This document provides an outline for a lecture on adversarial machine learning in network intrusion detection. It discusses network intrusion detection systems (NIDS) and how machine learning can be used for anomaly detection in NIDS. It describes commonly used NIDS datasets like NSL-KDD and CSE-CIC-IDS2018. It then covers machine learning models for anomaly detection, including one-class SVMs, autoencoders, variational autoencoders, and sequence-to-sequence models. Finally, it discusses adversarial attacks against machine learning models for NIDS.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET PARALLEL DISTRIBUTED PROJECTS A system-for-denial-of-service...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Cloud-based IDS architectures : APPLYING THE IDS APPROACHES INTO THE CLOUD EN...Hassan EL ALLOUSSI
This document discusses intrusion detection systems (IDS) in cloud computing environments. It begins by defining cloud computing and describing its essential characteristics and deployment and service models. It then addresses security concerns in cloud computing, including threats from both insiders and outsiders. Next, it provides an overview of traditional IDS approaches, including host-based, network-based, and virtual machine-based systems. The document proposes several architectures for implementing IDS in clouds, including distributing sensors across cloud nodes, using a third-party monitoring service, integrating detection engines into cloud services, and taking a virtual machine-based approach. It concludes that the best approach combines behavioral and signature-based detection methods and can be implemented either by cloud providers or tenants
Current issues - International Journal of Network Security & Its Applications...IJNSA Journal
nternational 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.
A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBE...IJNSA Journal
In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.
This document describes a system for alert aggregation of cyber attacks and intrusions using generative data stream modeling. The system aims to reduce false positive and negative alerts by correlating alerts that exhibit similar patterns into single meta-alerts. It also aims to distinguish between different instances of the same attack type. The system architecture includes layers for detection, alert processing, and reaction. It evaluates intrusions using the KDD dataset and detects attacks falling into categories like probing, DoS, U2R, and R2L.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
COMBINING NAIVE BAYES AND DECISION TREE FOR ADAPTIVE INTRUSION DETECTIONIJNSA Journal
In this paper, a new learning algorithm for adaptive network intrusion detection using naive Bayesian classifier and decision tree is presented, which performs balance detections and keeps false positives at acceptable level for different types of network attacks, and eliminates redundant attributes as well as contradictory examples from training data that make the detection model complex. The proposed algorithm also addresses some difficulties of data mining such as handling continuous attribute, dealing with missing attribute values, and reducing noise in training data. Due to the large volumes of security audit data as well as the complex and dynamic properties of intrusion behaviours, several data miningbased intrusion detection techniques have been applied to network-based traffic data and host-based data in the last decades. However, there remain various issues needed to be examined towards current intrusion detection systems (IDS). We tested the performance of our proposed algorithm with existing learning algorithms by employing on the KDD99 benchmark intrusion detection dataset. The experimental results prove that the proposed algorithm achieved high detection rates (DR) and significant reduce false positives (FP) for different types of network intrusions using limited computational resources.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET PARALLEL DISTRIBUTED PROJECTS A system-for-denial-of-service...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Cloud-based IDS architectures : APPLYING THE IDS APPROACHES INTO THE CLOUD EN...Hassan EL ALLOUSSI
This document discusses intrusion detection systems (IDS) in cloud computing environments. It begins by defining cloud computing and describing its essential characteristics and deployment and service models. It then addresses security concerns in cloud computing, including threats from both insiders and outsiders. Next, it provides an overview of traditional IDS approaches, including host-based, network-based, and virtual machine-based systems. The document proposes several architectures for implementing IDS in clouds, including distributing sensors across cloud nodes, using a third-party monitoring service, integrating detection engines into cloud services, and taking a virtual machine-based approach. It concludes that the best approach combines behavioral and signature-based detection methods and can be implemented either by cloud providers or tenants
Current issues - International Journal of Network Security & Its Applications...IJNSA Journal
nternational 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.
A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBE...IJNSA Journal
In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.
This document describes a system for alert aggregation of cyber attacks and intrusions using generative data stream modeling. The system aims to reduce false positive and negative alerts by correlating alerts that exhibit similar patterns into single meta-alerts. It also aims to distinguish between different instances of the same attack type. The system architecture includes layers for detection, alert processing, and reaction. It evaluates intrusions using the KDD dataset and detects attacks falling into categories like probing, DoS, U2R, and R2L.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
High Performance NMF based Intrusion Detection System for Big Data IoT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
The document proposes an architecture called IntelFlow that aims to integrate cyber threat intelligence into software defined networks. IntelFlow would introduce a knowledge plane that receives threat intelligence from various sources and allows the Bro IDS to query this intelligence. The knowledge plane would then export OpenFlow rules to implement countermeasures. The document outlines IntelFlow's components, how it would map intelligence indicators to OpenFlow flows, and presents initial results from a proof-of-concept showing IntelFlow can detect attacks faster than reactive approaches and successfully mitigated a DDoS attack in testing. Future work will further evaluate IntelFlow's effectiveness against other attacks.
