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.
DDOS ATTACKS DETECTION USING DYNAMIC ENTROPY INSOFTWARE-DEFINED NETWORK PRACT...IJCNCJournal
Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the
flexibility and simplicity in network operation and management through a centralized controller. These
features help SDN to easily adapt to the expansion of network requirements, but it is also a weakness when
it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially
Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network
resources and causes congestion in the entire network. In this research, we will introduce a DDoS
detection model based on the statistical method with a dynamic threshold value that changes over time.
Along with the simulation result, we build a practical SDN model to apply our method, the results show
that our method can detect DDoS attacks rapidly with high accuracy.
DDoS Attacks Detection using Dynamic Entropy in Software-Defined Network Prac...IJCNCJournal
Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the flexibility and simplicity in network operation and management through a centralized controller. These features help SDN to easily adapt tothe expansion of networkrequirements, but it is also a weakness when it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network resources and causes congestion in the entire network. In this research, we will introduce a DDoS detection model based on the statistical method with a dynamic threshold value that changes over time. Along with the simulation result, we build a practical SDN model to apply our method, the results show that our method can detectD DoS attacks rapidly with high accuracy.
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
A New Way of Identifying DOS Attack Using Multivariate Correlation Analysisijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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.
DDOS ATTACKS DETECTION USING DYNAMIC ENTROPY INSOFTWARE-DEFINED NETWORK PRACT...IJCNCJournal
Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the
flexibility and simplicity in network operation and management through a centralized controller. These
features help SDN to easily adapt to the expansion of network requirements, but it is also a weakness when
it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially
Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network
resources and causes congestion in the entire network. In this research, we will introduce a DDoS
detection model based on the statistical method with a dynamic threshold value that changes over time.
Along with the simulation result, we build a practical SDN model to apply our method, the results show
that our method can detect DDoS attacks rapidly with high accuracy.
DDoS Attacks Detection using Dynamic Entropy in Software-Defined Network Prac...IJCNCJournal
Software-Defined Network (SDN) is an innovative network architecture with the goal of providing the flexibility and simplicity in network operation and management through a centralized controller. These features help SDN to easily adapt tothe expansion of networkrequirements, but it is also a weakness when it comes to security. With centralized architecture, SDN is vulnerable to cyber-attacks, especially Distributed Denial of Service (DDoS) attack. DDoS is a popular attack type which consumes all network resources and causes congestion in the entire network. In this research, we will introduce a DDoS detection model based on the statistical method with a dynamic threshold value that changes over time. Along with the simulation result, we build a practical SDN model to apply our method, the results show that our method can detectD DoS attacks rapidly with high accuracy.
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
A New Way of Identifying DOS Attack Using Multivariate Correlation Analysisijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
DDOS DETECTION IN SOFTWARE-DEFINED NETWORK (SDN) USING MACHINE LEARNINGIJCI JOURNAL
In recent years, the concept of cloud computing and the software-defined network (SDN) have spread
widely. The services provided by many sectors such as medicine, education, banking, and transportation
are being replaced gradually with cloud-based applications. Consequently, the availability of these
services is critical. However, the cloud infrastructure and services are vulnerable to attackers who aim to
breach its availability. One of the major threats to any system availability is a Denial-of-Service (DoS)
attack, which is intended to deny the legitimate user from accessing cloud resources. The Distributed
Denial-of-Service attack (DDoS) is a type of DoS attack which is considerably more effective and
dangerous. A lot of efforts have been made by the research community to detect DDoS attacks, however,
there is still a need for further efforts in this germane field. In this paper, machine learning techniques are
utilized to build a model that can detect DDoS attacks in Software-Defined Networks (SDN). The used ML
algorithms have shown high performance in the earliest studies; hence they have been used in this study
along with feature selection technique. Therefore, our model utilized these algorithms to detect DDoS
attacks in network traffic. The outcome of this experiment shows the impact of feature selection in
improving the model performance. Eventually, The Random Forest classifier has achieved the highest
accuracy of 0.99 in detecting DDoS attack.
