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Autonomic Anomaly Detection System in Computer Networks
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
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IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Analysis and Design for Intrusion Detection System Based on Data Mining
Reference:
Dyuanyang Zhao, Zhilin Feng, Qingxiang Xu, “Analysis and design for Intrusion detection system based on data mining” in proceedings of 2010 IEEE second international workshop on education technology and computer science
IDS (intrusion detection system)
An IDS (Intrusion detection system) is a device or software application that monitors network or system
activities for malicious activities or policy violations and produces reports to a management station. IDS
come in a variety of “flavors” and approach the goal of detecting suspicious traffic in different ways.
There are network based (NIDS) and host based (HIDS) intrusion detection systems. Some systems may
attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system.
Recommended
Autonomic Anomaly Detection System in Computer Networks
This paper describes how you can protect your system from Intrusion, which is the method of Intrusion Prevention and Intrusion Detection .The underlying premise of our Intrusion detection system is to describe attack as instance of ontology and its first need is to detect attack. In this paper, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-governing: self-labeling, self-updating and self-adapting. Our structure employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies.
Bt33430435
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Analysis and Design for Intrusion Detection System Based on Data Mining
Reference:
Dyuanyang Zhao, Zhilin Feng, Qingxiang Xu, “Analysis and design for Intrusion detection system based on data mining” in proceedings of 2010 IEEE second international workshop on education technology and computer science
IDS (intrusion detection system)
An IDS (Intrusion detection system) is a device or software application that monitors network or system
activities for malicious activities or policy violations and produces reports to a management station. IDS
come in a variety of “flavors” and approach the goal of detecting suspicious traffic in different ways.
There are network based (NIDS) and host based (HIDS) intrusion detection systems. Some systems may
attempt to stop an intrusion attempt but this is neither required nor expected of a monitoring system.
Insight Brief: Security Analytics to Identify the 12 Indicators of Compromise
In this security insight brief, 21CT researchers look at the malicious network behaviors that concern organizations the most, and how to use security analytics to find them before damage is done. Understanding these 12 indicators of compromise are critical to identifying a network breach.
Network security using data mining concepts
Network Security is a major part of a network that needs to be maintained because information is being passed between computers etc. and is very vulnerable to attack.
Data Mining is the process of extraction of required/specific information from data in database.
Data mining is integrated with network security and can be used with various security tools as well as hacking tool.
Intrusion detection system
hey guys here comes my new implementation of my learning i.e the IDS a concept of network security go through it and add your valuable comments
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...IJCSIS Research Publications
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paperVol 6 No 1 - October 2013
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
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Design and Implementation of Intelligence Car Parking Systems ........................................................1
Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
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Lukasz Ostrowski and Markus Helfert
Personalizing Education News Articles Using Interest Term and Category Based Recommender
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A CAPTCHA – BASED INTRUSION DETECTION MODEL
Intrusion Detection systems (IDS) are an essential element for Network Security Infrastructure and play an
important role in detecting large number of attacks. Intrusion Prevention System (IPS) is a tool that is used
to prevent spywares from getting intrusion into a system and one of the techniques used in IPS is
Completely Automated Public Turning test to tell Computers and Human Apart (CAPTCHA). In order to
detect illegal access of the web from the intruder, IDS, IPS can be implemented with the use of honeypot to
track the IP address, location and country or region of the attacker in order to block the attacker from
accessing the system. Different techniques have been adopted by different researchers using IDS, IPS and
honeypot to protect their system against illegal attacks. As discovered in the existing systems CAPTCHA
was not employed in IDS to detect spywares capable of breaking and having access to the system. To
increase and maintain the security in a Network the combination of IDS with CAPTCHA, IPS and a dummy Honeypot can be employed. This work proposes a CAPTCHA –based Intrusion Detection Model with a redirector in order to identify the intelligent spywares that are capable of breaking CAPTCHA in IPS. Also using a dummy honeypot with circular hyperlinks so as to lewd the software that infiltrated the system in order to capture its IP address and other important information about the spywares such as the country or region it’s coming from, web browser used and date and time of intrusion so as to block and prevent illegal access by intruders. This paper focuses on capturing the intelligent spywares capable to break through the new CAPTCHA trap IDS so as to gather information about it and necessary action can be taken against it. A security model was designed having having CAPTCHA IDS with a redirector, IPS and a honeypot cable of detecting intrusion by intelligent spyware With this model the network will be more secured against intrusion by spywares
Detecting Anomaly IDS in Network using Bayesian Network
In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive
security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a
review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and
classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows
the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian
network.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...IJCSIS Research Publications
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.A hybrid intrusion detection system for cloud computing environments
A hybrid intrusion detection system for cloud computing environments
Futuristic data mining technologies for cyber security
These slide provides the insights about futuristic data mining technologies for cyber security
Intrusion Detection System using AI and Machine Learning Algorithm
https://irjet.net/archives/V4/i12/IRJET-V4I12314.pdf
Optimized Intrusion Detection System using Deep Learning Algorithm
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
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Insight Brief: Security Analytics to Identify the 12 Indicators of Compromise
In this security insight brief, 21CT researchers look at the malicious network behaviors that concern organizations the most, and how to use security analytics to find them before damage is done. Understanding these 12 indicators of compromise are critical to identifying a network breach.