A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and...IJMER
—This paper proposes a technique uses decision tree for dataset and to find the basic
parameters for creating the membership functions of fuzzy inference system for Intrusion Detection and
Forensics. Approach of generating rules using clustering methods is limited to the problems of
clustering techniques. To trait to solve this problem, several solutions have been proposed using
various Techniques. One such Technique is proposed to be applied here, for an analysis to Intrusion
Detection and Forensics. . Fuzzy Inference approach and decision algorithms are investigated in this
work. Decision tree is used to identify the parameters to create the fuzzy inference system. Fuzzy
inference system used as an input and the final ANFIS structure is generated for intrusion detection
and forensics. The experiments and evaluations of the proposed method were done with NSL-KDD
intrusion detection dataset.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
JPD1424 A System for Denial-of-Service Attack Detection Based on Multivariat...chennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/dot-net-projects/
Abstract—With the heightening reliance on Information Technology in recent times, it has becoming more relevant to find measures to secure every online device, data and information. A Network Intrusion Detection System (NIDS) is one of the security options to consider to help protect such devices, data and information. However, IDS needs to be up to date to mitigate current threats to secure systems. A critical issue in the development of the right IDS is the scarcity of current data sets used for training these IDS and the impact on system performance. This paper presents an On-demand Network Data Set Creation Application (ONDaSCA) a Graphical User Interface software capable of generating labelled network intrusion data set. ONDaSCA grants IDS users or researchers the option to choose a raw data set and processed this data set as output, real-time packet capture and offline upload of existing PCAP file and two (2) difference packet capturing methods (Tshark and Dumpcap). ONDaSCA is highly customisable and an IDS user or researcher can leverage its capabilities to suit their needs. The abilities of this software are compared with other similar products that generate data set for use by IDS model.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
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.
This document describes a project to develop an intrusion detection system using data mining techniques. It discusses approaches to intrusion detection including signature-based and anomaly-based methods. For the project, a hybrid network-based and host-based intrusion detection system is proposed. Data preprocessing and mining techniques including clustering, outlier detection, and classification are applied to network packet data and system call logs to detect attacks.
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
DDoS Attack Detection on Internet o Things using Unsupervised Algorithmsijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.
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.
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.
This document discusses intrusion detection systems (IDS), beginning with historical examples of cyber attacks. It describes the role of firewalls in network security and how IDS serve as a complementary technique to firewalls by monitoring network traffic and detecting intrusions. The document outlines different types of IDS, including host-based, network-based, and hybrid systems. It also covers common intrusion detection techniques and the limitations of IDS in providing comprehensive security.
Security is a major concern in computer networking which faces increasing threats as the commercial
Internet and related economies continue to grow. Virtualization technologies enabling
scalable Cloud services pose further challenges to the security of computer infrastructures,
demanding novel mechanisms combining the best-of-breed to counter certain types of attacks
. Our work aims to explore advances in Cyber Threat Intelligence (CTI) in the context of
Software Defined Networking (SDN) architectures. While CTI represents a recent approach
to combat threats based on reliable sources, by sharing information and knowledge about
computer criminal activities, SDN is a recent trend in architecting computer networks based
on modularization and programmability principles. In this dissertation, we propose IntelFlow,
an intelligent detection system for SDN that follows a proactive approach using OpenFlow
to deploy countermeasures to the threats learned through a distributed intelligent plane. We
show through a proof of concept implementation that the proposed system is capable of delivering
a number of benefits in terms of effectiveness, altogether contributing to the security
of modern computer network designs.
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.
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.
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.
"Multipliers accidental demolisher." If you have a specific industry, topic, ...TsegabrehanZerihun
Multipliers accidental demolisher" seems to be a combination of terms that might not form a recognized concept in a specific field. However, I can offer some interpretations based on the individual terms:
Multipliers:
As mentioned earlier, in various contexts, "multipliers" can refer to economic ratios, leadership philosophies, digital components, marketing strategies, or mathematical factors.
Accidental Demolisher:
"Accidental demolisher" could be interpreted as someone or something that unintentionally causes destruction or demolishes. It could refer to an entity or activity that, while not intentionally harmful, leads to unintended negative consequences.
Without a specific context or additional details, it's challenging to provide a more precise interpretation of "Multipliers accidental demolisher." If you have a specific industry, topic, or context in mind, providing more information would allow for a more accurate response.
To access and use the internet, you typically need a few basic components and follow some straightforward steps. Here's a general guide:
Components Needed:
Device: You'll need a device capable of connecting to the internet, such as a computer, laptop, smartphone, tablet, or a smart device.
