With the ever increasing number and diverse type of attacks, including new and previously unseen attacks, the effectiveness of an Intrusion Detection System is very important. Hence there is high demand to reduce the threat level in networks to ensure the data and services offered by them to be more secure. In this paper we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection system using the layered CRFs. We set up different types of checks at multiple levels in each layer. Our framework examines various attributes at every layer in order to effectively identify any breach of security. Once the attack is detected, it is intimated through mobile phone to the system administrator for safeguarding the server system. We established experimentally that the layered CRFs can thus be more effective in detecting intrusions when compared with the other previously known techniques.
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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging
endlessly. So it is critical to protect the networks from attackers and the Intrusion detection
technology becomes popular. Therefore, it is necessary that this security concern must be articulate
right from the beginning of the network design and deployment. The intrusion detection technology is the
process of identifying network activity that can lead to a compromise of security policy. Lot of work has
been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a
novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and
manage misuse and anomaly detects
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging
endlessly. So it is critical to protect the networks from attackers and the Intrusion detection
technology becomes popular. Therefore, it is necessary that this security concern must be articulate
right from the beginning of the network design and deployment. The intrusion detection technology is the
process of identifying network activity that can lead to a compromise of security policy. Lot of work has
been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a
novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and
manage misuse and anomaly detects
Outstanding to the promotion of the Internet and local networks, interruption occasions to computer
systems are emerging. Intrusion detection systems are becoming progressively vital in retaining
appropriate network safety. IDS is a software or hardware device that deals with attacks by gathering
information from a numerous system and network sources, then evaluating signs of security complexities.
Enterprise networked systems are unsurprisingly unprotected to the growing threats posed by hackers as
well as malicious users inside to a network. IDS technology is one of the significant tools used now-a-days,
to counter such threat. In this research we have proposed framework by using advance feature selection
and dimensionality reduction technique we can reduce IDS data then applying Fuzzy ARTMAP classifier
we can find intrusions so that we get accurate results within less time. Feature selection, as an active
research area in decreasing dimensionality, eliminating unrelated data, developing learning correctness,
and improving result unambiguousness.
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.
A Performance Analysis of Chasing Intruders by Implementing Mobile AgentsCSCJournals
An Intrusion Detection System in network fetches the intrusions information from systems by using Mobile Agents aid. Intrusion Detection System detects intrusions based on the collected information and routes the intrusion. The intelligent decisions on communications, permit agents to gain their goals more efficiently and provide more survivability and security of an agent system. The proposed model showed a formal representation of information assurance in agent messaging over a dynamic network by probability of redundant routes. The proposed Intrusion Detection System, chase intruders and collect information by the Mobile Agents. Our propose architecture is an information exchange method and chasing intrusion along with a method by implementing Mobile Agents.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
A Survey: Comparative Analysis of Classifier Algorithms for DOS Attack Detectionijsrd.com
In today's interconnected world, one of pervasive issue is how to protect system from intrusion based security attacks. It is an important issue to detect the intrusion attacks for the security of network communication.Denial of Service (DoS) attacks is evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money.Significance of Intrusion detection system (IDS) in computer network security well proven. Intrusion Detection Systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. Mining approach can play very important role in developing intrusion detection system. Classification is identified as an important technique of data mining. This paper evaluates performance of well known classification algorithms for attack classification. The key ideas are to use data mining techniques efficiently for intrusion attack classification. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
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.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
INTRUSION DETECTION 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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
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.
A Performance Analysis of Chasing Intruders by Implementing Mobile AgentsCSCJournals
An Intrusion Detection System in network fetches the intrusions information from systems by using Mobile Agents aid. Intrusion Detection System detects intrusions based on the collected information and routes the intrusion. The intelligent decisions on communications, permit agents to gain their goals more efficiently and provide more survivability and security of an agent system. The proposed model showed a formal representation of information assurance in agent messaging over a dynamic network by probability of redundant routes. The proposed Intrusion Detection System, chase intruders and collect information by the Mobile Agents. Our propose architecture is an information exchange method and chasing intrusion along with a method by implementing Mobile Agents.