Network security using data mining concepts
Network Security is a major part of a network that needs to be maintained because information is being passed between computers etc. and is very vulnerable to attack.
Data Mining is the process of extraction of required/specific information from data in database.
Data mining is integrated with network security and can be used with various security tools as well as hacking tool.
Intrusion detection system
hey guys here comes my new implementation of my learning i.e the IDS a concept of network security go through it and add your valuable comments
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...
A Hybrid Intrusion Detection System for Network Security: A New Proposed Min ...IJCSIS Research Publications
When talk about intrusion, then it is pre- assume
that the intrusion is happened or it is stopped by the intrusion
detection system. This is all done through the process of collection
of network traffic information at certain point of networks in the
digital system. In this way the IDS perform their job to secure the
network. There are two types of Intrusion Detection: First is
Misuse based detection and second one is Anomaly based detection.
The detection which uses data set of known predefined set of
attacks is called Misuse - Based IDSs and Anomaly based IDSs are
capable of detecting new attacks which are not known to previous
data set of attacks and is based on some new heuristic methods. In
our hybrid IDS for computer network security we use Min-Min
algorithm with neural network in hybrid method for improving
performance of higher level of IDS in network. Data releasing is
the problem for privacy point of view, so we first evaluate training
for error from neural network regression state, after that we can get
outer sniffer by using Min length from source, so that we
hybridized as with Min – Min in neural network in hybrid system
which we proposed in our research paperVol 6 No 1 - October 2013
An Efficient Classification Mechanism For Network Intrusion Detection System Based on Data Mining
Techniques:A Survey..........................................................................................................................1
Subaira A. S. and Anitha P.
Automated Biometric Verification: A Survey on Multimodal Biometrics ..............................................1
Rupali L. Telgad, Almas M. N. Siddiqui and Dr. Prapti D. Deshmukh
Design and Implementation of Intelligence Car Parking Systems ........................................................1
Ogunlere Samson, Maitanmi Olusola and Gregory Onwodi
Intrusion Detection Techniques for Mobile Ad Hoc and Wireless Sensor Networks..............................1
Rakesh Sharma, V. A. Athavale and Pinki Sharma
Performance Evaluation of Sentiment Mining Classifiers on Balanced and Imbalanced Dataset ...........1
G.Vinodhini and R M. Chandrasekaran
Demosaicing and Super-resolution for Color Filter Array via Residual Image Reconstruction and Sparse
Representation..................................................................................................................................1
Jie Yin, Guangling Sun and Xiaofei Zhou
Determining Weight of Known Evaluation Criteria in the Field of Mehr Housing using ANP Approach ..1
Saeed Safari, Mohammad Shojaee, Mohammad Tavakolian and Majid Assarian
Application of the Collaboration Facets of the Reference Model in Design Science Paradigm ...............1
Lukasz Ostrowski and Markus Helfert
Personalizing Education News Articles Using Interest Term and Category Based Recommender
Approaches .......................................................................................................................................1
A CAPTCHA – BASED INTRUSION DETECTION MODEL
Intrusion Detection systems (IDS) are an essential element for Network Security Infrastructure and play an
important role in detecting large number of attacks. Intrusion Prevention System (IPS) is a tool that is used
to prevent spywares from getting intrusion into a system and one of the techniques used in IPS is
Completely Automated Public Turning test to tell Computers and Human Apart (CAPTCHA). In order to
detect illegal access of the web from the intruder, IDS, IPS can be implemented with the use of honeypot to
track the IP address, location and country or region of the attacker in order to block the attacker from
accessing the system. Different techniques have been adopted by different researchers using IDS, IPS and
honeypot to protect their system against illegal attacks. As discovered in the existing systems CAPTCHA
was not employed in IDS to detect spywares capable of breaking and having access to the system. To
increase and maintain the security in a Network the combination of IDS with CAPTCHA, IPS and a dummy Honeypot can be employed. This work proposes a CAPTCHA –based Intrusion Detection Model with a redirector in order to identify the intelligent spywares that are capable of breaking CAPTCHA in IPS. Also using a dummy honeypot with circular hyperlinks so as to lewd the software that infiltrated the system in order to capture its IP address and other important information about the spywares such as the country or region it’s coming from, web browser used and date and time of intrusion so as to block and prevent illegal access by intruders. This paper focuses on capturing the intelligent spywares capable to break through the new CAPTCHA trap IDS so as to gather information about it and necessary action can be taken against it. A security model was designed having having CAPTCHA IDS with a redirector, IPS and a honeypot cable of detecting intrusion by intelligent spyware With this model the network will be more secured against intrusion by spywares
Detecting Anomaly IDS in Network using Bayesian Network
In a hostile area of network, it is a severe challenge to protect sink, developing flexible and adaptive
security oriented approaches against malicious activities. Intrusion detection is the act of detecting, monitoring
unwanted activity and traffic on a network or a device, which violates security policy. This paper begins with a
review of the most well-known anomaly based intrusion detection techniques. AIDS is a system for detecting
computer intrusions, type of misuse that falls out of normal operation by monitoring system activity and
classifying it as either normal or anomalous .It is based on Machine Learning AIDS schemes model that allows
the attacks analyzed to be categorized and find probabilistic relationships among attacks using Bayesian
network.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...IJCSIS Research Publications
Malicious activities (malcodes) are self replicating
malware and a major security threat in a network environment.
