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.
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.
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.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
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.
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.
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.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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
A BAYESIAN CLASSIFICATION ON ASSET VULNERABILITY FOR REAL TIME REDUCTION OF F...IJNSA Journal
IT assets connected on internetwill encounter alien protocols and few parameters of protocol process are exposed as vulnerabilities. Intrusion Detection Systems (IDS) are installed to alerton suspicious traffic or activity. IDS issuesfalse positives alerts, if any behavior construe for partial attack pattern or the IDS lacks environment knowledge. Continuous monitoring of alerts to evolve whether, an alert is false positive or not is a major concern. In this paper we present design of an external module to IDS,to identify false positive alertsbased on anomaly based adaptive learning model. The novel feature of this design is that the system updates behavior profile of assets and environment with adaptive learning process.A mixture model is used for behavior modeling from reference data. The design of the detection and learning process are based on normal behavior and of environment. The anomaly alert identification algorithm isbuiltonSparse Markov Transducers (SMT) based probability.The total process is presented using real-time data. The Experimental results are validated and presentedwith reference to lab environment.
IMPROVED IDS USING LAYERED CRFS WITH LOGON RESTRICTIONS AND MOBILE ALERTS BAS...IJNSA Journal
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.
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.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
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.
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 Survey on Various Data Mining Technique in Intrusion Detection SystemIOSRjournaljce
The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...ijaia
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A Survey on Different Machine Learning Algorithms and Weak Classifiers Based ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal
distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R)
attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,
these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of
KDD and NSL-KDD datasets using different classifiers in WEKA.
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.
Wmn06MODERNIZED INTRUSION DETECTION USING ENHANCED APRIORI ALGORITHM ijwmn
Communication networks are essential and it will create many crucial issues today. Nowadays, we
consider that the firewalls are the first line of defense but that policies cannot meet the particular
requirements of needed process to achieve security. Most of the research has been done in this area but
we are lagging to achieve security needs. Already many models such as ADAM, DHP, LERAD and
ENTROPHY are proposed to resolve security problems but we need an efficient model to detect new types
of various intrusions within the entire network. In this paper, we proposed to design a modernized
intrusion detection system which consist of two methods such as anomaly and misuse detection. Both are
integrated and also used to detect novel attacks. Our system proposed to discover temporal pattern of
attacker behaviors, which is profiled using an algorithm EAA (Enhanced Apriori Algorithm). This is
experimented with a simple interface to display the behaviors of attacks effectively
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
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.
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 Survey on Various Data Mining Technique in Intrusion Detection SystemIOSRjournaljce
The intrusion detection plays an essential role in computer security. Data Mining refers to the process of extracting hidden, previously unknown and useful information from large databases. Thus data mining techniques help to detect patterns in the data set and use these patterns to detect future intrusions. Data Mining based Intrusion Detection System is combined with Multi-Agent System to improve the performance of the IDS. This paper concerned with the brief review of comparative study on applied data mining based intrusion detection techniques with their merit and demerits. This paper relay more number of applications of the data mining and also focuses extent of the data mining which will useful in the further research.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A SURVEY ON DIFFERENT MACHINE LEARNING ALGORITHMS AND WEAK CLASSIFIERS BASED ...ijaia
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R) attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So, these result in model not able to efficiently learn the characteristics of rare categories and this will result in poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of KDD and NSL-KDD datasets using different classifiers in WEKA.
A Survey on Different Machine Learning Algorithms and Weak Classifiers Based ...gerogepatton
Network intrusion detection often finds a difficulty in creating classifiers that could handle unequal
distributed attack categories. Generally, attacks such as Remote to Local (R2L) and User to Root (U2R)
attacks are very rare attacks and even in KDD dataset, these attacks are only 2% of overall datasets. So,
these result in model not able to efficiently learn the characteristics of rare categories and this will result in
poor detection rates of rare attack categories like R2L and U2R attacks. We even compared the accuracy of
KDD and NSL-KDD datasets using different classifiers in WEKA.