Internet Connection:
Wi-Fi: If using a wireless device, make sure you're in range of a Wi-Fi network. Connect by selecting the appropriate network and entering the password if required.
Ethernet: For wired connections, connect your device to an available Ethernet port using an Ethernet cable.
Internet Service Provider (ISP):
Subscribe to an internet service through an ISP. This may involve setting up an account, choosing a plan, and installing necessary equipment like a modem or router.
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High Performance NMF Based Intrusion Detection System for Big Data IOT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
High Performance NMF based Intrusion Detection System for Big Data IoT TrafficIJCNCJournal
With the emergence of smart devices and the Internet of Things (IoT), millions of users connected to the network produce massive network traffic datasets. These vast datasets of network traffic, Big Data are challenging to store, deal with and analyse using a single computer. In this paper we developed parallel implementation using a High Performance Computer (HPC) for the Non-Negative Matrix Factorization technique as an engine for an Intrusion Detection System (HPC-NMF-IDS). The large IoT traffic datasets of order of millions samples are distributed evenly on all the computing cores for both storage and speedup purpose. The distribution of computing tasks involved in the Matrix Factorization takes into account the reduction of the communication cost between the computing cores. The experiments we conducted on the proposed HPC-IDS-NMF give better results than the traditional ML-based intrusion detection systems. We could train the HPC model with datasets of one million samples in only 31 seconds instead of the 40 minutes using one processor), that is a speed up of 87 times. Moreover, we have got an excellent detection accuracy rate of 98% for KDD dataset.
The document proposes an architecture called IntelFlow that aims to integrate cyber threat intelligence into software defined networks. IntelFlow would introduce a knowledge plane that receives threat intelligence from various sources and allows the Bro IDS to query this intelligence. The knowledge plane would then export OpenFlow rules to implement countermeasures. The document outlines IntelFlow's components, how it would map intelligence indicators to OpenFlow flows, and presents initial results from a proof-of-concept showing IntelFlow can detect attacks faster than reactive approaches and successfully mitigated a DDoS attack in testing. Future work will further evaluate IntelFlow's effectiveness against other attacks.
A Technique by using Neuro-Fuzzy Inference System for Intrusion Detection and...IJMER
—This paper proposes a technique uses decision tree for dataset and to find the basic
parameters for creating the membership functions of fuzzy inference system for Intrusion Detection and
Forensics. Approach of generating rules using clustering methods is limited to the problems of
clustering techniques. To trait to solve this problem, several solutions have been proposed using
various Techniques. One such Technique is proposed to be applied here, for an analysis to Intrusion
Detection and Forensics. . Fuzzy Inference approach and decision algorithms are investigated in this
work. Decision tree is used to identify the parameters to create the fuzzy inference system. Fuzzy
inference system used as an input and the final ANFIS structure is generated for intrusion detection
and forensics. The experiments and evaluations of the proposed method were done with NSL-KDD
intrusion detection dataset.
Survey on classification techniques for intrusion detectioncsandit
Intrusion detection is the most essential component
in network security. Traditional Intrusion
Detection methods are based on extensive knowledge
of signatures of known attacks. Signature-
based methods require manual encoding of attacks by
human experts. Data mining is one of the
techniques applied to Intrusion Detection that prov
ides higher automation capabilities than
signature-based methods. Data mining techniques suc
h as classification, clustering and
association rules are used in intrusion detection.
In this paper, we present an overview of
intrusion detection, KDD Cup 1999 dataset and detai
led analysis of different classification
techniques namely Support vector Machine, Decision
tree, Naïve Bayes and Neural Networks
used in intrusion detection.
JPD1424 A System for Denial-of-Service Attack Detection Based on Multivariat...chennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/dot-net-projects/
Abstract—With the heightening reliance on Information Technology in recent times, it has becoming more relevant to find measures to secure every online device, data and information. A Network Intrusion Detection System (NIDS) is one of the security options to consider to help protect such devices, data and information. However, IDS needs to be up to date to mitigate current threats to secure systems. A critical issue in the development of the right IDS is the scarcity of current data sets used for training these IDS and the impact on system performance. This paper presents an On-demand Network Data Set Creation Application (ONDaSCA) a Graphical User Interface software capable of generating labelled network intrusion data set. ONDaSCA grants IDS users or researchers the option to choose a raw data set and processed this data set as output, real-time packet capture and offline upload of existing PCAP file and two (2) difference packet capturing methods (Tshark and Dumpcap). ONDaSCA is highly customisable and an IDS user or researcher can leverage its capabilities to suit their needs. The abilities of this software are compared with other similar products that generate data set for use by IDS model.
International Journal of Computer Science and Information Security,IJCSIS ISSN 1947-5500, Pittsburgh, PA, USA
Email: ijcsiseditor@gmail.com
http://sites.google.com/site/ijcsis/
https://google.academia.edu/JournalofComputerScience
https://www.linkedin.com/in/ijcsis-research-publications-8b916516/
http://www.researcherid.com/rid/E-1319-2016
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.