Timely detection and system alert flags are very essential to
prevent rapid malcodes spreading in the network. The difficulty
in detecting malcodes is that they evolve over time. Despite the fact
that signature-based tools, are generally used to secure systems,
signature-based malcode detectors neglect to recognize muddled
and beforehand concealed malcode executables. Automatic signature
generation systems has likewise been use to address the issue
of malcodes, yet there are many works required for good detection.
Base on the behavior way of malcodes, a behavior approach is
required for such detection. Specifically, we require a dynamic
investigation and behavior Rule Base system that distinguishes
malcodes without erroneously block legitimate traffic or increase
false alarms. This paper proposed and discussed the approach
using Machine learning and Indicators of Compromise (IOC) to
analyze intrusion in a network, to identify the cause of the attack
and to provide future detection. This paper proposed the use of
behaviour malware analysis framework to analyze intrusion data,
apply clustering algorithm on the analyzed data and generate IOC
from the clustered data for IOCRule, which will be implemented
into Snort Intrusion Detection System (IDS) for malicious code
detection.A hybrid intrusion detection system for cloud computing environments
A hybrid intrusion detection system for cloud computing environments
Futuristic data mining technologies for cyber security
These slide provides the insights about futuristic data mining technologies for cyber security
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Detecting Anomaly IDS in Network using Bayesian Network
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NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...
Malicious Code Intrusion Detection using Machine Learning and Indicators of C...
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A hybrid intrusion detection system for cloud computing environments
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Futuristic data mining technologies for cyber security
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Similar to Idps
Intrusion Detection System using AI and Machine Learning Algorithm
https://irjet.net/archives/V4/i12/IRJET-V4I12314.pdf
Optimized Intrusion Detection System using Deep Learning Algorithm
A method and a system for the detection of an intrusion in a computer network compare the network traffic of the computer network at multiple different points in the network. In an uncompromised network the network traffic monitored at these two different points in the network should be identical. A network intrusion detection system is mostly place at strategic points in a network, so that it can monitor the traffic traveling to or from different devices on that network. The existing Software Defined Network SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. Machine learning is an artificial intelligence approach that focuses on acquiring knowledge from raw data and, based at least in part on the identified flow, selectively causing the packet, or a packet descriptor associated with the packet. The performance is evaluated using the network analysis metrics such as key generation delay, key sharing delay and the hash code generation time for both SDN and the proposed machine learning SDN. Prof P. Damodharan | K. Veena | Dr N. Suguna "Optimized Intrusion Detection System using Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21447.pdf
Paper URL: https://www.ijtsrd.com/engineering/other/21447/optimized-intrusion-detection-system-using-deep-learning-algorithm/prof-p-damodharan
information security (Audit mechanism, intrusion detection, password manageme...
