This document discusses recent trends and developments in intrusion detection systems. It covers several topics:
- Artificial intelligence and machine learning techniques like neural networks, genetic algorithms, and fuzzy logic can be applied to intrusion detection to identify patterns and anomalies.
- There are different types of intrusion detection systems, including network-based, host-based, and wireless intrusion detection. Signature-based and anomaly-based detection are also discussed.
- Popular open source intrusion detection tools like Snort are discussed as alternatives to commercial intrusion prevention systems for some organizations.
- Intrusion prevention systems not only detect intrusions but can also automatically block attacks in real-time.
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
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 ALGORITHMSieijjournal1
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.
Detecting Anomaly IDS in Network using Bayesian NetworkIOSR Journals
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.
Intrusion Detection Systems (IDSs) have become widely recognized as powerful tools for identifying, deterring and deflecting malicious attacks over the network. Intrusion detection systems (IDSs) are designed and installed to aid in deterring or mitigating the damage that can be caused by hacking, or breaking into sensitive IT systems. . The attacks can come from outsider attackers on the Internet, authorized insiders who misuse the privileges that have been given them and unauthorized insiders who attempt to gain unauthorized privileges. IDSs cannot be used in isolation, but must be part of a larger framework of IT security measures. Essential to almost every intrusion detection system is the ability to search through packets and identify content that matches known attacks. Space and time efficient string matching algorithms are therefore important for identifying these packets at line rate. In this paper we examine string matching algorithm and their use for Intrusion Detection. Keywords: System Design, Network Algorithm
AN IMPROVED METHOD TO DETECT INTRUSION USING MACHINE LEARNING ALGORITHMSieijjournal
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 ALGORITHMSieijjournal1
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.
Detecting Anomaly IDS in Network using Bayesian NetworkIOSR Journals
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.
Intrusion Detection Systems (IDSs) have become widely recognized as powerful tools for identifying, deterring and deflecting malicious attacks over the network. Intrusion detection systems (IDSs) are designed and installed to aid in deterring or mitigating the damage that can be caused by hacking, or breaking into sensitive IT systems. . The attacks can come from outsider attackers on the Internet, authorized insiders who misuse the privileges that have been given them and unauthorized insiders who attempt to gain unauthorized privileges. IDSs cannot be used in isolation, but must be part of a larger framework of IT security measures. Essential to almost every intrusion detection system is the ability to search through packets and identify content that matches known attacks. Space and time efficient string matching algorithms are therefore important for identifying these packets at line rate. In this paper we examine string matching algorithm and their use for Intrusion Detection. Keywords: System Design, Network Algorithm
An intrusion detection system (IDS) is an ad hoc security solution to protect flawed computer systems. It works
like a burglar alarm that goes off if someone tampers with or manages to get past other security mechanisms
such as authentication mechanisms and firewalls. An Intrusion Detection System (IDS) is a device or a software
application that monitors network or system activities for malicious activities or policy violations and produces
reports to a management station.Intrusion Detection System (IDS) has been used as a vital instrument in
defending the network from this malicious or abnormal activity..In this paper we are comparing host based and
network based IDS and various types of attacks possible on IDS.
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 paper
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
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.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
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
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
Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
Autonomic Anomaly Detection System in Computer Networksijsrd.com
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.
In recent years, wireless sensor network (WSN) is used in several application areas resembling observance, tracking, and dominant in IoTs. for several applications of WSN, security is a crucial demand. However, security solutions in WSN disagree from ancient networks because of resource limitation and process constraints. This paper analyzes security solutions: TinySec, IEEE 802.15.4, SPINS, MiniSEC, LSec, LLSP, LISA, and LISP in WSN. This paper additionally presents characteristics, security needs, attacks, cryptography algorithms, and operation modes. This paper is taken into account to be helpful for security designers in WSNs.
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.
An intrusion detection system (IDS) is an ad hoc security solution to protect flawed computer systems. It works
like a burglar alarm that goes off if someone tampers with or manages to get past other security mechanisms
such as authentication mechanisms and firewalls. An Intrusion Detection System (IDS) is a device or a software
application that monitors network or system activities for malicious activities or policy violations and produces
reports to a management station.Intrusion Detection System (IDS) has been used as a vital instrument in
defending the network from this malicious or abnormal activity..In this paper we are comparing host based and
network based IDS and various types of attacks possible on IDS.