A technical review and comparative analysis of machine learning techniques fo...IJECEIAES
Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICSIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
Machine learning in network security using knime analyticsIJNSA Journal
Machine learning has more and more effect on our every day’s life. This field keeps growing and expanding into new areas. Machine learning is based on the implementation of artificial intelligence that gives systems the capability to automatically learn and enhance from experiments without being explicitly
programmed. Machine Learning algorithms apply mathematical equations to analyze datasets and predict values based on the dataset. In the field of cybersecurity, machine learning algorithms can be utilized to train and analyze the Intrusion Detection Systems (IDSs) on security-related datasets. In this paper, we tested different machine learning algorithms to analyze NSL-KDD dataset using KNIME analytics.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
EFFICACY OF ATTACK DETECTION CAPABILITY OF IDPS BASED ON ITS DEPLOYMENT IN WI...IJNSA Journal
Intrusion Detection and/or Prevention Systems (IDPS) represent an important line of defence against a variety of attacks that can compromise the security and proper functioning of an enterprise information system. Along with the widespread evolution of new emerging services, the quantity and impact of attacks have continuously increased, attackers continuously find vulnerabilities at various levels, from the network itself to operating system and applications, exploit them to crack system and services. Network defence and network monitoring has become an essential component of computer security to predict and prevent attacks. Unlike traditional Intrusion Detection System (IDS), Intrusion Detection and Prevention System (IDPS) have additional features to secure computer networks.
In this paper, we present a detailed study of how deployment of an IDPS plays a key role in its performance and the ability to detect and prevent known as well as unknown attacks. We categorize IDPS based on deployment as Network-based, host-based, and Perimeter-based and Hybrid. A detailed comparison is shown in this paper and finally we justify our proposed solution, which deploys agents at host-level to give better performance in terms of reduced rate of false positives and accurate detection and prevention.
As the Supervisory Control and Data Acquisition (SCADA) system are deployed in infrastructures which are critical to the survival of a nation, they have emerged as a potential terrain for cyber-war, thus attracting the considered attention of ‘nation-states’. The analysis of worms like ‘stuxnet’ ‘flame’ and ‘duqu’ reveals the hand of a ‘nation-state’ in their design and deployment. Hence, the necessity to understand various issues in the defence of SCADA systems arises. The forensics of the SCADA system provide deep insight into the design and deployment of the worm (the malware) once the system is attacked. This is precisely the scope of this essay.
A Survey: Comparative Analysis of Classifier Algorithms for DOS Attack Detectionijsrd.com
In today's interconnected world, one of pervasive issue is how to protect system from intrusion based security attacks. It is an important issue to detect the intrusion attacks for the security of network communication.Denial of Service (DoS) attacks is evolving continuously. These attacks make network resources unavailable for legitimate users which results in massive loss of data, resources and money.Significance of Intrusion detection system (IDS) in computer network security well proven. Intrusion Detection Systems (IDSs) have become an efficient defense tool against network attacks since they allow network administrator to detect policy violations. Mining approach can play very important role in developing intrusion detection system. Classification is identified as an important technique of data mining. This paper evaluates performance of well known classification algorithms for attack classification. The key ideas are to use data mining techniques efficiently for intrusion attack classification. To implement and measure the performance of our system we used the KDD99 benchmark dataset and obtained reasonable detection rate.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
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.
Intrusion detection and anomaly detection system using sequential pattern miningeSAT Journals
Abstract
Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these types of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model.
Keywords — Anomaly Detection, Modified Apriori Algorithm, Misuse detection, Sequential Pattern Mining
INTRUSION DETECTION 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.
HYBRID ARCHITECTURE FOR DISTRIBUTED INTRUSION DETECTION SYSTEM IN WIRELESS NE...IJNSA Journal
In order to the rapid growth of the network application, new kinds of network attacks are emerging endlessly. So it is critical to protect the networks from attackers and the Intrusion detection technology becomes popular. Therefore, it is necessary that this security concern must be articulate right from the beginning of the network design and deployment. The intrusion detection technology is the process of identifying network activity that can lead to a compromise of security policy. Lot of work has been done in detection of intruders. But the solutions are not satisfactory. In this paper, we propose a novel Distributed Intrusion Detection System using Multi Agent In order to decrease false alarms and manage misuse and anomaly detects.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
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.