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.
Comparative Study on Machine Learning Algorithms for Network Intrusion Detect...ijtsrd
Network has brought convenience to the earth by permitting versatile transformation of information, however it conjointly exposes a high range of vulnerabilities. A Network Intrusion Detection System helps network directors and system to view network security violation in their organizations. Characteristic unknown and new attacks are one of the leading challenges in Intrusion Detection System researches. Deep learning that a subfield of machine learning cares with algorithms that are supported the structure and performance of brain known as artificial neural networks. The improvement in such learning algorithms would increase the probability of IDS and the detection rate of unknown attacks. Throughout, we have a tendency to suggest a deep learning approach to implement increased IDS and associate degree economical. Priya N | Ishita Popli "Comparative Study on Machine Learning Algorithms for Network Intrusion Detection System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38175.pdf Paper URL : https://www.ijtsrd.com/computer-science/computer-network/38175/comparative-study-on-machine-learning-algorithms-for-network-intrusion-detection-system/priya-n
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.
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.
INTRUSION DETECTION SYSTEM CLASSIFICATION USING DIFFERENT MACHINE LEARNING AL...ijcsit
Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.
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.
ATTACK DETECTION AVAILING FEATURE DISCRETION USING RANDOM FOREST CLASSIFIERCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion. Our observations confirm the conjecture
that both the feature selection and stochastic based genetic operators improves the accuracy and the
effectiveness. The training time is shown to be reduced tremendously by 98.59% and accuracy improved to
98.75%.
Attack Detection Availing Feature Discretion using Random Forest ClassifierCSEIJJournal
The widespread use of the Internet has an adverse effect of being vulnerable to cyber attacks. Defensive
mechanisms like firewalls and IDSs have evolved with a lot of research contributions happening in these
areas. Machine learning techniques have been successfully used in these defense mechanisms especially
IDSs. Although they are effective to some extent in identifying new patterns and variants of existing
malicious patterns, many attacks are still left as undetected. The objective is to develop an algorithm for
detecting malicious domains based on passive traffic measurements. In this paper, an anomaly-based
intrusion detection system based on an ensemble based machine learning classifier called Random Forest
with gradient boosting is deployed. NSL-KDD cup dataset is used for analysis and out of 41 features, 32
features were identified as significant using feature discretion.
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
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.
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.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
MACHINE LEARNING IN NETWORK SECURITY USING KNIME ANALYTICS
1. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
DOI: 10.5121/ijnsa.2019.11501 1
MACHINE LEARNING IN NETWORK SECURITY
USING KNIME ANALYTICS
MuntherAbualkibash
School of Information Security and Applied Computing, College of Technology, Eastern
Michigan University, Ypsilanti, MI, USA
ABSTRACT
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.
KEYWORDS
Network Security, KNIME, NSL-KDD, and Machine Learning
1. INTRODUCTION
In today’s connected world, where billions of people access the internet, anything that depends on
the internet for communication, or is connected to a computer or any type of smart device, can be
affected by several kinds of cyber attacks. As a result, many organizations, either public or
private, have to deal with continuous and complicated different types of cyber attacks and cyber
threats. The fact that cyber threats and cyber attacks now permeate every facet of society shows
why cybersecurity is crucially important.
Cybersecurity is the action of securing programs, networks, and systems from any cyberattacks.
These cyberattacks are mainly intended at gaining access to, altering, or deleting critical data;
stealing money from users; or stopping usual business operations.
Intrusion Detection System (IDS) is one of the important and dynamic areas to handle
cyberattacks.IDS is an implementation or a technique which can detect an attack attempt by
analyzing the activity of network or system then IDS will raise the alarm. Any system that can
decide for further steps is named Intrusion Prevention System (IPS). The role of IDS is to raise
the security level by identifying malicious and suspicious events that could be detected in a
computer or network system.