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 paper
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
These days the security provided by the computer systems is a big issue as it always has the threats of
cyber-attacks like IP address spoofing, Denial of Service (DOS), token impersonation, etc. The security
provided by the blue team operations tends to be costly if done in large firms as a large number of systems
need to be protected against these attacks. This leads these firms to turn to less costly security
configurations like IDS Suricata and IDS Snort. The main theme of the project is to improve the services
provided by Snort which is a tool used in creating a vague defense against cyber-attacks like DDOS
attacks which are done on both physical and network layers. These attacks in turn result in loss of
extremely important data. The rules defined in this project will result in monitoring traffic, analyzing it,
and taking appropriate action to not only stop the attack but also locate its source IP address. This whole
process uses different tools other than Snort like Wireshark, Wazuh and Splunk. The product of this will
result in not only the detection of the attack but also the source IP address of the machine on which the
attack is initiated and completed. The end product of this research will result in sets of default rules for the
Snort tool which will not only be able to provide better security than its previous versions but also be able
to provide the user with the IP address of the attacker or the person conducting the attack. The system
involves the integration of Wazuh with Snort tool in order to make it more efficient than IDS Suricata
which is another intrusion detection system capable of detecting all these types of attacks as mentioned.
Splunk is another tool used in this project which increases the firewall efficiency to pass the no. of bits to
be scanned and the no. of bits scanned successfully. Wazuh is used in this system as it is the best choice for
traffic monitoring and incident response than any other of its alternatives in the market. Since this system
is used in firms which are known to handle big amounts of data and for this purpose, we use Splunk tool as
it is very efficient in handling big amounts of data. Wireshark is used in this system in order to give the IDS
automation in its capability to capture and report the malicious packets found during the network scan. All
of this gives the IDS a capability of a low budget automated threat detection system. This paper gives
complete guidelines for authors submitting papers for the AIRCC Journals.
The Practical Data Mining Model for Efficient IDS through Relational DatabasesIJRES Journal
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.
Optimized Intrusion Detection System using Deep Learning Algorithmijtsrd
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
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
Survey on Host and Network Based Intrusion Detection SystemEswar Publications
With invent of new technologies and devices, Intrusion has become an area of concern because of security issues, in the ever growing area of cyber-attack. An intrusion detection system (IDS) is defined as a device or software application which monitors system or network activities for malicious activities or policy violations. It produces reports to a management station [1]. In this paper we are mainly focused on different IDS concepts based on Host and Network systems.
Autonomic Anomaly Detection System in Computer Networksijsrd.com
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.
In recent years, wireless sensor network (WSN) is used in several application areas resembling observance, tracking, and dominant in IoTs. for several applications of WSN, security is a crucial demand. However, security solutions in WSN disagree from ancient networks because of resource limitation and process constraints. This paper analyzes security solutions: TinySec, IEEE 802.15.4, SPINS, MiniSEC, LSec, LLSP, LISA, and LISP in WSN. This paper additionally presents characteristics, security needs, attacks, cryptography algorithms, and operation modes. This paper is taken into account to be helpful for security designers in WSNs.
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.
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A Study On Recent Trends And Developments In Intrusion Detection System
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 11, Issue 6 (May. - Jun. 2013), PP 27-30
www.iosrjournals.org
www.iosrjournals.org 27 | Page
A Study on Recent Trends and Developments in Intrusion
Detection System
Madura sheetal 1
, Manjunath CR 2
, Santosh Naik3
1,2
Department of Computer Science and Engineering, Jain University
3
Departments of Information Science and Engineering, Jain University
Abstract :Intrusion detection is the process of detecting unauthorized traffic on a network or a device. Intrusion
Detection Systems (IDS) are designed to detect the real-time intrusions and to stop the attack. An IDS is a
software or a physical device that monitors traffic on the network and detect unauthorized entry that violates
security policy. We present in this paper the various Neural Network approaches adopted by the different
Intrusion Detection Systems.
Artificial Intelligence plays significantly role in intrusion detection. Machine learning can also be
applied to intrusion detection systems. Artificial Neural Networks are modelled inline with the learning
processes that take place in biological systems. The Neural Networks are basically consists of a set of inputs,
some intermediate layers and one output. They are capable of identifying the patterns and its variations. They
can be “trained” to produce an accurate output for a given input. Neural Networks are capable of predicting
new observations from other observations after executing a process of so called learning from existing data.