Internal security on an ids based on agentscsandit
An Intrusion Detection System (IDS) can monitor different events that may occur in a
determined network or host, and which affect any network security service (confidentiality,
integrity, availability). Because of this, an IDS must be flexible and it must detect and trace
each alert without affecting the system´s performance. On the other hand, agents ina Multi-
Agent system have inherent security problems due to their mobility; that’s why we propose some
techniques in order to provide internal security for the agents belonging to the system. The
deployed IDS works with a multiagent platform and each component inside the infrastructure is
verified using security techniques in order to provide integrity. Likewise, the agents can
specialize in order to carry out specific jobs, for example monitoring TCP, UDP traffic, etc. The
IDS can work without interfering in the system's performance. In this article we present a
hierarchical IDS deployment with internal security on a multiagent system, using a platform
named BESA with its processes, functions and results.
INTERNAL SECURITY ON AN IDS BASED ON AGENTScscpconf
An Intrusion Detection System (IDS) can monitor different events that may occur in a determined network or host, and which affect any network security service (confidentiality,
integrity, availability). Because of this, an IDS must be flexible and it must detect and trace each alert without affecting the system´s performance. On the other hand, agents ina MultiAgent system have inherent security problems due to their mobility; that’s why we propose some techniques in order to provide internal security for the agents belonging to the system. The deployed IDS works with a multiagent platform and each component inside the infrastructure is verified using security techniques in order to provide integrity. Likewise, the agents can
specialize in order to carry out specific jobs, for example monitoring TCP, UDP traffic, etc. The IDS can work without interfering in the system's performance. In this article we present a hierarchical IDS deployment with inter nal security on a multiagent system, using a platform named BESA with its processes, functions and results.
INTERNAL SECURITY ON AN IDS BASED ON AGENTSIJNSA Journal
An Intrusion Detection System (IDS) can monitor different events that may occur in a determined network or host, and which affect any network security service (confidentiality, integrity, availability). Because of this, an IDS must be flexible and it must detect and trace each alert without affecting the system´s performance. On the other hand, agents ina Multi-Agent system have inherent security problems due to their mobility; that’s why we propose some techniques in order to provide internal security for the agents belonging to the system. The deployed IDS works with a multiagent platform and each component inside the infrastructure is verified using security techniques in order to provide integrity. Likewise, the agents can specialize in order to carry out specific jobs, for example monitoring TCP, UDP traffic, etc. The IDS can work without interfering in the system's performance. In this article we present a hierarchical IDS deployment with internal security on a multiagent system, using a platform named BESA with its processes, functions and results.
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.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Articles - International Journal of Network Security & Its Applications (IJNSA)IJNSA Journal
International Journal of Network Security & Its Applications (IJNSA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the computer Network Security & its applications. The journal focuses on all technical and practical aspects of security and its applications for wired and wireless networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern security threats and countermeasures, and establishing new collaborations in these areas.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
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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
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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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
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• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
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IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BASED ON DEVIANT SYSTEM BEHAVIOUR
1. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
DOI : 10.5121/ijnsa.2013.5214 179
IMPROVED IDS USING LAYERED CRFS WITH
LOGON RESTRICTIONS AND MOBILE ALERTS
BASED ON DEVIANT SYSTEM BEHAVIOUR
Arpitha M1
, Geetha V1
,
Gowranga K H2
and Bhakthavathsalam R2
1
Department of Information Science and Engineering
Alpha College Of Engineering, Bangalore, India
arpitha119@gmail.com, geethaanjali78@gmail.com
2
Supercomputer Education and Research Center
Indian Institute of Science, Bangalore, India
gowranga@serc.iisc.ernet.in, bhaktha@serc.iisc.ernet.in
ABSTRACT
With the ever increasing numberand diverse type of attacks, including new and previouslyunseen attacks,
the effectiveness of an Intrusion DetectionSystem is very important. Hence there is high demand to reduce
the threat level in networks to ensure the data and services offered by them to be more secure. In this paper
we developed an effective test suite for improving the efficiency and accuracy of an intrusion detection
system using the layered CRFs. We set up different types of checks at multiple levels in each layer.Our
framework examines various attributes at every layer in order to effectively identify any breach of security.
Once the attack is detected, it is intimated throughmobile phone to the system administrator for
safeguarding the server system. We established experimentally that the layered CRFs can thus be more
effectivein detecting intrusions when compared with the other previouslyknown techniques.