One of the most popular study areas in intrusion detection is anomaly-based and signature-based
detection. Anomaly-based intrusion detection talks about the case of detecting untypical events in
the network traffic that do not follow the normal patterns. It is presumed that anything that is
untypical or, in another word, anomalous could be critical and to some extent associated with
some security events. Signature-based intrusion detection tasks is to detect attacks by looking for
specific patterns where these detected patterns are referred to as signatures.[1] This paper is
2. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
2
organized as follows: Section two gives a brief introduction about Intrusion Detection Systems
(IDSs). Section three talks about NSL-KDD dataset. Section four summarizes the tested machine
learning algorithms. Section five presents the experiments and results. The last section is the
conclusion.
2. INTRUSION DETECTION SYSTEMS(IDSS)
2.1. Intrusion Detection Systems Categories
IDSs can be recognized based on two different categories:
Host-based Intrusion Detection Systems (HIDS): They can scan and watch the system
actions on which it has been installed. HIDS might detect any changes in the integrity of
files of any file system and analyze log files to look for any malicious or suspicious
activity.
Network-based Intrusion Detection Systems: They focus on scanning and watching the
network infrastructure. They analyze the packets flow of the network and examining
packets' headers and contents to detect any possible attack on the network.
As mentioned before, two different actions are applied by both types of IDS to analyze
data:
Signature-based Intrusion Detection Systems: Such systems can detect known attacks by
comparing them with stored patterns, however, it can't recognize new attacks. So the
detection will be based on signatures of known attacks as well as any rules determined by
a system administrator.
Anomaly-based Intrusion Detection Systems: Such systems can create a model based on
normal system activity, and then use the built model to evaluate observed activity to
determine if the observed activity is an anomaly.
2.2. Anomaly Detection Methods
There are many algorithms and methods from different classes used by researchers to implement
anomaly detection in network traffic. Some techniques are implemented based on a statistical
point of view where statistics are used to compute and to determine if the observed case is an
anomaly[2]. Other techniques are based on machine learning algorithms that are applied to detect
an anomaly.
Machine learning methods can be classified into three categories:
Supervised learning: A training set have labelled examples and an algorithm will match a
new observation with just one class.
Unsupervised learning: Training set does not have labels or any details about a possible
group in it. In the time of training, the algorithm sets groups and determines its level of
similarity.
Semi-supervised learning: Training set has a small amount of labelled example with a
large number of unlabelled examples. In other words, semi-supervised learning takes
place between supervised learning, where training data is labelled, and unsupervised
learning, where training data is not labelled.
3. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
3
Algorithms from the first two categories have been implemented in a network anomaly detection
problem. It is expected that attacks can be detected since they are abnormal events and will be
classified by the algorithm model. Models are used by machine learning algorithms to analyze
network traffic.
3. DATASETS
3.1. NLS-KDD Dataset
Over the last decades, a few datasets have been utilized to examine network anomaly detection
systems. The most well-known dataset is KDDcup99. This dataset contains about 4,900,000
samples where 300,000 represent 24 different attack types. Every sample is described by 41
features and labelled as either an attack or normal. However, KDDcup99 has been criticized by
many researchers because it has many redundant records and irregularities[3, 4].
To fix this problem, a new dataset, NSL-KDD was proposed, that contains selected records of the
entireKDDcup99 dataset. The differences between KDDcup99 and NSL-KDD are that the
redundant records in NSL-KDD have been deleted from the training set to make sure that the
built classifiers are not biased. Also, duplicated records have been excluded to have a better
detection rate once applied to some methods. As a result, it is highly recommended to stop using
the KDDcup99 dataset and to use the NSL-KDD dataset instead to evaluate machine learning
algorithms because it solves the issues in the KDDcup99.
3.1.1. Attacks categories in the NSL-KDD dataset
There are four attacks categories represented in the NSL-KDD dataset:
1. A denial-of-Service attack (DoS) is an attack intended to freeze or powering down a
machine or network by forcing it to be unreachable to its legitimate users. DoS attacks
achieve this by flooding the target with traffic or transmitting it information that causes a
crash. As a result, the DoS attack blocks intended users of the service or resource they
looking for.