Keywords: Intrusion detection, Neural networks, Prevention system, Security, Technique, Traffic.
I. Understanding Ids
Due to the variance in Network configuration, a number of IDS technologies have emerged. Each type
has its own advantages and disadvantage in terms of detection, configuration, and overall cost.
1.1 Detection Technologies:
The few of the categories of the Detection technologies are, Network Based, Wireless, Network Behavior
Anomaly Detection and Host-Based.
Network-Based: A Network Intrusion Detection System (NIDS) analyzes network traffic at every layer
of the OSI model for suspicious activity.
Wireless: A wireless local area network (WLAN) IDS analyzes wireless-specific traffic, including
scanning for unauthorized users trying to connect to active wireless network components.
Network Behavior Anomaly Detection: Network behavior anomaly detection (NBAD) analyzes
network traffic to identify anomalies that exists if any.
Host-Based: Host-based intrusion detection systems (HIDS) analyzes system-specific settings
including security policies, log audits and software calls.
Figure1.Characteristics of intrusion-detection systems.
1.2 Detection Types
Few types of detections include, Signature-Based Detection, Anomaly-Based Detection and Stateful
Protocol Inspection Signature -Based Detection: An IDS can use signature-based detection completely relying
on known traffic data and analyzes potentially unwanted traffic. It has a limited detection capability, but can be
2. AStudy on Recent Trends and Developments in Intrusion Detection System
www.iosrjournals.org 28 | Page
very accurate.Anomaly-Based Detection: An IDS analyzes the traffic on the network and detects incorrect,
invalid and abnormal IP packets.A hybrid or compound detection system combines both approaches. In essence,
a hybrid detection system is a signature inspired intrusion detection system that makes a decision using a
―hybrid model‖ that is based on both the normal behavior of the system and the intrusive behavior of the
Intruders Stateful Protocol Inspection: An IDS that inspects traffic at the network and transport layer including
the vendor-specific traffic in the application layer for any malicious behavior.
II. Application of AI and Allied Techniques in IDS
Artificial Intelligence contributes significantly for intrusion detection in terms of data reduction,
analyzing data to identify components and identifying the intruders. Artificial Intelligence could make the use of
Intrusion Detection Systems. They could learn the preferences of the security officers and show the kind of
alerts first that the officer has previously been most interested. As always, the hardest thing with learning AIs, is
to make them learn the right things. AIs could learn the same things as a rule-based system by watching a
security officer work. AIs could also link together events that, by themselves, are insignificant but when
combined may indicate that an attack is underway.
AI and machine learning could be applied to intrusion detection systems by using concept learning,
Clustering, Predictive learning and ability to extract relevant features from irrelevant data and the possibility of
combining relevant features into functions that identify intrusive events.
There are several different soft computing techniques and algorithms that can be successfully used to
detect intrusions. These techniques include: Fuzzy logic, Probabilistic reasoning, neural networks, Genetic
algorithms and combinations of these can also be used. For example, genetic algorithms can be used to build
neural networks and probabilistic reasoning can be built on fuzzy logic.
Neural Networks provides a value addition to IDS because of its flexible pattern recognition capability
and effectively handle intrusive events. Neural Networks are useful in identifying gradual changes to the system.
Application of AI, machine learning techniques, and neural networks could result in the development of a
comprehensive intrusion detection system.
III. Off The Shelf Ids Tools
The several typical features were usually given due importance the study of different products. They
are : flexibility of adaptation for any network which needs to be monitored, suitability for IDS architecture,
protection against malicious attacks, interoperability with various network management tools,
comprehensiveness to extend the intrusion detection mechanism to block program plug-ins viz. Java applets or
Active-X controls, event management to manage and report the event traces and update the existing attack
database, active response during occurrences of attacks, such as reconfiguration of router or a firewall and
support for the product.
The IPS market is composed of stand-alone appliances that inspect all network traffic that has passed
through frontline security devices, such as firewalls, Web security gateways and email security gateways. IPS
devices are deployed in line and perform full-stream reassembly of network traffic. They provide detection via
several methods — signatures, protocol anomaly detection, behavioral or heuristics. By being in-line, IPSs can
also use various techniques to block attacks that are identified with high confidence. The capabilities of IPS
products need to adapt to changing threats, and next-generation IPSs have evolved in response to advanced
targeted threats evading first-generation IPSs.