This document describes a project to develop an intrusion detection system using data mining techniques. It discusses approaches to intrusion detection including signature-based and anomaly-based methods. For the project, a hybrid network-based and host-based intrusion detection system is proposed. Data preprocessing and mining techniques including clustering, outlier detection, and classification are applied to network packet data and system call logs to detect attacks.
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
DDoS Attack Detection on Internet o Things using Unsupervised Algorithmsijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.
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.
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.
This document discusses intrusion detection systems (IDS), beginning with historical examples of cyber attacks. It describes the role of firewalls in network security and how IDS serve as a complementary technique to firewalls by monitoring network traffic and detecting intrusions. The document outlines different types of IDS, including host-based, network-based, and hybrid systems. It also covers common intrusion detection techniques and the limitations of IDS in providing comprehensive security.
Security is a major concern in computer networking which faces increasing threats as the commercial
Internet and related economies continue to grow. Virtualization technologies enabling
scalable Cloud services pose further challenges to the security of computer infrastructures,
demanding novel mechanisms combining the best-of-breed to counter certain types of attacks
. Our work aims to explore advances in Cyber Threat Intelligence (CTI) in the context of
Software Defined Networking (SDN) architectures. While CTI represents a recent approach
to combat threats based on reliable sources, by sharing information and knowledge about
computer criminal activities, SDN is a recent trend in architecting computer networks based
on modularization and programmability principles. In this dissertation, we propose IntelFlow,
an intelligent detection system for SDN that follows a proactive approach using OpenFlow
to deploy countermeasures to the threats learned through a distributed intelligent plane. We
show through a proof of concept implementation that the proposed system is capable of delivering
a number of benefits in terms of effectiveness, altogether contributing to the security
of modern computer network designs.
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.
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.
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.
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Network Intrusion Detection
• Network security is critical to every organization, as all computer systems suffer
from security vulnerabilities
Network security requires solutions in place for protection from the increasing
number of cyber threats
It is essential for every organization to implement some form of intrusion detection
systems (IDS) that can discover potential threat events early and in a reliable manner
• An intrusion is a deliberate unauthorized attempt, successful or not, to break
into, access, manipulate, or misuse some valuable property, which may result
into or render the property unreliable or unusable
• An intrusion detection system (IDS) is a security tool for detecting unauthorized
intrusions into computer systems and networks
A security system used to secure networks from unauthorized intrusions is a network
intrusion detection system (NIDS)
NIDS should prevents possible intrusions by continuously monitoring the network
traffic, to detect any suspicious behavior that violates the security policies and
compromises the network confidentiality, integrity, and availability
Network Intrusion Detection
Ahmad (2020) – Network Intrusion Detection System: A Systematic Study of ML and DL Approaches
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Network Intrusion Detection
• NIDS is implemented in the form of a device or software that monitors all traffic
passing through a strategic point in the network for malicious activities
Network Intrusion Detection
It is typically deployed at a single
point, for example, it can be connected
to the network switch (as in the figure)
o If malicious behavior is detected, NIDS
will generate alerts to the host or
network administrators
Figure from: Ahmad (2020) – Network Intrusion Detection System: A Systematic Study of ML and DL Approaches
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Goals of NIDS
• The main goals of NIDS include:
1. Detect wide variety of intrusions
o Previously known and unknown attacks
o Suggests if there is a need to learn/adapt to new attacks or change in behavior
2. Detect intrusions in timely fashion
o And minimize the time spent verifying attacks
o Depending on the system criticality, it may be required to operate in real-time, especially
when the system responds to (and not only monitors) intrusions
– Problem: analyzing commands may impact the response time of the system
3. Present the analysis in a simple, easy-to-understand format
o Ideally as a binary indicator (normal vs malicious activities)
o Usually the analysis is more complex (than a binary output), and security analysts are
required to examine suspected attacks
o The user interface is critical, especially when monitoring large systems
4. Is accurate
o Minimize false positives, false negatives
Goals of Network Intrusion Detection Systems
Slide credit: Intrusion Detection - Chapter 22 in “Introduction to Computer Security”
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IDS Categories
• The figure depicts an IDS taxonomy based on the deployment or detection
methods
Deployment methods
o Host-based IDS – deployed to monitor the activities of a single host and scan for its security
policy violations and suspicious activities
– Requires information processing for each single node in a network