2. User to Root Attack (U2R) is an attack where the attacker logs in on the system with a
normal user account then make the effort to find any vulnerability in the system and use it
to obtain the root or the admin privileges.
3. Remote to Local Attack(R2L)is an attack where the attacker sends packets to a targeted
machine in which the attacker does not have access to it to expose any vulnerability in the
targeted the machines and exploit privileges that only a local user can have on that
machine.
4. Probing Attack is an attack where the attacker scans a targeted machine as a means to
find any vulnerability on that machine that can be used to compromise the system.
4. MACHINE LEARNING ALGORITHMS
There are many techniques in IDS are built on machine learning approaches. This paper will go
over several machine learning algorithms that have been used in the cybersecurity research area.
4. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
4
4.1. Decision Trees
Decision Trees algorithms are one of the used algorithms to solve classification where algorithms
sort data into classes, like whether an event is an attack or not. Decision Trees are made up of
nodes, branches, and leaves where every node presents as an attribute or feature and every branch
present as a rule or decision, and every leaf presents as an outcome. We can look at the decision
tree as a series of yes/no questions applied to our data resulting in a predicted class.
Figure 1. Decision Trees of two levels.
There are many algorithms derived from the decision tree algorithm. Some of them are ID3, C4.5,
J48, etc. The issue with ID3 algorithm is that the information might be overfitted.C4.5 is an
enhanced version of ID3 and it handles the overfitting issue. J48is an open-source of C4.5.
4.2. Random Forest
The random forest is a model structured from many decision trees. This model applies two key
concepts that yield it the name random:
Random sampling of training data will be considered when creating trees, where each
tree in a random forest learns from a random sample of the training data. The purpose
behind training each tree on different samples, even though each tree may have high
variance regarding a specific set of the training data, is that the entire forest will have
lower variance without increasing the bias. During testing, predictions are formed by
taking the predictions averages of each decision tree. These steps of training each learner
on various bootstrapped subsets of the training data and then taking the predictions
averages are called bagging, that is short for bootstrap aggregating.
Figure 2. Random forests and decision trees
5. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
5
Random subsets of features will be considered when dividing nodes in each decision tree.
For example, if there are 4 features, at each node in each tree, only 2 random features will
be taken into consideration for dividing the node.
4.3. Naive Bayes
Naive Bayes is a classifier where the machine learning model is built based on probability to be
used in classification using the Bayes theorem. For example, by applying Bayes theorem, we can
calculate the probability of an attack to happen when an event has occurred.
4.4. Support Vector Machines (SVM)
Support vector machines are a supervised learning algorithm that can be applied in classification
cases. It is usually applied to a small dataset because it takes a long time to process. SVM is built
based on the concept of determining a hyperplane that best divides the features into several
domains. For example, if we want to create a function (hyperplane) that will identify the two
cases, such that whenever a server received so many packets is it going to be classified as an
attack or normal? The following are the figures of two scenarios in which the hyperplane are
drawn.
Figure 3. SVM will come up with an optimal hyperplane that will classify the different classes
The points near the hyperplane are called: support vector points and the space between the vectors
and the hyperplane are called: margins.
Figure 4. Support vector points andmargins
6. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
6
4.5. Artificial Neural Network
Artificial neural networks are one of the widely used algorithms in machine learning. They are
brain-inspired systems that are designed to copy the way how humans learn. Neural networks
contain input and output layers and also, it can have hidden layers that convert the input into
something that the output layer can utilize. In Artificial Neural Network, if more data is used as a
training set then it will become more accurate. It is similar when someone keeps doing a task over
and over. Over time, that person becomes more efficient as well as makes a small number of
mistakes.
Figure 5. A simple neural network with a simple hidden layer
4.6. Gradient Boosted Trees
The Gradient boosted trees used an ensemble of several trees to make more powerful prediction
models for classification by building a series of trees where each tree is trained to try correcting
mistakes in the previous tree in the series.
