swati Sharma2
, Pooja Gupta3
1
M .Tech (C.S) (Assistant Professor, Bhagwant University, Ajmer, India)
2
M .Tech (C.S) (Scholar, Bhagwant University, Ajmer, India)
3
M .Tech (C.S) (Scholar, Bhagwant University, Ajmer, India)
Department of Computer Sciences and Engineering Bhagwant University, Ajmer, India
I. Introduction
An Intrusion Detection System [1] [2] is a computer program that attempts to perform ID By either
misuse or anomaly detection, or a combination of techniques. Intrusion detection is the process of identifying
network activity that can lead to the compromise of a security policy. IDS’s are classified in many different
ways, including active and passive, network-based and host-based, and knowledge-based and behaviour-based.
Intrusion Detection System is a most essential part of the security infrastructure for the network connected to the
internet, because of the numerous way to comprise the stability and security of the network .Intrusion Detection
System [1] [2] can be used to monitor and analysis of network for unauthorized activity.
Forensic science [3] is based on a methodology composed by a group of stages, being the analysis one
of them. Analysis is responsible to determine when a data constitutes evidence; and as a consequence it can be
presented to a court. When the amount of data in a network is small, its analysis is relatively simple, but when it
is huge the data analysis becomes a challenge for the forensics expert
Decision tree [4] as a predictive model which maps observations about an item to conclusions about the
item's target value. Decision tree is used to data sheet to find suitable parameters. This technique is applied to
generate the rules for fuzzy expert system. This approach proves to be better than the traditional approach of
generating rules for fuzzy expert system.
Neuro-Fuzzy Inference System [5] combines the neural network with the fuzzy system, which
Implements the fuzzy inference system under the framework of neural network. Neuro-Fuzzy Inference System
has the ability to construct models solely based on the target system sample data.
The objective of this paper is to generate fuzzy rules for fuzzy expert system using a technique called
decision tree [4] for suitable parameters as the input of fuzzy interference System. The proposed system has
been testing using the NSL-KDD dataset [6] to find out the best suitable parameters. The NSL-KDD data set
suggested to solve some of the inherent problems of the KDDCUP'99 [7] data set. KDDCUP’99 is the mostly
widely used data set for Anomaly detection. But Tavallaee et al conducted a statistical analysis on this data set
and found two important issues that greatly affected the performance of evaluated systems, and results in a very
poor evaluation of anomaly detection approaches. To solve these issues, they proposed a new data set, NSL-
KDD, which consists of selected records of the complete KDD data set The NSL-KDD dataset which consist of
selected records of the complete KDD dataset.
The subsequent parts of this paper are organized as follows: At first, in section 2, the NSL-KDD
dataset on which experiments are conducted briefly reviewed. Next, at section 3 and 4, the proposed system is
ABSTRACT—This paper proposes a technique uses decision tree for dataset and to find the basic
parameters for creating the membership functions of fuzzy inference system for Intrusion Detection and
Forensics. Approach of generating rules using clustering methods is limited to the problems of
clustering techniques. To trait to solve this problem, several solutions have been proposed using
various Techniques. One such Technique is proposed to be applied here, for an analysis to Intrusion
Detection and Forensics. . Fuzzy Inference approach and decision algorithms are investigated in this
work. Decision tree is used to identify the parameters to create the fuzzy inference system. Fuzzy
inference system used as an input and the final ANFIS structure is generated for intrusion detection
and forensics. The experiments and evaluations of the proposed method were done with NSL-KDD
intrusion detection dataset.
Keywords—Intrusion detection, Forensics, Technique decision tree, NSL-KDD intrusion detection
dataset, decision tree algorithm, Fuzzy inference system, ANFIS approach.
2. A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 5 | Iss.3| Mar. 2015 | 25|
explained and experimental results are discussed respectively. Finally, section 5 makes some concluding
remarks and proposes further areas for research.
