This document describes NetReconner, an intrusion detection system that uses regular expressions to detect network attacks. It works by capturing network packets using tcpdump and storing them in a file. A detection engine then compares each line of the captured packets to a set of regular expressions that represent known attacks. If a match is found, an alert is generated. The system also allows administrators to add new regular expressions to detect newly discovered attacks. It was developed to provide continuous monitoring of the network to identify malicious traffic in real-time.
A novel signature based traffic classification engine to reduce false alarms ...IJCNCJournal
Pattern matching plays a significant role in ascertaining network attacks and the foremost prerequisite for a trusted intrusion detection system (IDS) is accurate pattern matching. During the pattern matching process packets are scanned against a pre-defined rule sets. After getting scanned, the packets are marked as alert or benign by the detection system. Sometimes the detection system generates false alarms i.e., good traffic being identified as bad traffic. The ratio of generating the false positives varies from the performance of the detection engines used to scan incoming packets. Intrusion detection systems use to deploy algorithmic procedures to reduce false positives though producing a good number of false alarms. As the necessities, we have been working on the optimization of the algorithms and procedures so that false positives can be reduced to a great extent. As an effort we have proposed a signature-based traffic classification technique that can categorize the incoming packets based on the traffic characteristics and behaviour which would eventually reduce the rate of false alarms
A NOVEL HEADER MATCHING ALGORITHM FOR INTRUSION DETECTION SYSTEMSIJNSA Journal
The evolving necessity of the Internet increases the demand on the bandwidth. Therefore, this demand opens the doors for the hackers’ community to develop new methods and techniques to gain control over networking systems. Hence, the intrusion detection systems (IDS) are insufficient to prevent/detect unauthorized access the network. Network Intrusion Detection System (NIDS) is one example that still suffers from performance degradation due the increase of the link speed in today’s networks. In This paper we proposed a novel algorithm to detect the intruders, who’s trying to gain access to the network using the packets header parameters such as;
source/destination address, source/destination port, and protocol without the need to inspect each packet content looking for signatures/patterns. However, the “Packet Header Matching” algorithm enhances the overall speed of the matching process between the incoming packet headers against the rule set. We ran the proposed algorithm to proof the proposed concept in coping with the traffic arrival speeds and the various bandwidth demands. The achieved results were of significant enhancement of the overall performance in terms of detection speed.
Cybercrime is increasing at a faster pace and sometimes causes billions of dollars of business- losses so
investigating attackers after commitment is of utmost importance and become one of the main concerns of
network managers. Network forensics as the process of Collecting, identifying, extracting and analyzing
data and systematically monitoring traffic of network is one of the main requirements in detection and
tracking of criminals. In this paper, we propose an architecture for network forensic system. Our proposed
architecture consists of five main components: collection and indexing, database management, analysis
component, SOC communication component and the database.
The main difference between our proposed architecture and other systems is in analysis component. This
component is composed of four parts: Analysis and investigation subsystem, Reporting subsystem, Alert
and visualization subsystem and the malware analysis subsystem. The most important differentiating
factors of the proposed system with existing systems are: clustering and ranking of malware, dynamic
analysis of malware, collecting and analysis of network flows and anomalous behavior analysis.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
A novel signature based traffic classification engine to reduce false alarms ...IJCNCJournal
Pattern matching plays a significant role in ascertaining network attacks and the foremost prerequisite for a trusted intrusion detection system (IDS) is accurate pattern matching. During the pattern matching process packets are scanned against a pre-defined rule sets. After getting scanned, the packets are marked as alert or benign by the detection system. Sometimes the detection system generates false alarms i.e., good traffic being identified as bad traffic. The ratio of generating the false positives varies from the performance of the detection engines used to scan incoming packets. Intrusion detection systems use to deploy algorithmic procedures to reduce false positives though producing a good number of false alarms. As the necessities, we have been working on the optimization of the algorithms and procedures so that false positives can be reduced to a great extent. As an effort we have proposed a signature-based traffic classification technique that can categorize the incoming packets based on the traffic characteristics and behaviour which would eventually reduce the rate of false alarms
A NOVEL HEADER MATCHING ALGORITHM FOR INTRUSION DETECTION SYSTEMSIJNSA Journal
The evolving necessity of the Internet increases the demand on the bandwidth. Therefore, this demand opens the doors for the hackers’ community to develop new methods and techniques to gain control over networking systems. Hence, the intrusion detection systems (IDS) are insufficient to prevent/detect unauthorized access the network. Network Intrusion Detection System (NIDS) is one example that still suffers from performance degradation due the increase of the link speed in today’s networks. In This paper we proposed a novel algorithm to detect the intruders, who’s trying to gain access to the network using the packets header parameters such as;
source/destination address, source/destination port, and protocol without the need to inspect each packet content looking for signatures/patterns. However, the “Packet Header Matching” algorithm enhances the overall speed of the matching process between the incoming packet headers against the rule set. We ran the proposed algorithm to proof the proposed concept in coping with the traffic arrival speeds and the various bandwidth demands. The achieved results were of significant enhancement of the overall performance in terms of detection speed.