Figure 6. Add new models to the ensemble sequentially
4.7. k-Nearest Neighbor (kNN)
The k-Nearest-Neighbors algorithm of classification is one of the straight forward algorithms in
machine learning. The classification relies on identifying similar data points in the training data
7. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
7
then making a prediction based on their classifications. kNN is one of the algorithms that give
better results on a small size data-sets that do not have several features.
5. EXPERIMENTS AND RESULTS
Using machine learning algorithms in Intrusion Detection System (IDS) is an attracting research
area for cyber security researchers around the world [5-14]. In this research, experiments were
made based on classifying NSL-KDD dataset to either normal or attack. NSL-KDD dataset was
downloaded from the webpage of the Canadian Institute for Cybersecurity (CIC) that is based at
the University of New Brunswick [15]. Subset files from the NSL-KDD dataset were used:
KDDTrain+.arff and KDDTest+.arff.
KDDTrain+.arff has a dataset that is used for training purposes and the data is labelled as
either normal or anomaly. This file is saved in ARFF format.
KDDTest+.arff has a dataset that is used for testing purposes and the data is labelled as
either normal or anomaly. This file is saved in ARFF format.
The KDDTest+ dataset contains known and new attacks. New attacks are the ones that do not
exist in KDDTrain+ dataset.
5.1. Data Pre-processing
Data pre-processing is a crucial step in the journey of making a machine learning model. We have
to do some preparation to make sure that we build our machine learning models without any
issues. If there is no data pre-processing then our machine learning model won't work properly.
In any dataset, we need to distinguish something very important which is the difference between
the independent variables and the dependent variables. In any machine learning algorithm or any
model, independent variables are used to predict a dependent variable e.g., normal or anomaly.
Also, we have to deal with cases where we have some missing data in the dataset. The most
common idea to handle missing data in a column is to take the mean of that column. In other
words, the mean of all the values in a column will replace the missing data in that column.
Furthermore, sometimes in the dataset, there will be a kind of variables that are called categorical
variables because simply they contain categories. Since machine learning models are based on
mathematical equations, therefore this would cause some problem if we keep the categorical
variables, e.g., text, in the equations because we would only want numbers in the equations. As a
result, we need to encode the categorical variables into numbers.
The next step features scaling, which is very important in machine learning when variables are
not on the same scale, this will cause some issues in some machine learning models.
5.2. Classification
Tables 1 and 2 show the results of applying different classification algorithms using KNIME to
analyze the NSL-KDD dataset. KNIME is an open-source data analytics. It has a shapely
graphical user interface that is easy to use and understand.
We tested several KNIME’s machine learning algorithms using two different approaches: In the
first approach, the KDDTrain+ has partitioned to 70% training set and 30% testing set so each
algorithm is trained on the 70% portion and tested on the 30% portion.
8. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
8
In the second approach, each algorithm is trained using the entire KDDTrain+ and tested using
the KDDTest+ dataset. The KDDTest+ dataset contains known and new attacks. New attacks are
the ones that do not exist in KDDTrain+ dataset.
The tested machine learning algorithms will generate alerts that can be categorized as follows.
True positive which means an attack is predicted as an attack.
False-positive which means a normal network packet is predicted as an attack.
True negative which means a normal network packet is predicted as normal.
False-negative which means an attack is predicted as normal.
5.3. Results
When KDDTrain+ is partitioned to 70% training set and 30% testing set and the 30% portion
dataset is used for testing the algorithms, it has been noticed that the majority of the tested
algorithms achieved a very high accuracy as shown in Table 1.
However, when KDDTest+ dataset is used, which contains both old and new attacks, a sudden
decrease in the accuracy has been noticed on all the tested algorithms. The best accuracy achieved
is 79% with Naive Bayes then 78% with Probabilistic Neural Network and Gradient Boost Tree
algorithms. The result obviously shows that some of the tested machine learning algorithms are
useful for detecting attacks on which they are trained but performs poorly on new attacks as
shown in Table 2.
Table 1. The accuracy statistics on KDDTrain+ Dataset that is partitioned to 70% training set and 30%
testing set.