II. NSL-KDD Dataset
The NSL-KDD data includes 41 features and 5classes that are normal and 4 types of attacks: Dos,
Probe, R2L, and U2R. Denial of Service Attack (DoS) is an attack in which the attacker makes some computing
or memory resource too busy or too full to handle legitimate requests, or denies legitimate users access to a
machine. Probing Attack is an attempt to gather information about a network of computers for the apparent
purpose of circumventing
Its security controls. User to Root Attack (U2R) is a class of exploit in which the attacker starts out
with access to a normal user account on the system (perhaps gained by sniffing passwords, a dictionary attack,
or social engineering) and is able to exploit some vulnerability to gain root access to the system. Remote to
Local Attack (R2L) occurs [8] when an attacker who has the ability to send packets to a machine over a network
but who does not have an account on that machine exploits some vulnerability to gain local access as a user of
that machine.41 attributes are consisted three features: Basic features, Content features, and Traffic features.
Table 1 shows Features name and type of features.
TABLE I: SHOWS FEATURES NAME AND TYPE OF THE FEATURES
Type Features
Nominal Protocol type(2), Service(3), Flag(4)
Binary Land(7), logged in(12),
root shell(14), su_attempted(15),
is_host_login(21),, is_guest_login(22)
Numeric Duration(1), src_bytes(5),
dst_bytes(6), wrong fragment(8),
urgent(9), hot(10),
num_failed_logins(11),
num_compromised(13),
num_root(16),
num_file_creations(17),
num_shells(18),
num_access_files(19),
num_outbound_cmds(20), count(23)
srv_count(24), serror_rate(25),
srv_serror_rate(26), rerror_rate(27),
srv_rerror_rate(28), same_srv_rate(29)
diff_srv_rate(30),
srv_diff_host_rate(31),
dst_host_count(32),
dst_host_srv_count(33),
dst_host_same_srv_rate(34),
dst_host_diff_srv_rate(35),
dst_host_same_src_port_rate(36),
dst_host_srv_diff_host_rate(37),
dst_host_serror_rate(38),
dst_host_srv_serror_rate(39),
dst_host_rerror_rate(40),
dst_host_srv_rerror_rate(41)
III. Proposed System
The proposed system is discussed in details in this section. First, the proposed frame work is explained.
Then, data selected from KDD for training the system, are introduced. Afterward, layers of proposed framework
are presented in more details.
proposed framework
The integration of neural and fuzzy systems leads to a symbiotic relationship in which fuzzy systems
provide a powerful framework for expert knowledge representation, while neural networks provide learning
capabilities and exceptional suitability for computationally efficient hardware implementations. In our approach
3. A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 5 | Iss.3| Mar. 2015 | 26|
we are proposing a combination of these two standalone techniques as a t to efficiently detect the anomalous
behavior. The proposed framework is as follows:
Figure 1: Proposed work structure
NSL-KDD dataset is first loaded to Neuro-Fuzzy tool. Some of the attributes are removed and then all
the attributes are normalized in the range of 0-1 so that the fuzzy decisions can be made. After that rules for fuzzy
expert system are generated using association rule mining and decision trees. We used WEKA tool to generate
decision trees for dataset and to find the basic parameters for creating the membership functions of fuzzy
inference system. Based on these rules we have created the fuzzy inference system that is used as an input to
neuro-fuzzy system and the final NFIS structure is generated. Neuro-Fuzzy system results in accuracy in
detecting the probe attack.
3.2 Data Sources
NSL-KDD consists of selected records of the complete KDD data set and is publicly available for
researchers. In each connection there are 41 attributes describing different features of the connection and a label
assigned to each either as an attack type or as normal. Table II shows all the 41 attributes present in the NSL-
KDD dataset with their description as well.
TABLE II: LIST OF NSL-KDD DATASET FEATURES WITH THEIR DESCRIPTION
No Feature Description
1 Duration Duration of the connection
2 protocol_type
Connection protocol (e.g.