Network security monitoring elastic webinar - 16 june 2021Mouaz Alnouri
The difference between successfully defending an attack or failing to compromise is your ability to understand what’s happening in your network better than your adversary. Choosing the right network security monitoring (NSM) toolset is crucial to effectively monitor, detect, and respond to any potential threats in an organisation’s network.
In this webinar, we’ll uncover the best practices, trends, and challenges in network security monitoring (NSM) and how Elastic is being used as a core component to network security monitoring.
Highlights:
- What is network security monitoring (NSM)?
- Types of network data
- Common toolset
- Overcoming challenges with network security monitoring
- Using Machine Learning for network security monitoring
- Demo
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/
A review on software defined network security risks and challengesTELKOMNIKA JOURNAL
Software defined network is an emerging network architecture that separates the traditional
integrated control logic and data forwarding functionality into different planes, namely the control plane and
data forwarding plane. The data plane does an end-to-end data delivery. And the control plane does
the actual network traffic forwarding and routing between different network segments. In software defined
network the networking infrastructure layer is where the entire networking device, such as switches and
routers are connected with the separate controller layer with the help of standard called OpenFlow
protocol. The OpenFlow is a standard protocol that allows different vendor devices like juniper, cisco and
huawei switches to be connected to the controller. The centralization of the software defined network
(SDN) controller makes the network more flexible, manageable and dynamic, such as provisioning of
bandwidth, dynamic scale out and scale in compared to the traditional communication network, however,
the centralized SDN controller is more vulnerable to security risks such as DDOS and flow rule poisoning
attack. In this paper, we will explore the architectures, the principles of software defined network and
security risks associated with the centralized SDN controller and possible ways to mitigate these risks.
ACTOR CRITIC APPROACH BASED ANOMALY DETECTION FOR EDGE COMPUTING ENVIRONMENTSIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy.
Actor Critic Approach based Anomaly Detection for Edge Computing EnvironmentsIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy
This volume of the Open Datacenter Interoperable Network (ODIN) describes software defined networking (SDN) and OpenFlow. SDN is used to simplify network control and management, automate network virtualization services, and provide a platform from which to build agile ....
An intelligent system to detect slow denial of service attacks in software-de...IJECEIAES
Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.
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.
Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Co...Editor IJCATR
Network Intrusion detection and Countermeasure Election in virtual network systems (NICE) are used to establish a
defense-in-depth intrusion detection framework. For better attack detection, NICE incorporates attack graph analytical procedures into
the intrusion detection processes. We must note that the design of NICE does not intend to improve any of the existing intrusion
detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to
compromise VMs, thus preventing zombie VMs. NICE includes two main phases: deploy a lightweight mirroring-based network
intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual
system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified
vulnerability toward the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state. Once a
VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the
inspecting VM to make the potential attack behaviors prominent.