Machine
Learning
Algorithm
Class True
Positives
False
Positives
True
Negatives
False
Negatives
Accuracy
Decision Tree Attack 17537 30 20191 34
0.998
Normal 20191 34 17537 30
Random
Forest
Attack 17532 20 20201 39
0.998
Normal 20201 39 17532 20
Naive Bayes Attack 18324 2635 14936 1897
0.880
Normal 14936 1897 18324 2635
Support
Vector
Machine
(SVM)
Attack 16527 655 19566 1044
0.955
Normal 19566 1044 16527 655
Probabilistic
Neural
Network
(PNN)
Attack 18467 807 16764 1754
0.932Normal 16764 1754 18467 807
Gradient
Boosted Trees
Attack 20183 57 17514 38
0.997
Normal 17514 38 20183 57
K Nearest
Neighbor
Attack 20145 78 17493 76
0.996
Normal 17493 76 20145 78
9. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
9
Table 2. The accuracy statistics on KDDTest+ Dataset.
Machine
Learning
Algorithm
Class True
Positives
False
Positives
True
Negatives
False
Negatives
Accuracy
Decision Tree Attack 7270 212 9499 5563
0.74
Normal 9499 5563 7270 212
Random Forest Attack 7106 645 9066 5727
0.72
Normal 9066 5727 7106 645
Naive Bayes Attack 8652 476 9235 4181
0.79
Normal 9235 4181 8652 476
Support Vector
Machine
(SVM)
Attack 7332 779 8932 5501
0.72Normal 8932 5501 7332 779
Probabilistic
Neural
Network (PNN)
Attack 8630 712 8999 4203
0.78Normal 8999 4203 8630 712
Gradient
Boosted Trees
Attack 8145 218 9493 4688
0.78
Normal 9493 4688 8145 218
K Nearest
Neighbor
Attack 7172 693 9018 5661
0.72
Normal 9018 5661 7172 693
The following figures show how to use KNIME workflow to build and run different KNIME’s
machine learning algorithms:
Figure 7. KNIME workflow using Decision Tree Learner where KDDTrain+ Dataset is partitioned to 70%
training set and 30% testing set
Figure 8. KNIME workflow using Random Forest Learner where KDDTrain+ Dataset is partitioned to 70%
training set and 30% testing set
10. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
10
Figure 9. KNIME workflow using Tree Ensemble Learner where KDDTrain+ Dataset is partitioned to 70%
training set and 30% testing set
Figure 10. KNIME workflow using Naive Bayes Learner where KDDTrain+ Dataset is partitioned to 70%
training set and 30% testing set
Figure 11. KNIME workflow using SVM Learner where KDDTrain+ Dataset is partitioned to 70% training
set and 30% testing set
Figure 12. KNIME workflow using PNN Learner where KDDTrain+ Dataset is partitioned to 70% training
set and 30% testing set
Figure 13. KNIME workflow using Gradient Boosted Trees Learner where KDDTrain+ Dataset is
partitioned to 70% training set and 30% testing set
11. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
11
Figure 14. KNIME workflow using K Nearest Neighbor where KDDTrain+ Dataset is partitioned to 70%
training set and 30% testing set
Figure 15. KNIME workflow using Decision Tree Learner on KDDTest+ Dataset
Figure 16. KNIME workflow using Random Forest Learner on KDDTest+ Dataset
Figure 17. KNIME workflow using Tree Ensemble Learner on KDDTest+ Dataset
12. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
12
Figure 18. KNIME workflow using Naive Bayes Learner on KDDTest+ Dataset
Figure 19. KNIME workflow using SVM Learner on KDDTest+ Dataset
Figure 20. KNIME workflow using PNN Learner on KDDTest+ Dataset
Figure 21. KNIME workflow using Gradient Boosted Trees Learner on KDDTest+ Dataset
13. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
13
Figure 22. KNIME workflow using K Nearest Neighbor on KDDTest+ Dataset
6. CONCLUSIONS
In this paper, different KNIME’s machine learning algorithms that can be used in intrusion
detection systems (IDSs) have been tested to analyze the NSL-KDD dataset. Also, an accuracy
comparison of these algorithms is given. Every algorithm has its features that have a significant
role in enhancing IDSs when compared to other algorithms. The results indicate that almost most
of the tested machine learning algorithms used in this paper show excellent performance on
detecting attacks using a dataset on which they are trained. However, the performance of the
algorithms goes down when the testing dataset includes new attacks.