o Network-based IDS – deployed to monitor the activities of all devices connected to a network
IDS Categories
Figure from: Ahmad (2020) – Network Intrusion Detection System: A Systematic Study of ML and DL Approaches
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IDS Categories
• Based on the used detection methods, IDS can be broadly divided into:
Signature-based systems
o These systems are also known as misuse intrusion detection
o The system compares the incoming traffic with a pre-existing database containing signatures
of known attacks
o Signature databases need to be continuously updated with the most recent attacks
o Detecting new attacks, for which a signature does not exist, is difficult
Anomaly-based systems
o The system uses statistics to form a baseline (normal) usage of the network at different time
intervals
o Deviations from the baseline usage are considered anomalies
o The advantage is that these systems can detect unknown attacks
o The main challenge is the high false alarms rate (as it is difficult to find the exact boundary
between normal and abnormal behavior)
IDS Categories
Cuelogic Technologies Blog - Evaluation of Machine Learning Algorithms for Intrusion Detection System
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NIDS with Machine Learning
• Enormous increase in network traffic in recent years and the resulting security
threats are posing many challenges for detecting malicious network intrusions
• To address these challenges, ML and DL-based NIDS have been implemented for
detecting network intrusions
Anomaly detection has been the main focus of these methods, due to the potential for
detecting new types of attacks
• In the remainder of the lecture, we will first overview the datasets that are
commonly used for training and evaluating ML-based NIDS, followed by a
description of the ML models used for anomaly detection, and followed by
adversarial attacks on ML models for NIDS
Network Intrusion Detection with Machine Learning
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Datasets for Network Intrusion Detection
• There are several public datasets consisting of records of normal network traffic
and network attacks
Each record in these datasets represents a network connection data packet
The data packets are collected between defined starting and ending times, as data
flows to and from a source machine and a target machine under a distinct network
communication protocol
• Network connection data packets are saved as PCAP (Packet Capture) files (i.e.,
.pcapfile)
PCAP files have different formats, e.g., Libpcap (Linux and macOS), WinPcap
(Windows), and Npcap (Windows)
PCAP files are used for network analysis, monitoring network traffic, and managing
security risks
o The data packets allow to identify network problems
– E.g., based on data usage of applications and devices
– Or, identify where a piece of malware breached the network, by tracking the flow of malicious traffic
and other malicious communications
Datasets for Network Intrusion Detection
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NSL-KDD Dataset
• The most popular dataset for benchmarking ML models for NIDS has been the
NSL-KDD dataset
Datasets for Network Intrusion Detection
It is an updated, cleaned-up version of the
original KDD Cup’99 dataset (released in 1999
and containing many redundant files)
• NSL-KDD contains 150 thousand network data
packet records (PCAP files)
• Each record has 41 features, shown in the table
The features include duration of the connection,
protocol type, data bytes send from source to
destination, number of failed logins, etc.
The 41 features are either categorical (4), binary
(6), discrete (23), or continuous (10)
o Many works use a selected subset of the 41 features
In addition, every record has a label (whether it
is a normal traffic or attack) and a score (the
severity of the traffic, on a scale from 0 to 21)
Table from: Gerry Saporito – A Deeper Dive into the NSL-KDD Data Set
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NSL-KDD Dataset
• The attacks in the NSL-KDD dataset are categorized into 4 classes
DoS - Denial of Service, by flooding the server with abnormal amount of traffic
Probing - Surveillance and other probing attacks to get information from a network
U2R - Unauthorized access of a normal user as a super-user (gain access to the root)
R2L - Unauthorized access from a remote machine to gain local access
• The subclasses for each attack are shown below, resulting in 39 attacks
Datasets for Network Intrusion Detection
Table from: Gerry Saporito – A Deeper Dive into the NSL-KDD Data Set
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NSL-KDD Dataset
• The records are divided into Train (125 K instances) and Test subsets (25 K
instances)
As well as a smaller subset Train+20%, containing 20% of the train records (25 K)
• The number of records per attack class is shown in the table
Majority of the records in the Train set are normal traffic (53%)
The most common attack in the Train set is DoS (37%), while U2R and R2L occur rarely
The Test set contains attack subclasses not seen in the Train set
Datasets for Network Intrusion Detection
Table from: Gerry Saporito – A Deeper Dive into the NSL-KDD Data Set
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CSE-CIC-IDS2018 Dataset
• CSE-CIC-IDS2018 dataset was collected with an attacking infrastructure
consisting of 50 machines, and a victim infrastructure of 420 machines and 30
servers
The testbed includes both Windows and Linux machines
It is a collaborative project between the Communications Security Establishment (CSE)
and the Canadian Institute for Cybersecurity (CIC)
Link to the dataset
It is a more recent dataset, in comparison to the most popular KDD Cup’99 dataset
• The dataset includes the network traffic records (PCAP files) and system logs of
each machine, captured with the CICFlowMeter-V3 device
The records have 80 network traffic features, which include duration, number of
packets, number of bytes, length of packets, etc.