Cybercrime is increasing at a faster pace and sometimes causes billions of dollars of business- losses so
investigating attackers after commitment is of utmost importance and become one of the main concerns of
network managers. Network forensics as the process of Collecting, identifying, extracting and analyzing
data and systematically monitoring traffic of network is one of the main requirements in detection and
tracking of criminals. In this paper, we propose an architecture for network forensic system. Our proposed
architecture consists of five main components: collection and indexing, database management, analysis
component, SOC communication component and the database.
The main difference between our proposed architecture and other systems is in analysis component. This
component is composed of four parts: Analysis and investigation subsystem, Reporting subsystem, Alert
and visualization subsystem and the malware analysis subsystem. The most important differentiating
factors of the proposed system with existing systems are: clustering and ranking of malware, dynamic
analysis of malware, collecting and analysis of network flows and anomalous behavior analysis.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
Internet Worm Classification and Detection using Data Mining Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
A Study on Data Mining Based Intrusion Detection SystemAM Publications
In recent years security has remained unsecured for computers as well as data network systems. Intrusion detecting
system used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining
techniques that can be applied to intrusion detection system to detect normal and abnormal behavior patterns. This paper studies
nature of network attacks and the current trends of data mining based intrusion detection techniques
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
ADRISYA: A FLOW BASED ANOMALY DETECTION SYSTEM FOR SLOW AND FAST SCANIJNSA Journal
Attackers perform port scan to find reachability, liveness and running services in a system or network. Current day scanning tools provide different scanning options and capable of evading various security tools like firewall, IDS and IPS. So in order to detect and prevent attacks in the early stages, an accurate detection of scanning activity in real time is very much essential. In this paper we present a flow based protocol behaviour analysis system to detect TCP based slow and fast scan. This system provides scalable, accurate and generic solution to TCP based scanning by means of automatic behaviour analysis of the network traffic. Detection capability of proposed system is compared with SNORT and result proves the high detection rate of the system over SNORT.
Replay of Malicious Traffic in Network TestbedsDETER-Project
In this paper we present tools and methods to integrate attack measurements from the Internet with controlled experimentation on a network testbed. We show that this approach provides greater fidelity than synthetic models. We compare the statistical properties of real-world attacks with synthetically generated constant bit rate attacks on the testbed. Our results indicate that trace replay provides fine time-scale details that may be absent in constant bit rate attacks. Additionally, we demonstrate the effectiveness of our approach to study new and emerging attacks. We replay an Internet attack captured by the LANDER system on the DETERLab testbed within two hours.
Data and tools from the paper are available at: http://montage.deterlab.net/magi/hst2013tools
Also read the LANDER Blog entry at: http://ant.isi.edu/blog/?p=411
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
A Survey on DPI Techniques for Regular Expression Detection in Network Intrus...ijsrd.com
Deep Packet Inspection (DPI) is becoming more widely used in virtually all applications or services like Intrusion Detection System (IDS), which operate with or within a network. DPI analyzes all data present in the packet as it passes an inspection to determine the application transported and protocol. Deep packet inspection typically uses regular expression matching as a core operator. Regular expressions (RegExes) are used to flexibly represent complex string patterns in many applications ranging from network intrusion detection and prevention systems (NIDPSs). Regular expressions represent complex string pattern as attack signatures in DPI. It examine whether a packet’s payload matches any of a set of predefined regular expressions. There are various techniques developed in DPI for deep packet inspection for regular expression. We survey on these techniques for further improvement in regular expression detection in this paper. In the result we found that it is possible to reduce RegEx transaction memory required in network intrusion detection. We made this survey with possible use of DPI techniques in the wireless network.