REFERENCES
[1] Warzyński, A. and G. Kołaczek. Intrusion detection systems vulnerability on adversarial examples. in
2018 Innovations in Intelligent Systems and Applications (INISTA). 2018.
[2] Kehe, W., D. Tao, and H. Jianping. The research of intrusion detection technology based on heuristic
analysis. in 2011 6th IEEE Joint International Information Technology and Artificial Intelligence
Conference. 2011.
[3] Stolfo, S.J., et al. Cost-based modeling for fraud and intrusion detection: results from the JAM project.
in Proceedings DARPA Information Survivability Conference and Exposition. DISCEX'00. 2000.
[4] Tavallaee, M., et al. A detailed analysis of the KDD CUP 99 data set. in 2009 IEEE Symposium on
Computational Intelligence for Security and Defense Applications. 2009.
[5] Thomas, R. and D. Pavithran. A Survey of Intrusion Detection Models based on NSL-KDD Data Set.
in 2018 Fifth HCT Information Technology Trends (ITT). 2018.
[6] Meena, G. and R.R. Choudhary. A review paper on IDS classification using KDD 99 and NSL KDD
dataset in WEKA. in 2017 International Conference on Computer, Communications and Electronics
(Comptelix). 2017.
[7] Paulauskas, N. and J. Auskalnis. Analysis of data pre-processing influence on intrusion detection
using NSL-KDD dataset. in 2017 Open Conference of Electrical, Electronic and Information Sciences
(eStream). 2017.
[8] Ravipati Rama Devi, M.A., Intrusion Detection System Classification Using Different Machine
Learning Algorithms On Kdd-99and Nsl-Kdd Datasets -Areview Paper. International Journal of
Computer Science & Information Technology (IJCSIT) 2019. Vol 11, No 3.
[9] Rama Devi Ravipati, M.A., A Survey on Different Machine Learning Algorithms and Weak
Classifiers Based on KDD And NSL-KDD Datasets. International Journal of Artificial Intelligence
and Applications (IJAIA), 2019. Vol.10, No.3.
14. International Journal of Network Security & Its Applications (IJNSA) Vol. 11, No.5, September 2019
14
[10] Arafat, M., A. Jain, and Y. Wu. Analysis of Intrusion Detection Dataset NSL-KDD Using KNIME
Analytics. in International Conference on Cyber Warfare and Security. 2018. Academic Conferences
International Limited.
[11] Dhanabal, L. and S. Shantharajah, A study on NSL-KDD dataset for intrusion detection system based
on classification algorithms. International Journal of Advanced Research in Computer and
Communication Engineering, 2015. 4(6): p. 446-452.
[12] Revathi, S. and A. Malathi, A detailed analysis on NSL-KDD dataset using various machine learning
techniques for intrusion detection. International Journal of Engineering Research & Technology
(IJERT), 2013. 2(12): p. 1848-1853.
[13] Aggarwal, P. and S.K. Sharma, Analysis of KDD dataset attributes-class wise for intrusion detection.
Procedia Computer Science, 2015. 57: p. 842-851.
[14] Deshmukh, D.H., T. Ghorpade, and P. Padiya. Intrusion detection system by improved preprocessing
methods and Naïve Bayes classifier using NSL-KDD 99 Dataset. in 2014 International Conference on
Electronics and Communication Systems (ICECS). 2014. IEEE.
[15] NSL-KDD Dataset. Available from: https://www.unb.ca/cic/datasets/nsl.html.