• There are 7 types of attack (details about the attacks are presented on the next
two pages)
Datasets for Network Intrusion Detection
Table from: https://www.unb.ca/cic/datasets/ids-2018.html
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CSE-CIC-IDS2018 Dataset
• Brute-force attack – submit many passwords to guess login information
• Heartbleed attack – scan for vulnerable applications (e.g., OpenSSL), and exploit
them to retrieve the memory of the web server (can include passwords, credit
card numbers, private email or social media messages)
• Botnet attack - Zeus and Ares malware used for requesting screenshots from
infected devices every 7 minutes, and stealing information by keystroke logging
• DoS attack - Slowloris Denial of Service attack allows a single device to take
down the web server of another device, by overwhelming it with network traffic
• DDoS attack - low Orbit in Cannon (LOIC) Distributed Denial of Service attack
used 4 devices to take down the web server of a target device
• Web attacks – scan a website for vulnerable applications, and conduct SQL
injection, command injection, and unrestricted file upload
• Infiltration of the network from inside attack – a vulnerable application (e.g.,
PDF Reader) is sent via a malicious email attachment, and if exploited, it is
followed by IP sweep, full port scan, and service enumerations
Datasets for Network Intrusion Detection
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CSE-CIC-IDS2018 Dataset
• Attacks in the CSE-CIC-IDS2018 dataset
Datasets for Network Intrusion Detection
Table from: https://www.unb.ca/cic/datasets/ids-2018.html
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Anomaly Detection with Machine Learning
• An anomaly is a data point or pattern in data that does not conform to a notion
of normal behavior
Anomalies are also often referred to as outliers, abnormalities, or deviations
• Anomaly detection is finding such patterns in data that do not adhere to
expected normal behavior, given previous observations
Anomaly detection has applications in many other domains besides network intrusion
detection, including medical diagnostics, financial fraud protection, manufacturing
quality control, marketing and social media analytics, etc.
• Approach: first model normal behavior, and then exploit it to identify anomalies
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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Anomaly Detection with Machine Learning
• Anomaly detection can be addressed as:
Supervised learning task – train a classification model using labeled normal and
abnormal samples
o E.g., signatures of normal and abnormal samples can be used as features for training a
classifier, and at inference, the classifier can be used to flag abnormal samples
o This approach assumes access to labeled examples of all types of anomalies that could occur
Unsupervised learning task – train a model using only unlabeled normal samples, to
learn the structure of the normal data
o At inference, any sample that is significantly different than the normal behavior is flagged as
an anomaly
Semi-supervised learning task – train a model using many unlabeled samples and a
few labeled samples
o E.g., train a model in unsupervised way using many samples (presumably most of which are
normal), and afterward fine-tune the model by using a small number of labeled normal and
abnormal samples
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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Anomaly Detection with Machine Learning
• Various conventional Machine Learning approaches have been employed for
anomaly detection
Clustering approaches: k-means clustering, SOM (self-organizing maps), EM
(expectation maximization)
Nearest neighbor approaches: k-nearest neighbors
Classification approaches (One-class SVM)
Statistical approaches (HMM, regression models)
• State-of-the-art results in anomaly detection have been typically reported by
Deep Learning approaches
Due to the capacity to model complex dependencies in multivariate and high-
dimensional data
These approaches commonly fall in the following categories:
o Autoencoders
o Variational autoencoders
o GANs
o Sequence-to-sequence models
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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One-Class SVM for Anomaly Detection
• One-class SVM (OCSVM) for anomaly detection is a variant of SVM designed
for learning a decision boundary around normal data instances
• Approach:
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
1. Train the OCSVM model on normal data (to
model normal behavior)
2. At inference, for an input instance calculate
the distance to the decision boundary (i.e.,
the separating hyperplane)
3. If the distance is positive then label the
instance as normal data, and if it is negative
then label it as abnormal data (anomaly)
• Alternatively, a discrete class value (+1 for
data that is similar and -1 for that that is not
similar) can be used as an anomaly score
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Autoencoders for Anomaly Detection
• Autoencoders (AE)
An encoder network maps inputs into a lower-dimensional representation (code, or
bottleneck), and a decoder network reconstructs the original input data
• Approach:
1. Train the autoencoder on normal data (to model normal behavior)
2. At inference, calculate the reconstruction error: e.g., RMSE deviation between the
input instance and the corresponding reconstructed output
3. If the reconstruction error is less than a threshold then label the instance as normal
data, if it is greater than the threshold then label it as abnormal data (anomaly)
o The manually-selected threshold value allows the user to tune the “sensitivity” to anomalies