TCP,UDP, ICMP)
3 service Destination service
4 flag Status flag of the connection
5 src_byte
Bytes sent from source to
destination
6 dst_byte
Bytes sent from destination to
source
7 land
1 if connection is from/to the
same host/port; 0 otherwise
8 wrong_fragment Number of wrong fragments
9 urgent Number of urgent packets
10 hot Number of “hot” indicators
11 num_failed_login Number of failed logins
12 logged in
1 if successfully logged in; 0
otherwise
13 num_compromised
Number of “compromised”
conditions
4. A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 5 | Iss.3| Mar. 2015 | 27|
14 root_shell
1 if root shell is obtained; 0
otherwise
15 su_attempted
1 if “su root” command attempted;
0 otherwise
16 num_root Number of “root” accesses
17 num_file_creation Number of file creation operations
18 num_shells Number of shell prompts
19 num_access_file
Number of operations on access
control files
20 num_outbound cmds
Number of outbound commands
in a ftp session
21 is_host_login
1 if login belongs to the “hot” list;
0 otherwise
22 is_gust_login
1 if the login is the “guest” login;
0 otherwise
23 count
Number of connections to the
same host as the current
connection in the past 2 seconds
24 srv_count
Number of connections to the
same service as the current
connection in the past two seconds
25 serror_rate
% of connections that have
“SYN” errors
26 srv_serror_rate
% of connections that have
“SYN” errors
27 rerror_rate
% of connections that have REJ
errors
28 srv_rerror_rate
% of connections that have REJ
errors
29 same_srv_rate
% of connections to the same
service
30 diff_srv_rate
% of connections to different
services
31 srv_diff_host_rate
% of connections to different
hosts
32 dst_host_count
Count of connections having the
same destination host
33 dst_host_srv_count
Count of connections having the
same destination host and using
the
same service
34 dst_host_same_srv_rate
% of connections having the same
destination host and using the
same
service
35 dst_host_diff_srv_rate
% of different services on the
current host
36 dst_host_same_src_port_rate
% of connections to the current
host having the same src port
37 dst_host_srv_diff_host_rate
% of connections to the same
service coming from different
hosts
38 dst_host_serror_rate
% of connections to the current
host that have an S0 error
39 dst_host_srv_serro_rate
% of connections to the current
host and specified service that
have an SO error
5. A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and Forensics
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40 dst_host_rerror_rate
% of connections to the current
host that have an RST error
41 dst_host_srv_rerror_rate
% of connections to the current
host and specified service that
have an RST error
The training dataset is made up of 21 different attacks out of the 37 present in the test dataset. The
known attack types are those present in the training dataset while the novel attacks are the additional attacks in the
test dataset i.e. not available in the training datasets. The attack types are grouped into four categories: DoS,
Probe, U2R and R2L. The training dataset consisted of 25193 instances among which 13449 are normal and
11744 are attack type whereas the testing dataset consist of 2152 normal and 9698 attack types. Table III
represents the attacks presented in training dataset which is 20% of the complete NSL-KDD intrusion detection
dataset.
TABLE III: ATTACKS IN TRAINIG DATASET
Training
Dataset
(20 %)
Attack – Type (21)
DoS
Back, Land, Neptune, Pod,
Smurf, teardrop
Probe
Satan, Ipsweep, Nmap,
Portsweep,
R2L
Guess_Password,
Ftp_write,Imap, Phf,Multihop,
Warezmaster, Warezclient, Spy
U2R
Buffer_overflow, Loadmodule,
Rootkit
Fig 2: Number of Instances in training dataset
TABLE IV: ATTACKS IN TESTIING DATASET
Testing Dataset
(20 %)
Attack- Type (37)
DoS
Back, Land, Neptune, Pod, Smurf, teardrop,
Mailbomb, Processtable, Udpstorm,
Apache2, Worm,
Probe Satan, Ipsweep, Nmap, Portsweep, Mscan, Saint
R2L
Guess_Password, Ftp_write, Imap, Phf, Multihop,
Warezmaster, Xlock, xsnoop, Snmpguess,
Snmpgetattack, Httptunnel, Sendmail, Named
U2R
Buffer_overflow, Loadmodule, Rootkit, Perl,
Sqlattack, Xterm, Ps
Fig 3: Number of Instances in Testing Dataset
Each record in NSL-KDD dataset is a network linking record. Each link consists of 41 attribute
properties containing 3 symbolic variables (protocol type, service and flag). In order not to affect the clustering
result, the attribute values need to be pre-treated. Firstly, these three symbolic attributes are removed and then all
the remaining numerical attributes (39 attributes) are normalized in the range of [0 1] so that the attributes having
higher values do not dominate over attributes with low values. The standard deviation transform is shown as
follows:
(1)
The normalized transform is as follows:
(2)
Table II consists of all 42 attributes of the NSL-KDD dataset. Three of the symbolic data attributes (protocol
type, service and flag) are removed as non-contributing.
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Decision tree algorithm
We used WEKA 3.7 a machine learning tool [10], to generate decision trees for dataset and to find the basic
parameters for creating the membership functions of fuzzy inference system. Several decision tree based
techniques are applied to the NSL-KDD dataset to find out the best Suitable parameters. Figure 4 shows a
simple output of J48 decision tree algorithm.