KEYWORDS
Network Security, Intrusion Detection, Layered Approach, Conditional Random Fields, Mobile Phones
1. INTRODUCTION
The current state of network is vulnerable they are prone to increasing number of attacks. Thus
securing a network from unwanted malicious traffic is of prime concern. A computer network
needs to provide continuous services, such as e-mail to users, while on the other it stores huge
amount of data which is of vital significance. Recently, there has been increasing concern over
safeguarding the vast amount of data stored in a network from malicious modifications and
disclosure to unauthorized individuals. Intrusion Detection Systems (IDS) [1] are based on two
concepts; matching of the previously seen and hence known anomalous patterns from an internal
2. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
180
database of signatures or building profiles based on normal data and detecting deviations from the
expected behaviour[2].Based on the mode of deployment, the Intrusion Detection Systems are
classifiedas Network based [3] andHost based [4]. Network based systems make a decision by
analysing the network logs and packet headers from the incoming and outgoing packets. Host
based systems monitor's individual systems and uses system logs extensively to make any
decision. Intrusion Detection Systems are either Signature based or Behaviour based [5]. The
Signature based systems build a model based on the available knowledge of the attacks. The
Behaviour based systems which build a model based on the available knowledge of the normal
use of the system.We propose and evaluate the use of the CRFs [6] also which is a novel
technique for the task of Intrusion Detection along with Layered Approach. Further, our system
can be used as a standalone system monitoring an entire Network or a single Host or even a single
Application running on a particular host.
1.1 Intrusion Detection
Intrusion detection [7] is the process of monitoring computers or networks for unauthorized
entrance, activity, or file modification. An IDS (Intrusion Detection System) is a device or
application used to inspect all network traffic, thereby detecting if a system is being targeted by a
network attack such as a denial of service attack. In some cases the IDS may also respond to
anomalous or malicious traffic by taking action such as blocking the user or source IP address
from accessing the network. IDS protect a network and attempt to prevent intrusions. They don’t
fully guarantee security, but when used with security policy, vulnerability assessments, data
encryption, user authentication, access control, and firewalls, they can greatly enhance network
safety [8].
Intrusion detection systems serve three essential security functions: they monitor, detect, and
respond to unauthorized activity by company insiders and outsiders. Intrusion detection systems
use policies to define certain events that, if detected will issue an alert. Certain intrusion detection
systems have the capability of sending out alerts, so that the administrator of the IDS will receive
a notification of a possible security incident. Many intrusion detection systems not only recognize
a particular incident and issue an appropriate alert, they also respond automatically to the event.
Such a response might include logging off a user, disabling a user account, and launching of
scripts.
2. NEW SCHEME FOR ROBUST IDS
Intrusion detection as a discipline is fairly immature. Commercially available examples of
successful intrusion detection systems are limited, although the state of the art is progressing
rapidly. The whole concept of our paper is to build an intrusion detection system which is very
accurate in detection of request from unknown computers and which is very fast to respond to
such intrusions taking place in system which gives efficiency [9] to the system and intimating the
administrator about the intrusions through the mobile phone. To achieve this system, we have
integrated the properties of conditional random fields and the layered approach.
3. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
181
2.1 Existing System
There are a number of methods and frameworks been proposed and many systems have been built
to detect intrusions. Various techniques such as association rules [10], clustering, naive Bayes
classifier, support vector machines, genetic algorithms, artificial neural networks, and others have
been applied to detect intrusions. These existing systems suffer from a wide range of problems.
a. The features are limited to the entry level of the packets and require the no. of records to
be large. They tend to produce a large number of rules that increases the system's
complexity.
b. Some methods consider the features independently and are unable to capture the
relationship between different features of a single record. This further degrades the attack
detection strength of the system.
c. Some existing systems are attack specific and hence they would build networks which
rapidly increases as the detection load increases.
2.2 Proposed System
In our proposed system we describe the Layer-based Intrusion Detection System (LIDS) [11]
[12]. The LIDS draws its motivation from what we call as the Airport Security model, where a
number of security checks are performed one after the other in a sequence. Similar to this model,
the LIDS represents a sequential Layered Approach [13] and is based on ensuring availability,
confidentiality, and integrity of data and (or) services over a network.
The goal of using a layered model is to reduce computation and the overall time required to detect
anomalous events. The time required to detect an intrusive event is significant and can be reduced
by eliminating the communication overhead among different layers. We define four layers they
are Probe layer, DoS layer, R2L layer, and U2R layer. Each layer is separately trained with a
small set of features. The layers essentially act as filters that block any anomalous connection,
thereby eliminating the need of further processing at subsequent layers enabling quick response to
intrusion.