Collecting and analyzing network-based evidenceCSITiaesprime
Since nearly the beginning of the Internet, malware has been a significant deterrent to productivity for end users, both personal and business related. Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ‘witness’ of a criminal event. Forensic investigations include collection, recovery, analysis, and presentation of information stored on network devices and related to network crimes. These activities often involve wide range of analysis tools and application of different methods. This work presents methods that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstructions of events or action to reach case conclusions. Main aim of network forensic is to gather evidence. Additionally, the evidence obtained during the investigation must be produced through a rigorous investigation procedure in a legal context.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
Internet Worm Classification and Detection using Data Mining Techniquesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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.
A Study on Data Mining Based Intrusion Detection SystemAM Publications
In recent years security has remained unsecured for computers as well as data network systems. Intrusion detecting
system used to safeguard the data confidentiality, integrity and system availability from various types of attacks. Data mining
techniques that can be applied to intrusion detection system to detect normal and abnormal behavior patterns. This paper studies
nature of network attacks and the current trends of data mining based intrusion detection techniques
INTRUSION DETECTION USING FEATURE SELECTION AND MACHINE LEARNING ALGORITHM WI...ijcsit
In order to avoid illegitimate use of any intruder, intrusion detection over the network is one of the critical
issues. An intruder may enter any network or system or server by intruding malicious packets into the
system in order to steal, sniff, manipulate or corrupt any useful and secret information, this process is
referred to as intrusion whereas when packets are transmitted by intruder over the network for any purpose
of intrusion is referred to as attack. With the expanding networking technology, millions of servers
communicate with each other and this expansion is always in progress every day. Due to this fact, more
and more intruders get attention; and so to overcome this need of smart intrusion detection model is a
primary requirement.
By analyzing the feature selection methods the identification of essential features of NSL-KDD data set is
done, then by using selected features and machine learning approach and analyzing the basic features of
networks over the data set a hybrid algorithm is made. Finally a model is produced over the algorithm
containing the rules for the network features.
A hybrid misuse intrusion detection model is made to find attacks on system to improve the intrusion
detection. Based on prior features, intrusions on the system can be detected without any previous learning.
This model contains the advantage of feature selection and machine learning techniques with misuse
detection.
False positive reduction by combining svm and knn algoeSAT Journals
Abstract
With the growth of information technology. There emerges many intrusion detection problem such as cyber security. Intrusion detection system provides basic infrastructure to detect a number of attacks. This research work focuses on intrusion detection problem of network security. The main goal is to detect network behaviour as normal or abnormal. In this research work, two different machine learning algorithm have been combined together to reduce its weakness and takes positive feature of both algorithm. Its experimental results generates better result than other algorithm in terms of performance, accuracy and false positive rate. These combined algorithm has been applied on KDDCUP99 dataset to find better result by improving its performance, accuracy and reducing its false positive rate.
Keywords: Intrusion detection system, KDDCUP99 dataset, False positive rate.
ADRISYA: A FLOW BASED ANOMALY DETECTION SYSTEM FOR SLOW AND FAST SCANIJNSA Journal
Attackers perform port scan to find reachability, liveness and running services in a system or network. Current day scanning tools provide different scanning options and capable of evading various security tools like firewall, IDS and IPS. So in order to detect and prevent attacks in the early stages, an accurate detection of scanning activity in real time is very much essential. In this paper we present a flow based protocol behaviour analysis system to detect TCP based slow and fast scan. This system provides scalable, accurate and generic solution to TCP based scanning by means of automatic behaviour analysis of the network traffic. Detection capability of proposed system is compared with SNORT and result proves the high detection rate of the system over SNORT.
Replay of Malicious Traffic in Network TestbedsDETER-Project
In this paper we present tools and methods to integrate attack measurements from the Internet with controlled experimentation on a network testbed. We show that this approach provides greater fidelity than synthetic models. We compare the statistical properties of real-world attacks with synthetically generated constant bit rate attacks on the testbed. Our results indicate that trace replay provides fine time-scale details that may be absent in constant bit rate attacks. Additionally, we demonstrate the effectiveness of our approach to study new and emerging attacks. We replay an Internet attack captured by the LANDER system on the DETERLab testbed within two hours.