Review Paper on Predicting Network Attack Patterns in SDN using MLijtsrd
Software Defined Networking SDN provides several advantages like manageability, scaling, and improved performance. SDN has some security problems, especially if its controller is defense less over Distributed Denial of Service attacks. The mechanism and communication extent of the SDN controller is overloaded when DDoS attacks are performed against the SDN controller. So, as results of the useless flow built by the controller for the attack packets, the extent of the switch flow table becomes full, leading the network performance to decline to a critical threshold. The challenge lies in defining the set of rules on the SDN controller to dam malicious network connections. Historical network attack data are often wont to automatically identify and block the malicious connections. In this review paper, we are going to propose using ML algorithms, tested on collected network attack data, to get the potential malicious connections and potential attack destinations. We use four machine learning algorithms C4.5, Bayesian Network BayesNet , multidimensional language DT , and Naive Bayes to predict the host which will be attacked to support the historical data. DDoS attacks in Software Defined Network were detected by using ML based models. Some key features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Dr. C. Umarani | Gopalshree Kushwaha "Review Paper on Predicting Network Attack Patterns in SDN using ML" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35732.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35732/review-paper-on-predicting-network-attack-patterns-in-sdn-using-ml/dr-c-umarani
Learning-based Orchestrator for Intelligent Software-defined Networking Contr...ijseajournal
This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we
present this using Machine Learning in Software-Defined Networking technology. The first task consists of
extracting relevant information from the Data flow generated from a SDN and using them to learn, to
predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent
Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional
Neural Network model to separate the classes that we’re working on. The result was very promising the
model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
researchers looking for the trade-offs between SDN performance and IA customization
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attri
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
More Related Content
Similar to Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques.docx
DDOS DETECTION IN SOFTWARE-DEFINED NETWORK (SDN) USING MACHINE LEARNINGIJCI JOURNAL
In recent years, the concept of cloud computing and the software-defined network (SDN) have spread
widely. The services provided by many sectors such as medicine, education, banking, and transportation
are being replaced gradually with cloud-based applications. Consequently, the availability of these
services is critical. However, the cloud infrastructure and services are vulnerable to attackers who aim to
breach its availability. One of the major threats to any system availability is a Denial-of-Service (DoS)
attack, which is intended to deny the legitimate user from accessing cloud resources. The Distributed
Denial-of-Service attack (DDoS) is a type of DoS attack which is considerably more effective and
dangerous. A lot of efforts have been made by the research community to detect DDoS attacks, however,
there is still a need for further efforts in this germane field. In this paper, machine learning techniques are
utilized to build a model that can detect DDoS attacks in Software-Defined Networks (SDN). The used ML
algorithms have shown high performance in the earliest studies; hence they have been used in this study
along with feature selection technique. Therefore, our model utilized these algorithms to detect DDoS
attacks in network traffic. The outcome of this experiment shows the impact of feature selection in
improving the model performance. Eventually, The Random Forest classifier has achieved the highest
accuracy of 0.99 in detecting DDoS attack.
Network security monitoring elastic webinar - 16 june 2021Mouaz Alnouri
The difference between successfully defending an attack or failing to compromise is your ability to understand what’s happening in your network better than your adversary. Choosing the right network security monitoring (NSM) toolset is crucial to effectively monitor, detect, and respond to any potential threats in an organisation’s network.
In this webinar, we’ll uncover the best practices, trends, and challenges in network security monitoring (NSM) and how Elastic is being used as a core component to network security monitoring.
Highlights:
- What is network security monitoring (NSM)?
- Types of network data
- Common toolset
- Overcoming challenges with network security monitoring
- Using Machine Learning for network security monitoring
- Demo
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/
A review on software defined network security risks and challengesTELKOMNIKA JOURNAL
Software defined network is an emerging network architecture that separates the traditional
integrated control logic and data forwarding functionality into different planes, namely the control plane and
data forwarding plane. The data plane does an end-to-end data delivery. And the control plane does
the actual network traffic forwarding and routing between different network segments. In software defined
network the networking infrastructure layer is where the entire networking device, such as switches and
routers are connected with the separate controller layer with the help of standard called OpenFlow
protocol. The OpenFlow is a standard protocol that allows different vendor devices like juniper, cisco and
huawei switches to be connected to the controller. The centralization of the software defined network
(SDN) controller makes the network more flexible, manageable and dynamic, such as provisioning of
bandwidth, dynamic scale out and scale in compared to the traditional communication network, however,
the centralized SDN controller is more vulnerable to security risks such as DDOS and flow rule poisoning
attack. In this paper, we will explore the architectures, the principles of software defined network and
security risks associated with the centralized SDN controller and possible ways to mitigate these risks.
ACTOR CRITIC APPROACH BASED ANOMALY DETECTION FOR EDGE COMPUTING ENVIRONMENTSIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy.