The effect of such a sequence of layers is that the anomalous events are identified and blocked as
soon as they are detected. Once the attack is detected, it is intimated through mobile phone to the
system administrator for safe guarding the server system.
We implement the LIDS and select four set of features which reduces the computational time.
Methods such as naive Bayes [14] assume independence among the observed data. To balance
this trade-off, we use the CRFs that are more accurate, though expensive, but we implement the
Layered Approach to improve overall system performance.
Our proposed system, Layered CRFs, performs significantly better than other systems.
4. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
182
Figure 1. Proposed System
3. IMPLEMENTATION
Implementation is the stage when the theoretical design is turned out into a working system. Thus
it can be considered to be the most critical stage in achieving a successful new system and in
giving the user, confidence that the new system will work and be effective. The implementation
stage involves careful planning, investigation of the existing system and it’s constraints on
implementation, designing of methods to achieve changeover and evaluation of changeover
methods.
3.1 Layered Approach for Intrusion Detection
Layer-based Intrusion Detection System (LIDS) draws its motivation from what we call as the
Airport Security model, where a number of security checks are performed one after the other in a
sequence. Similar to this model, the LIDS represents a sequential Layered Approach and is based
on ensuring availability, confidentiality, and integrity of data and (or) services over a network.
Figure 2 gives a generic representation of the framework. The goal of using a layered model is to
reduce computation and the overall time required to detect anomalous events. The time required
to detect an intrusive event is significant and can be reduced by eliminating the communication
5. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
183
overhead among different layers. Every layer in the LIDS framework is trained separately and
then deployed sequentially.
Figure 2. Layered Approach for Intrusion Detection
We define four layers that correspond to the four attack groups [15]. They are Probe layer, DoS
layer, R2L layer, and U2R layer. Each layer is then separately trained with features. Feature
selection is significant for Layered Approach. The layers essentially act as filters that block any
anomalous connection, thereby eliminating the need of further processing at subsequent layers
enabling quick response to intrusion. The effect of such a sequence of layers is that the anomalous
events are identified and blocked as soon as they are detected. Hence, we implement the LIDS
and select four set of features for every layer. In many situations, there is a trade-off between
efficiency and accuracy of the system and there can be various avenues to improve system
performance. To balance this trade-off, we use the CRFs that are more accurate, though
expensive, but we implement the Layered Approach to improve overall system performance. The
performance of our proposed system, Layered CRFs is comparable to that of the decision trees
and the naive Bayes, and our system has higher attack detection accuracy.
3.2 Conditional Random Fields for Intrusion Detection
Conditional models are systems that are used to model the conditional distribution [16] over a set
of random variables. Such models have been extensively used in the natural language processing
tasks. Conditional models offer a better framework and can be used to model rich overlapping
features among the visible observations. CRFs are undirected graphical models used for sequence
tagging.
The CRFs have proven to be very successful in such tasks, as they do not make any unwarranted
assumptions about the data. Hence, we explore the suitability of CRFs for intrusion detection.
System may consider features such as “logged in” and “number of file creations.”
6. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
184
When these features are analyzed individually, they do not provide any information that can aid
in detecting attacks. However, when these features are analyzed together, they can provide
meaningful information.
Figure 3. Conditional Random Field
3.3 Integrating Layered Approach with Conditional Random Fields
A natural choice is to integrate them to build a single system that is accurate in detecting attacks
and efficient in operation.
Probe layer
The probe attacks are aimed at acquiring information about the target network from a source that
is often external to the network. Hence, basic connection level features such as the “duration of
connection” and “source bytes” are significant while features like “number of files creations” and
“number of files accessed” are not expected to provide information for detecting probes.
DoS layer
For the DoS layer, traffic features such as the “percentage of connections having same destination
host and same service” and packet level features such as the “source bytes” and “percentage of
packets with errors” is significant.
R2L layer
The R2L attacks are one of the most difficult to detect as they involve the network level and the
host level features. We therefore select both the network level features such as the “duration of
7. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
185
connection” and “service requested” and the host level features such as the “number of failed
login attempts” among others for detecting R2L attack.