this slides contains the contents of Audit mechanism, intrusion detection, password management, general principals of system security and IP security
Bt33430435
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
The Practical Data Mining Model for Efficient IDS through Relational Databases
Enterprise network information system is not only the platform for information sharing and information exchanging, but also the platform for enterprise production automation system and enterprise management system working together. As a result, the security defense of enterprise network information system does not only include information system network security and data security, but also include the security of network business running on information system network, which is the confidentiality, integrity, continuity and real-time of network business. Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements – they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, data mining-based intrusion detection systems (IDSs) have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. Still, significant challenges exist in the design and implementation of production quality IDSs. Incrementing components such as data transformations, model deployment, and cooperative distributed detection remain a labor intensive and complex engineering endeavor. This paper describes DAID, a database-centric architecture that leverages data mining within the Relational RDBMS to address these challenges. DAID also offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability. DAID is illustrated with an Intrusion Detection Center application prototype that leverages existing functionality in Relational Database 10g. Intrusion detection system work at many levels in the network fabric and are taking the concept of security to a whole new sphere by incorporating intelligence as a tool to protect networks against un-authorized intrusions and newer forms of attack. We have described formal model for the construction of network security situation measurement based on d-s evidence theory, frequent mode, and sequence model extracted from the data on network security situation based on the knowledge found method and convert the pattern on the related rules of the network security situation, and automatic generation of network security situation.
A Study on Recent Trends and Developments in Intrusion Detection System
http://www.iosrjournals.org/iosr-jce/pages/v11i6.html
Kx3419591964
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.
Comparative Analysis: Network Forensic Systems
Network Forensics is scientifically proven technique to accumulate, perceive, identify, examine, associate, analyse and document digital evidence from multiple systems for the purpose of uncovering the fact of attacks and other problem incident as well as performing the action to recover from the attack. Many systems are proposed for designing the network forensic systems. In this paper we have prepared comparative analysis of various models based on different techniques.
Analysis of Artificial Intelligence Techniques for Network Intrusion Detectio...
With the rapid advancement of computer technology during the last couple of decades. Computer systems are commonly used in manufacturing, corporate, as well as other aspects of human living. As a result, constructing dependable infrastructures is a major challenge for IT managers. On the contrary side, this same rapid advancement of technology has created numerous difficulties in building reliable networks which are challenging tasks. There seem to be numerous varieties of attacks that affect the accessibility, authenticity, as well as secrecy of communications systems. In this paper, an in-depth and all-inclusive description of artificial intelligence methods used for the detection of network intrusions is discussed in detail.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
An intrusion detection system detects various malicious behaviors and abnormal activities that might harm
security and trust of computer system. IDS operate either on host or network level via utilizing anomaly
detection or misuse detection. Main problem is to correctly detect intruder attack against computer
network. The key point of successful detection of intrusion is choice of proper features. To resolve the
problems of IDS scheme this research work propose “an improved method to detect intrusion using
machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion
detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random
forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three
algorithms J48, J48Graft and Random forest gives much better results than other machine learning
algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have
considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.
Defense mechanism for ddos attack through machine learning
Abstract
There is a huge advancement in Computer networking in the past decade. But with the advancement, the threats to the computer networks are also increased. Today one of the biggest threats to the computer networks is the Distributed Denial of Service (DDoS) flooding attack. This paper emphasizes the application layer DDoS flooding attacks because these (layer seven) attacks are growing rapidly and becoming more severe problem. Many researchers used machine-learning techniques for intrusion detection, but some shows poor detection and some methods take more training time. From a survey, it is found that Naïve Bayes (NB) algorithm provides faster learning/training speed than other machine learning algorithms. Also it has more accuracy in classification and detection of attack. So we are proposing a network intrusion detection system (IDS) which uses a machine learning approach with the help of NB algorithm.
Keywords: DDoS (Distributed Denial of Service) flooding attack, Machine Learning, Naïve Bayes, Network Intrusion Detection
A Study On Recent Trends And Developments In Intrusion Detection System
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Optimized Intrusion Detection System using Deep Learning Algorithm
Optimized Intrusion Detection System using Deep Learning Algorithm
information security (Audit mechanism, intrusion detection, password manageme...
information security (Audit mechanism, intrusion detection, password manageme...
The Practical Data Mining Model for Efficient IDS through Relational Databases
The Practical Data Mining Model for Efficient IDS through Relational Databases
A Study on Recent Trends and Developments in Intrusion Detection System
A Study on Recent Trends and Developments in Intrusion Detection System
A review of anomaly based intrusions detection in multi tier web applications
A review of anomaly based intrusions detection in multi tier web applications
A review of anomaly based intrusions detection in multi tier web applications
A review of anomaly based intrusions detection in multi tier web applications
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Analysis of Artificial Intelligence Techniques for Network Intrusion Detectio...
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
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AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMS
Defense mechanism for d do s attack through machine learning
Defense mechanism for d do s attack through machine learning
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Defense mechanism for ddos attack through machine learning
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A Study On Recent Trends And Developments In Intrusion Detection System
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Physiology of Special Chemical Sensation of Taste
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
NVBDCP.pptx Nation vector borne disease control program
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ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdf
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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