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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Autoencoders for Anomaly Detection
• Use of autoencoder model for anomaly detection: airspeed during a takeoff
Anomaly Detection with Machine Learning
Figure from: Memarzadeh (2020) Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder
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Variational Autoencoders for Anomaly Detection
• Variational autoencoders (VAE) learn a mapping from input data to a
distribution
I.e., the encoder network learns the parameters (mean and variance) of a distribution
The decoder network learns to reconstruct the original data by sampling from the
distribution using a latent code
Typically, a Gaussian distribution is used to model the reconstruction space
• VAE are trained by minimizing the KL-divergence between the estimated
distribution by the model and the distribution of the real data
VAE are also generative models, since they can generate new instances (by sampling
from the latent code and reconstructing the sampled data)
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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Variational Autoencoders for Anomaly Detection
• Approach 1 (similar to the AE approach):
1. Train the VAE model on normal data instances (to model normal behavior)
2. At inference, calculate the reconstruction error: e.g., RMSE deviation between the
input instance and the reconstructed output of the corresponding sample code
3. If the reconstruction error is less than a threshold then label the instance as normal
data, if it is greater than the threshold then label it as abnormal data (anomaly)
Anomaly Detection with Machine Learning
• Approach 2:
1. Train the VAE model on normal data
instances (to model normal behavior)
2. At inference, calculate the mean and
variance from the decoder, and
calculate the probability that a new
instance belongs to the distribution
3. If the data instance lies in a low-
density region (i.e., below some
threshold), it is labeled as abnormal
data (anomaly)
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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GANs for Anomaly Detection
• The architecture called BiGAN (Bidirectional GAN) is commonly used for
anomaly detection
E.g., Akcay et al. (2018) GANomaly: Semi-Supervised Anomaly Detection via
Adversarial Training (link)
• In BiGAN:
A Generator takes as inputs random noise vectors 𝑍, and generate synthetic samples 𝑋
An additional Encoder is added that learns the reverse mapping – how to generate a
fixed noise vector 𝑍 given a real sample 𝑋
Anomaly Detection with Machine Learning
The Discriminator takes as inputs both
real samples 𝑋 and synthetic samples
𝑋, as well as latent noise vectors 𝑍
(from the Generator) and 𝑍 (from the
Encoder)
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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GANs for Anomaly Detection
• Approach:
1. Train the BiGAN model on normal data instances (to model normal behavior)
2. At inference, for a real data instance 𝑋, from the Encoder obtain a latent vector 𝑍
3. The noise vector is 𝑍 is fed to the Generator to yield a synthetic sample 𝑋
4. Calculate the reconstruction error: e.g., RMSE deviation between the real data
instance 𝑋 and the corresponding synthetic sample 𝑋
5. Calculate the loss of the Discriminator, i.e., cross-entropy of predictions for 𝑋 and 𝑋
6. Calculate an anomaly score as a weighted sum of the reconstruction error and the
loss of the Discriminator
7. If the anomaly score is less than a threshold then label the instance as normal data, if
it is greater than the threshold then label it as abnormal data (anomaly)
Anomaly Detection with Machine Learning
Blog: Cloudera Fast Forward – Deep Learning for Anomaly Detection
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Sequence-to-sequence Models for Anomaly Detection
• Sequence-to-sequence models are designed to learn mappings between
sequential data (e.g., time-series signals)
• Sequence-to-sequence models typically consist of an Encoder that generates a
hidden representation of the input tokens, and a Decoder that takes in the
encoder representation and sequentially generates a set of output tokens
The encoder and decoder are composed of recurrent layers, such as RNN, LSTM, or
GRU
Recurrent networks are particularly suitable for modeling temporal relationships
within input data tokens
• The anomaly detection approach is similar to the Autoencoder models
Anomaly Detection with Machine Learning
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Anomaly Detection with Machine Learning
• The table lists the pros and cons of the described ML approaches for anomaly
detection
Anomaly Detection with Machine Learning
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Benchmarking Models for Anomaly Detection
• Performance by the presented models evaluated using the NSL-KDD dataset
The best performance was achieved by BiGAN and Autoencoder
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Considerations for Anomaly Detection
• Imbalanced datasets
Normal data samples are more readily available than abnormal samples
Consequently, the model may perform poorly on abnormal samples
Remedy: collect more data, or consider using precision, recall, F1 metrics
• Definition of anomaly
The boundary between normal and anomalous behavior can evolve over time
It may require retraining the models to adopt to the changes in the data distribution
• False alarms
Many of the found anomalies could correspond to noise in the data
False alarms require human review of the cases, which increases the costs
• Computational complexity
Anomaly detection can require low latency (DL models are computationally intensive)