U2R layer (User to Root attacks)
The U2R attacks involve the semantic details that are very difficult to capture at an early stage.
Such attacks are often content based and target an application. Hence, for U2R attacks, we select
features such as “number of file creations” and “number of shell prompts invoked,” while we
ignored features such as “protocol” and “source bytes.”
Figure 4. Integrating Layered Approach with Conditional Random Fields
3.4 Time Scheduling of Users
With the increasing number of user’s everyday on the internet, networks are getting burdened
with a huge amount of requests, processes, services etc. Every user performs some or the other
function when they are using the internet this increases the load on the network. In our system we
have scheduled a particular day and time for the users who are a part of an organisation,
restricting their usage to prevent intrusions and wastage of bandwidth in the network. In simple
words they are assigned a particular day and time to login to their accounts and work on their
requirements.
We have symbolised the days of a week as 0-6 depicting Sunday-Saturday and time on a 24 hour
clock. This feature is added in the database and access will be given only to those users who login
8. International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.2, March 2013
186
at the right schedule. Users who do not login at the right schedule are denied access and will be
treated as intruders.
3.5 Intrusion Detected Message Sent to System Administrators Mobile
The mobile device can be used to keep oneself informed about the attacks. The corresponding
error messages are generated and are intimated to the server which schedules the appropriate
actions. Mobile alerts are sent to the server administrator’s mobile through usage of a GSM
modem connected to the COM port of your computer and making sure that the Java
communication API is installed in your system. We also carefully consider several parameters
such as text message centre number found in your mobile in the SMS settings menu and the baud
rate and type of flow control for receiving, type of flow control for sending, the number of data
bits, the number of stop bits, and the type of parity.
In a nutshell, intrusion detection systems do exactly as the name suggests: they detect possible
intrusions. More specifically, IDS tools aim to detect computer attacks and/or computer misuse,
and to alert the proper individuals upon detection.
Through various methods, both detect when an intruder/attacker/burglar is present, and both
subsequently issue some type of warning or alert. Using the previous example, firewalls can be
thought of as a fence or a security guard placed in front of a house. They protect a network and
attempt to prevent intrusions, while IDS tools detect whether or not the network is under attack or
has, in fact, been breached. IDS tools thus form an integral part of a thorough and complete
security system.
Figure 5. Proposed IDS system Activities
Intrusion detection systems [17] serve three essential security functions: they monitor, detect, and
respond to unauthorized activity by company insiders and outsider intrusion. An IDS installed on
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a network provides much the same purpose as a burglar alarm system installed in a house.
Intrusion detection systems use policies to define certain events that, if detected will issue an
alert. In other words, if a particular event is considered to constitute a security incident, an alert
will be issued if that event is detected. Many intrusion detection systems not only recognize a
particular incident and issue an appropriate alert, they also respond automatically to the event.
Such a response might include logging off a user, disabling a user account, and launching of
scripts. Our system has the capability of sending out alerts, so that the administrator of the IDS
will receive a notification of a possible security incident in the form of a page, email, or SNMP
trap [18].
3.6 Proposed Algorithm
Step 1: Select the number of layers, n, for the complete system.
Step 2: Separately perform features selection for each layer.
Step 3: Plug in the layers sequentially such that only the connections labelled as normal are
passed to the next layer
Step 4: For each (next) test instance perform Steps 5 through 8.
Step 5: Test the instance and label it either as attack or normal.
Step 6: If the instance is labelled as an attack, block it and then identify it as an attack with the
corresponding layer name at which it is detected and go to step 4. Pass the sequence to
next layer.
Step 7:If the current layer is not the last layer in the system, test the instance and go to step 6.
Else go to step 8.
Step 8: Test the instance and label it either as normal or as an attack. If the instance is labelled as
an attack, block it and identify it as an attack corresponding to the layer name.
Step 9: If the instance is labelled as an attack at any layer then intimate it to system admin’s
mobile with a corresponding appropriate message of attack.
4. RESULTS
We have represented the results for every operation that is performed as per the proposed
algorithm. Our results confirm that the implementations that are carried out are
effectively displaying the outcomes accurately.
We have produced results for eight possible conditions on the use of four CRFs. type of a
system is very much suited in an organizational network. Finally, our system has the advantage
that the number of layers can be increased or decreased depending upon the environment in which
the system is deployed, giving flexibility to the network administrator.