Actor Critic Approach based Anomaly Detection for Edge Computing EnvironmentsIJCNCJournal
The pivotal role of data security in mobile edge-computing environments forms the foundation for the
proposed work. Anomalies and outliers in the sensory data due to network attacks will be a prominent
concern in real time. Sensor samples will be considered from a set of sensors at a particular time instant as
far as the confidence level on the decision remains on par with the desired value. A “true” on the
hypothesis test eventually means that the sensor has shown signs of anomaly or abnormality and samples
have to be immediately ceased from being retrieved from the sensor. A deep learning Actor-Criticbased
Reinforcement algorithm proposed will be able to detect anomalies in the form of binary indicators and
hence decide when to withdraw from receiving further samples from specific sensors. The posterior trust
value influences the value of the confidence interval and hence the probability of anomaly detection. The
paper exercises a single-tailed normal function to determine the range of the posterior trust metric. The
decision taken by the prediction model will be able to detect anomalies with a good percentage of anomaly
detection accuracy
This volume of the Open Datacenter Interoperable Network (ODIN) describes software defined networking (SDN) and OpenFlow. SDN is used to simplify network control and management, automate network virtualization services, and provide a platform from which to build agile ....
An intelligent system to detect slow denial of service attacks in software-de...IJECEIAES
Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.
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.
Evasion Streamline Intruders Using Graph Based Attacker model Analysis and Co...Editor IJCATR
Network Intrusion detection and Countermeasure Election in virtual network systems (NICE) are used to establish a
defense-in-depth intrusion detection framework. For better attack detection, NICE incorporates attack graph analytical procedures into
the intrusion detection processes. We must note that the design of NICE does not intend to improve any of the existing intrusion
detection algorithms; indeed, NICE employs a reconfigurable virtual networking approach to detect and counter the attempts to
compromise VMs, thus preventing zombie VMs. NICE includes two main phases: deploy a lightweight mirroring-based network
intrusion detection agent (NICE-A) on each cloud server to capture and analyze cloud traffic. A NICE-A periodically scans the virtual
system vulnerabilities within a cloud server to establish Scenario Attack Graph (SAGs), and then based on the severity of identified
vulnerability toward the collaborative attack goals, NICE will decide whether or not to put a VM in network inspection state. Once a
VM enters inspection state, Deep Packet Inspection (DPI) is applied, and/or virtual network reconfigurations can be deployed to the
inspecting VM to make the potential attack behaviors prominent.
Review Paper on Predicting Network Attack Patterns in SDN using MLijtsrd
Software Defined Networking SDN provides several advantages like manageability, scaling, and improved performance. SDN has some security problems, especially if its controller is defense less over Distributed Denial of Service attacks. The mechanism and communication extent of the SDN controller is overloaded when DDoS attacks are performed against the SDN controller. So, as results of the useless flow built by the controller for the attack packets, the extent of the switch flow table becomes full, leading the network performance to decline to a critical threshold. The challenge lies in defining the set of rules on the SDN controller to dam malicious network connections. Historical network attack data are often wont to automatically identify and block the malicious connections. In this review paper, we are going to propose using ML algorithms, tested on collected network attack data, to get the potential malicious connections and potential attack destinations. We use four machine learning algorithms C4.5, Bayesian Network BayesNet , multidimensional language DT , and Naive Bayes to predict the host which will be attacked to support the historical data. DDoS attacks in Software Defined Network were detected by using ML based models. Some key features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Dr. C. Umarani | Gopalshree Kushwaha "Review Paper on Predicting Network Attack Patterns in SDN using ML" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd35732.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-network/35732/review-paper-on-predicting-network-attack-patterns-in-sdn-using-ml/dr-c-umarani