Figure 4. Shows a simple output of J48 decision tree algorithm
Here, we are combining both the approaches of association rule mining [for selecting the best attributes
together and decision tree for identifying the best parameters together to create the rules for fuzzy expert system.
Decision Trees in WEKA tool is used to identify the required parameters. The minimum and maximum is set to
the range 0-1. Each attribute is distributed over a range of low, mid (medium) and high. Range of parameters is
shown in table V.
TABLE V: MEMBERSHIP PARAMETERS FOR PROBE ATTACKS
Attributes Min Low Mid High Max
source_bytes 0 0.2 0.34 0.7 1
logged_in 0 - - - 1
serror_rate 0 0.295 0.453 0.89 1
srv_serror_rate 0 0.294 0.454 0.81 1
rerror_rate 0 0.131 0.335 0.682 1
same_srv_rate 0 0.126 0.44 0.643 1
diff_srv_rate 0 0.069 0.483 0.732 1
dst_host_srv_r
ate
0 0 0.426 0.436 1
dst_host_serro
r_rate
0 0.296 0.452 0.691 1
Fuzzy Inference System
Fuzzy Inference Systems (FIS) have the ability to make use of knowledge expressed in the form of linguistic
rules, thus they offer the possibility of implementing the expert human knowledge and experience. Neuro-Fuzzy
Inference System (NFIS) models have become one of the major areas of interest as it combines the beneficial
features of both neural networks and fuzzy systems. Based on these rules we have created the fuzzy inference
system that is used as an input to neuro-fuzzy system. Figure 5 shows the generated FIS
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Figure 5: Fuzzy inference system
Membership functions are generated using trapezoidal functions and one Gaussian function is used for Boolean
type of attribute (i.e. logged in). Nine inputs having three membership functions except logged in (i.e. having 2
membership functions) generated for the probe attack. Figure 6 shows the generated fuzzy membership
functions
Figure 6: Membership functions for Probe Attack
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Figure 7: Fuzzy inference system output
Figure 7 shows the output of the fuzzy inference system. This system can be adjusted according to the values of
the input attributes. Fuzzy Inference system shows an accuracy of 94.4 %. The last attribute shows the defuzzified
output of the fuzzy system. Rules generated for fuzzy inference system is shown in figure 8. These rules are used
to makes decision for neuro-fuzzy system. The final outcome is the production of these rules.
Figure 8: Rules in fuzzy inference system
Now this fuzzy inference system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS. Structure
is generated as shown in figure 9 below.
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Figure 9: ANFIS structure
IV. Results
ANFIS tool output is evaluated on the basis of accuracy in predicting the attacks clearly. Results below
show the outcome of neuro-fuzzy approach [11].
The output of neuro-fuzzy toolbox [12]. Is the weighted neurons that are defuzzified using centroid
defuzzification method Neuro-Fuzzy system results in 99.68 % accuracy in detecting the probe attack. A slight
comparison to previous literature is given as follows:
TABLE VI: COMPARISON RESULTS
Approach in [paper] Probe Detection rate (%)
[84] 73.20
[85] 100
[86] 72.8
[87] 99
ANFIS Approach 99.68
V. Conclusion
In this paper, a technique uses decision tree for dataset and to find the basic parameters for creating the
membership functions of fuzzy inference system for Intrusion Detection and Forensics was successfully
demonstrated its usefulness on the training and testing subset NSL-KDD dataset. The NFIS network was used as
a Systems. NFIS is Capable of producing fuzzy rules without the aid of human experts. A fuzzy decision-
making engine was developed to make the system more powerful for attack detection, using the fuzzy inference
approach. At last, this paper proposed a technique to use Decision tree algorithms to optimize the decision-
making engine. The decision tree classifier will be evaluated with the NSL-KDD dataset to detect attacks on
four attack categories: Dos, Probe, R2L, and U2R.Experimentation results showed the proposed method is
effective in detecting intrusions in computer networks. Our future work will focus investigating how to improve
the performance of neuro-fuzzy logic to meet the requirements of computer security and forensics. Also, the
work will be continued to Developing an automotive system that can generate rule for its own without any
interaction of human experts would be a major challenge to researchers.
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