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Figure 6. Sequence of checks for Valid User.
Figure 7. Intrusion detected at User Level
For every valid user the security checks are followed in sequence in the given time schedule and
the necessary action is taken. At the first level the user level agent gets activated and authenticates
the user. At the second level the process level agent gets activated and the user can use the
process allocated. At the next level the packet level agent gets activated and the user is allowed to
transmit files. Once all the necessary operations of the user is fulfilled the client panel sucessfully
terminates.
At the first level user is checked for authentication and if he is not authenticated he is treated as
an intruder. Next he is checked for the use of processes and if he is violating the allocated process
uasage he is treated as a process level intruder. At the third level if the file transmissions are
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crossing the fixed bytes of data he is treated as a packet level intruder.Adding to al these even
when the user tries to access at a time which is not scheduled he will be treated as an intuder [19].
Figure 8. Intrusion detected at Process Level.
Figure 9. Intrusion detected at Packet Level.
The results represent the intrusions detected at various levels of the security checks. For an
invalid user or intruder the security checks are explicit. All the events of intrusions are alerted to
the sytem administrator to his mobile phone to ensure that the intuder is blocked at the level at
which he is detected ensuring security to the IDS.
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Figure 10. Intrusion detection at Untime Login of user.
From the above results it can be concluded that our proposed system is capable of detecting
intrusions at various layers by using layered conditional random fields and when detected they
will be first intimated to the system administrator at the server side so that necessary actions can
be taken. The particular intruder will be denied of access thereby indicating that the intruder is
blocked at a particular level.
5. CONCLUSIONS
As security incidents become more numerous, IDS tools are becoming increasingly necessary.
They round out the security factor, working in conjunction with other information security tools,
such as firewalls, and allow for the complete supervision of all network activity. In our project we
have implemented a system for building robust and efficient intrusion detection systems by
implementing the layered conditional random fields using mobile phones.
Ideally, the best IDS tools combine both approaches. That way, the user gets comprehensive
coverage, making sure to guard against as many threats as possible. It is clear that using intrusion
detection systems is an important and necessary tool in the security manager's arsenal.
Our system addresses the problem of finding intruders effectively and blocking them as soon as
they are detected. The Layered Approach is a signature based system and the Conditional
Random Fields is an anomaly based system thus combining these both systems would result in a
hybrid system. Taking a thread from the integrated approach we have established scheduled user
login and successful communication with the system administrator through the mobile phones.
Our system can help in identifying an attack once it is detected at a particular layer, which
expedites the intrusion mechanism, thus minimizing the impact of an attack. Once the attack is
detected, it is intimated through mobile phone to the system administrator for safe guarding the
server system. This type of a system is very much suited in an organizational network. Finally,
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our system has the advantage that the number of layers can be increased or decreased depending
upon the environment in which the system is deployed, giving flexibility to the network
administrator.
ACKNOWLEDGEMENT
The authors sincerely thank the authorities of Supercomputer Education and Research Center,
Indian Institute of Science for the encouragement and support.
REFERENCES
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[18] Rebecca Bace, “An Introduction to Intrusion Detection and Assessment for System and Network
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[19] Arpitha M, Geetha V, Gowranga K H and Bhakthavathsalam R, (2013) “Test Suite for Intrusion
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AUTHORS
Arpitha M has obtained her B.E. degree from the Dept of Information Science and Engineering, Alpha
College of Engineering affiliated to Visvesvaraya Technological University. She has successfully
completed her final semester project at IISc. She has presented a paper at the NetCom2012 conference. Her
interests are Wireless Technology and Network Security.
Geetha V has obtained her B.E. degree from the Dept of Information Science and Engineering, Alpha
College of Engineering, Bangalore affiliated to Visvesvaraya Technological University. She has
successfully completed her final semester project at IISc. She has published a paper in the NCS-2012
conference. Her interests are Network Security & Mobile Communication.
Mr.Gowranga K H is currently working as a Scientific Assistant in Supercomputer Education and
Research Center, IISc, Bangalore. His research interests include Wireless Networks, Webmail Systems, and
Digital Communication.
Dr.Bhakthavathsalam R is presently working as a Senior Scientific Officer in SERC, IISc, Bangalore. His
areas of interests are Electromagnetics, Wireless Networks and Pervasive Computing and Communication.
He is a Member of ACM and CSI.