This may impose a trade-off between performance and accuracy
Anomaly Detection with Machine Learning
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Adversarial Attacks on NIDS
• White-box attacks on ML-based NIDS
Note that almost all adversarial attacks against NIDS presented here are evasion
attacks (i.e., integrity attacks)
• Warzinsky et al. (2018) Intrusion Detection Systems Vulnerability on
Adversarial Examples (link)
White-box integrity attack
Against a three-layer MLP classifier using the NSL-KDD dataset
FGSM (Fast Gradient Sign Method) was used to create perturbed data packets by
modifying input features
o The adversarial samples of network data packets were misclassified as normal samples by the
MLP model
The disadvantage of this approach is that modifying the individual features in the
data packets does not necessarily means that the resulting data packet will preserve its
functionality
o I.e., although the generated adversarial samples were misclassified by the model trained by
the authors, that does not mean that these samples will bypass real-life NIDS classifiers
White-box Adversarial Attacks on ML-based NIDS
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Adversarial Attacks on NIDS
• Wang et al. (2018) Deep Learning-Based Intrusion Detection with Adversaries
(link)
White-box attack, similar to Warzinsky et al. (2018): a two-layer MLP classifier is
trained on the NSL-KDD dataset
This work implemented four attacks: FGSM, JSMA (Jacobian-based Saliency Map
Approach), DeepFool, and C&W (Carlini & Wagner)
The output of the attacks are modified feature vectors
• Clements et al. (2019) Rallying Adversarial Techniques against Deep Learning
for Network Security (link)
White-box evasion attack against Kitsune – a NIDS comprising an ensemble of
autoencoders (an anomaly score is calculated based on a weighted RMSE deviation of
the ensemble of autoencoders)
The authors implemented 4 attacks: FGSM, JSMA, C&W, and ENM (Elastic Net
Method)
This approach has the same limitations, since only the feature vectors were modified
White-box Adversarial Attacks on ML-based NIDS
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Adversarial Attacks on NIDS
• Huang et al. (2019) Adversarial Attacks on SDN-Based Deep Learning IDS
System (link)
White-box evasion attack on port scanning NIDS classifiers in a software-defined
network (SDN)
o SDNs use software-based controllers to control network traffic (instead of using dedicated
hardware-based devices, such as routers or switches)
Attacked are three NIDS deep learning models, employing LSTM, CNN, and MLP
architectures
FGSM and JSMA attacks were performed on regular traffic packets to generate
adversarial data packets
Besides the evasion attack, this work also demonstrated an availability attack
o JSMA was applied on regular traffic data packets, which were classified by the port scanning
NIDS as attacks, resulting in blocked legitimate traffic
White-box Adversarial Attacks on ML-based NIDS
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Adversarial Attacks on NIDS
• Gray-box attacks on ML-based NIDS
Gray-box attacks assume knowledge about the features used by the classifier
• Lin et al. (2018) Generative Adversarial Networks for Attack Generation against
Intrusion Detection (link)
Against seven traditional ML-based NIDS: SVM, naïve Bayes, MLP, logistic regression,
decision tree, random forest, and k-NN classifier
A GAN architecture called IDSGAN (Intrusion Detection System GAN) is proposed
NSL-KDD dataset was used for training the classifiers, and for evaluating the
adversarial samples (with perturbed feature vectors)
• Yang et al. (2018) Adversarial Examples Against the Deep Learning Based
Network Intrusion Detection Systems (link)
Against a deep NN model using the same features from the NSL-KDD dataset as in
Lin et al. (2018)
C&W, ZOO (Zeroth Order Optimization), and a GAN-based attack were used to add
small perturbations to the input feature vectors, so as to deceive the deep NN model
and misclassify malicious network packets as benign
Gray-box Adversarial Attacks on ML-based NIDS
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Adversarial Attacks on NIDS
• Kuppa et al. (2019) Black Box Attacks on Deep Anomaly Detectors (link)
Gray-box evasion attack
Attacked are seven anomaly detectors: autoencoder, One-Class SVM, autoencoder
with Gaussian Mixture Model, anoGAN, deep SVM, isolation forests, and an
adversarially learned model
The seven classifiers were trained on the CSE-CIC-IDS2018 dataset
The work employs a manifold approximation algorithm for generating adversarial
examples, by using KL divergence to constrain the changes in input instances
These attacks generated full PCAP files (and not just modified feature vectors, as in
most previous works)
o I.e., this is considered an end-to-end attack, based on the attack’s output in the taxonomy by
Rosenberg et al. (2021)
o The previously described attacks are considered “feature vector” attacks
o A limitation of the approach is that it wasn’t verified if the functionality of the perturbed files
is preserved
Gray-box Adversarial Attacks on ML-based NIDS
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Additional References
1. Rosenberg et al. (2021) – Adversarial Machine Learning Attacks and Defense
Methods in the Cyber Security Domain (link)
2. Ahmad (2020) – Network Intrusion Detection System: A Systematic Study of
Machine Learning and Deep Learning Approaches (link)
3. Cloudera Fast Forward – Deep Learning for Anomaly Detection (link)
4. Blog Post by Cuelogic Technologies – Evaluation of Machine Learning
Algorithms for Intrusion Detection System (link)
5. Intrusion Detection – Chapter 22 in “Introduction to Computer Security”
6. Blog Post by Gerry Saporito – A Deeper Dive into the NSL-KDD Data Set (link)