Gartner is an information technology research and advisory company providing technology related
insight. Research provided by Gartner is targeted at CIOs and senior IT leaders in industries that include
government agencies, high-tech and telecom enterprises, professional services firms, and technology investors.
The Following figure shows Gartner’s report on IT Security leaders.
Figure 2 Gartner’s Magic Quadrant
3. AStudy on Recent Trends and Developments in Intrusion Detection System
www.iosrjournals.org 29 | Page
IV. Open Source Tools
The Open Source tools include Snort, another freely-available vulnerability-assessment tool is Nessus,
a Linux-based vulnerability scanner (http://www.nessus.org)
Some commercial IDS providers use Snort and/or Snort signatures for their appliances, which gives Snort
credibility in this direction. But different organizations have different needs. While for big enterprises open
source IDS systems might not be enough, since they are the target of the most sophisticated attacks, the smaller
and medium-sized businesses can protect their intranet for free or at a fraction of the price of a commercial
system. However, even for the companies choosing commercial IDS, tools like Snort, OSSEC or Prelude can be
used for 'a second opinion’.
V. Intrusion Prevention Systems
IPS contains all features of IDS with two more improvements
IPS moves beyond simple attack signature detection to add vulnerability based signatures and non-signature
detection capabilities
Network IPS sensors operate in line at wire speeds to enable automated blocking and mitigation of attacks.
The new and enhanced automated techniques have improved detection abilities. These vulnerabilities
facilitate attacks on computer systems by reducing the amount of effort required by an intruder to gain access.
The structured software engineering techniques eliminates numerous potential sources of insecurity. The
available development techniques would eliminate the three types of flaws.
1. The structured software validation and verification methods will reduce errors due Design flaws which result
from inaccurate interpretation of software requirements and the improper analysis of the intended design of the
system.
2. Faults within the application occur from the development of computer code which does not follow the defined
specifications of the intended application. The complete eradication of all coding errors is extremely
difficult, if not impossible, to achieve. However, the investment in the reduction of software faults offers the
return of increasingly secure systems.
3. Operational and administrative flaws are those which result from the improper configuration of applications,
operating systems, and security systems. These flaws are also difficult to eliminate completely, but the removal
of configuration errors which are commonly known greatly enhances the security of the system.
VI. Intrusion Verification Systems
Recently, intrusion detection systems (IDSs) have been increasingly brought to task for failing to meet
the expectations of researchers and vendors. Promises that IDSs would be capable of reliably identifying
malicious activity never turned into reality. While virus scanners and firewalls have visible benefits and remain
virtually unnoticed during normal operation, intrusion detection systems are known for producing a large
number of alerts that are either not related to malicious activity or not representative of a successful attack.
Although tuning and proper configuration may eliminate the most obvious spurious alerts, the problem of the
vast imbalance between actual and false or non-relevant alerts remains.
The problem is that the concept of network-awareness is not broad enough to completely capture the
complexity that is at the core of excessive amounts of false alarms. When a sensor outputs an alert, there are
three possibilities. Alert verification is a term that we use for all mechanisms that can help to determine whether
an attack was successful or not. This information is passed to the intrusion detection system to help differentiate
between type-1 (The sensor has correctly identified a successful attack. This alert is most likely relevant) alerts
on one hand and type-2 (The sensor has correctly identified an attack, but the attack failed to meet its objectives)
and type-3 (The sensor incorrectly identified an event as an attack. The alert represents incorrect information)
alerts on the other hand. When the success of an attack is a priori impossible (e.g., no vulnerable service is
running) or cannot be verified (e.g., the attack failed because incorrect offsets were used), the IDS can react
accordingly and suppress the alert or reduce its priority.
VII. Conclusion
The study indicates that intrusion detection system will be replaced by intrusion prevention systems.
With the advent of IPS and IVS the Organizations will have cutting edge technological solutions in providing a
stronger defense against attacks.
Security is an utmost priority of any organization, but this costs the exchequer of the organization.
So, more and more organizations are leaning towards cost effective solutions like open source IDS tools which
are equally efficient in providing defense.
4. AStudy on Recent Trends and Developments in Intrusion Detection System
www.iosrjournals.org 30 | Page
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