Data and tools from the paper are available at: http://montage.deterlab.net/magi/hst2013tools
Also read the LANDER Blog entry at: http://ant.isi.edu/blog/?p=411
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
A Survey on DPI Techniques for Regular Expression Detection in Network Intrus...ijsrd.com
Deep Packet Inspection (DPI) is becoming more widely used in virtually all applications or services like Intrusion Detection System (IDS), which operate with or within a network. DPI analyzes all data present in the packet as it passes an inspection to determine the application transported and protocol. Deep packet inspection typically uses regular expression matching as a core operator. Regular expressions (RegExes) are used to flexibly represent complex string patterns in many applications ranging from network intrusion detection and prevention systems (NIDPSs). Regular expressions represent complex string pattern as attack signatures in DPI. It examine whether a packet’s payload matches any of a set of predefined regular expressions. There are various techniques developed in DPI for deep packet inspection for regular expression. We survey on these techniques for further improvement in regular expression detection in this paper. In the result we found that it is possible to reduce RegEx transaction memory required in network intrusion detection. We made this survey with possible use of DPI techniques in the wireless network.
Collecting and analyzing network-based evidenceCSITiaesprime
Since nearly the beginning of the Internet, malware has been a significant deterrent to productivity for end users, both personal and business related. Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ‘witness’ of a criminal event. Forensic investigations include collection, recovery, analysis, and presentation of information stored on network devices and related to network crimes. These activities often involve wide range of analysis tools and application of different methods. This work presents methods that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstructions of events or action to reach case conclusions. Main aim of network forensic is to gather evidence. Additionally, the evidence obtained during the investigation must be produced through a rigorous investigation procedure in a legal context.
Intrusion Detection Systems By Anamoly-Based Using Neural NetworkIOSR Journals
To improve network security different steps has been taken as size and importance of the network has
increases day by day. Then chances of a network attacks increases Network is mainly attacked by some
intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data
packets and each packet has to scan for its detection. This paper works to develop a intrusion detection system
which utilizes the identity and signature of the intrusion for identifying different kinds of intrusions. As network
intrusion detection system need to be efficient enough that chance of false alarm generation should be less,
which means identifying as a intrusion but actually it is not an intrusion. Result obtained after analyzing this
system is quite good enough that nearly 90% of true alarms are generated. It detect intrusion for various
services like Dos, SSH, etc by neural network
Online stream mining approach for clustering network trafficeSAT Journals
Abstract A large number of research have been proposed on intrusion detection system, which leads to the implementation of agent based intelligent IDS (IIDS), Non – intelligent IDS (NIDS), signature based IDS etc. While building such IDS models, learning algorithms from flow of network traffic plays crucial role in accuracy of IDS systems. The proposed work focuses on implementing the novel method to cluster network traffic which eliminates the limitations in existing online clustering algorithms and prove the robustness and accuracy over large stream of network traffic arriving at extremely high rate. We compare the existing algorithm with novel methods to analyse the accuracy and complexity. Keywords— NIDS, Data Stream Mining, Online Clustering, RAH algorithm, Online Efficient Incremental Clustering algorithm
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
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.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Pre-filters in-transit malware packets detection in the networkTELKOMNIKA JOURNAL
Conventional malware detection systems cannot detect most of the new malware in the network
without the availability of their signatures. In order to solve this problem, this paper proposes a technique
to detect both metamorphic (mutated malware) and general (non-mutated) malware in the network using a
combination of known malware sub-signature and machine learning classification. This network-based
malware detection is achieved through a middle path for efficient processing of non-malware packets.
The proposed technique has been tested and verified using multiple data sets (metamorphic malware,
non-mutated malware, and UTM real traffic), this technique can detect most of malware packets in
the network-based before they reached the host better than the previous works which detect malware in
host-based. Experimental results showed that the proposed technique can speed up the transmission of
more than 98% normal packets without sending them to the slow path, and more than 97% of malware
packets are detected and dropped in the middle path. Furthermore, more than 75% of metamorphic
malware packets in the test dataset could be detected. The proposed technique is 37 times faster than
existing technique.
A PROPOSED MODEL FOR DIMENSIONALITY REDUCTION TO IMPROVE THE CLASSIFICATION C...IJNSA Journal
Over the past few years, intrusion protection systems have drawn a mature research area in the field of computer networks. The problem of excessive features has a significant impact on
intrusion detection performance. The use of machine learning algorithms in many previous researches has been used to identify network traffic, harmful or normal. Therefore, to obtain the accuracy, we must reduce the dimensionality of the data used. A new model design based on a combination of feature selection and machine learning algorithms is proposed in this paper. This model depends on selected genes from every feature to increase the accuracy of intrusion detection systems. We selected from features content only ones which impact in attack detection. The performance has been evaluated based on a comparison of several known algorithms. The NSL-KDD dataset is used for examining classification. The proposed model outperformed the other learning approaches with accuracy 98.8 %.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.