Learning-based Orchestrator for Intelligent Software-defined Networking Contr...ijseajournal
This paper presents an overview of our learning-based orchestrator for intelligent Open vSwitch that we
present this using Machine Learning in Software-Defined Networking technology. The first task consists of
extracting relevant information from the Data flow generated from a SDN and using them to learn, to
predict and to accurately identify the optimal destination OVS using Reinforcement Learning and QLearning Algorithm. The second task consists to select this using our hybrid orchestrator the optimal
Intelligent SDN controllers with Supervised Learning. Therefore, we propose as a solution using Intelligent
Software-Defined Networking controllers (SDN) frameworks, OpenFlow deployments and a new intelligent
hybrid Orchestration for multi SDN controllers. After that, we feeded these feature to a Convolutional
Neural Network model to separate the classes that we’re working on. The result was very promising the
model achieved an accuracy of 72.7% on a database of 16 classes. In any case, this paper sheds light to
researchers looking for the trade-offs between SDN performance and IA customization
Similar to Detecting_and_Mitigating_Botnet_Attacks_in_Software-Defined_Networks_Using_Deep_Learning_Techniques.docx (20)
A Personal Privacy Data Protection Scheme for Encryption and Revocation of Hi...Shakas Technologies
A Personal Privacy Data Protection Scheme for Encryption and Revocation of High-Dimensional Attri
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Detecting Mental Disorders in social Media through Emotional patterns-The cas...Shakas Technologies
Detecting Mental Disorders in social Media through Emotional patterns-The case of Anorexia and depression
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
CO2 EMISSION RATING BY VEHICLES USING DATA SCIENCE
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evo...Shakas Technologies
Identifying Hot Topic Trends in Streaming Text Data Using News Sequential Evolution Model Based on Distributed Representations.
Shakas Technologies ( Galaxy of Knowledge)
#11/A 2nd East Main Road,
Gandhi Nagar,
Vellore - 632006.
Mobile : +91-9500218218 / 8220150373| land line- 0416- 3552723
Shakas Training & Development | Shakas Sales & Services | Shakas Educational Trust|IEEE projects | Research & Development | Journal Publication |
Email : info@shakastech.com | shakastech@gmail.com |
website: www.shakastech.com
Facebook: https://www.facebook.com/pages/Shakas-Technologies
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
1. Base paper Title: Detecting and Mitigating Botnet Attacks in Software-Defined Networks
Using Deep Learning Techniques
Modified Title: Using Deep Learning Techniques to Identify and Reduce Botnet Attacks in
Software-Defined Networks
Abstract
Software-Defined Networking (SDN) is an emerging architecture that enables flexible
and easy management and communication of large-scale networks. It offers programmable and
centralized interfaces for making complex network decisions dynamically and seamlessly.
However, SDN provides opportunities for businesses and individuals to build network
applications based on their demands and improve their services. In contrast, it started to face a
new array of security and privacy challenges and simultaneously introduced the threats of a
single point of failure. Usually, attackers launch malicious attacks such as botnets and
Distributed Denial of Service (DDoS) to the controller through OpenFlow switches. Deep
learning (DL)-based security applications are trending, effectively detecting and mitigating
potential threats with fast response. In this article, we analyze and show the performance of the
DL methods to detect botnet-based DDoS attacks in an SDN-supported environment. A newly
self-generated dataset is used for the evaluation. We also used feature weighting and tuning
methods to select the best subset of features. We verify the measurements and simulation
outcomes over a self-generated dataset and real testbed settings. The main aim of this study is
to find a lightweight DL method with baseline hyper-parameters to detect botnet-based DDoS
attacks with features and data that can be easily acquired. We observed that the best subset of
features influences the performance of the DL method, and the prediction accuracy of the same
method could be variated with a different set of features. Finally, based on empirical results,
we found that the CNN method outperforms the dataset and real testbed settings. The detection
rate of CNN reaches 99% for normal flows and 97% for attack flows.
Existing System
The development of the internet is rapidly growing; the limitations of traditional
networks have been explored. The emerging issues of the conventional networks can be solved
by patching the network, which makes the network more bloated and the control ability of the
network becomes weaker. The invention of Software-Defined Networking (SDN) [1], [2] has
2. resolved these problems by decoupling the data and control planes. SDN became famous
among thenetwork community due to its novel architecture and can fulfill the demands of fast-
growing networks. SDN has a centralized control architecture, so the SDN controllers can
access all the OpenFlow switches in their range and control the entire network through the open
south API interfaces. It is also known as the three-layer network architecture, application,
control, and data layers. The application layer runs all the policies and rules the network
administrator defines, and the SDN controller can adopt these rules dynamically. Any
modification in the application layer may change the behavior of the whole network. The
application layer is an excellent development by the open-source platform, which does not
force the administrator to entirely relies on vendors [5]. Positively, the SDN allows
administrators to eliminate license constraints and cloud-develop customized network
applications over general-purpose hardware. The control layer is known as the brain of the
architecture, and SDN controllers run in this layer. The controllers receive the rules from the
application layer, decode them into readable messages, and forward them to the underlying
data layer; after that, they collect the feedback from the data layer and pass it back to the
application layer. Moreover, a decision is made on the control layer, and the rules are
implemented in the data layer. The data layer is non-intelligent, and different hardware devices,
such as routers, OpenFlow switches, etc., exist in this layer, and instructions are passed by the
control layer.
Drawback in Existing System
Data Availability and Quality: Deep learning models require large amounts of high-
quality data for effective training. Obtaining labeled data for botnet attacks in SDNs
can be challenging due to the dynamic and evolving nature of cyber threats.
Complexity of Network Traffic Patterns: SDNs generate complex and varied
network traffic patterns, making it difficult to accurately identify malicious activities
from normal network behavior. Deep learning models may struggle with understanding
these intricate patterns.
Resource Intensiveness: Deep learning models often demand significant
computational resources and time for training, especially for large-scale networks. This
can be a bottleneck in real-time threat detection and mitigation.
Adversarial Attacks: Deep learning models can be susceptible to adversarial attacks
where attackers manipulate inputs to deceive the model's predictions, leading to false
negatives or false positives in identifying botnet activities.
3. Proposed System
The proposed study and the adopted scene. Its accuracy reaches 99.37% with subset-3
features using generated dataset. During real testbed traffic, the detection rate of CNN
for normal flows is 99% and 97% for attack flows.
The authors proposed a distributed method based on CNN and LSTM with an additional
cloud-based component for detecting DDoS and phishing attacks.
The overhead of the switches and controller. Another hybrid method based on Artificial
Neural Networks (ANNs) and DNN was proposed
The proposed system produced effective results on the NID dataset compared to BoT-
IoT.
Algorithm
They do the hyper parameterization of SVM using the ‘‘Grey Wolf Optimization
(GWO) algorithm’’ to determine the critical features for a botnet attack.
A hybrid method of PSO algorithms with a voting mechanism to detect botnet attacks
in IoT.
All five algorithms for subset-3 features. It is observed that all the algorithms RNN,
CNN, MLP, LSTM, and DNN
Advantages
Anomaly Detection: Deep learning models excel at recognizing patterns and
anomalies in complex data, allowing them to identify unusual or suspicious behaviors
within network traffic more effectively than traditional rule-based systems.
Adaptability to Evolving Threats: Deep learning models can adapt and learn from
new data, making them potentially more resilient against evolving botnet attack
strategies that may have previously gone undetected.
Automation and Real-Time Detection: Once trained, deep learning models can
perform automated real-time analysis of network traffic, enabling swift detection and
response to potential botnet activities without human intervention.
Scalability: Deep learning models can scale efficiently to handle large volumes of
network traffic, making them suitable for monitoring and protecting expansive SDN
environments.
4. Software Specification
Processor : I3 core processor
Ram : 4 GB
Hard disk : 500 GB
Software Specification
Operating System : Windows 10 /11
Frond End : Python
Back End : Mysql Server
IDE Tools : Pycharm