A system for-denial-of-service-attack-detection-based-on-multivariate-correla...LeMeniz Infotech
A system for-denial-of-service-attack-detection-based-on-multivariate-correlation-analysis.Interconnected systems, such as Web servers, database servers, cloud computing servers etc, are now under threads from network attackers
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
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/
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
A system for-denial-of-service-attack-detection-based-on-multivariate-correla...LeMeniz Infotech
A system for-denial-of-service-attack-detection-based-on-multivariate-correlation-analysis.Interconnected systems, such as Web servers, database servers, cloud computing servers etc, are now under threads from network attackers
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
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/
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
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 of Detection DDOS Attack Detection Using Naive Bayes Classifier for Ne...journalBEEI
Distributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and more costs mitigation to increase in this era. Attackers used many zombie computers to exhaust the resources available to a network, application or service so that authorize users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. In the Network Forensic, a crime that occurs in the system network services can be sued in the court and the attackers will be punished in accordance with law. This research has the goal to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Naive Bayes method. Data were taken from the training and testing of network traffic in a core router in Master of Information Technology Research Laboratory University of Ahmad Dahlan Yogyakarta. The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks.
FLOODING ATTACK DETECTION AND MITIGATION IN SDN WITH MODIFIED ADAPTIVE THRESH...IJCNCJournal
Flooding attack is a network attack that sends a large amount of traffic to the victim networks or services to cause denial-of-service. In Software-Defined Networking (SDN) environment, this attack might not only breach the hosts and services but also the SDN controller. Besides, it will also cause a disconnection of links between the controller and the switches. Thus, an effective detection and mitigation technique of flooding attacks is required. Statistical analysis techniques are widely used for the detection and mitigation of flooding attacks. However, the effectiveness of these techniques strongly depends on the defined threshold. Defining the static threshold is a tedious job and most of the time produces a high false positive alarm .In this paper, we proposed the dynamic threshold which is calculated using modified adaptive threshold algorithm (MATA). The original ATA is based on the Exponential Weighted Moving Average (EWMA) formula which produces the high number of false alarms. To reduce the false alarms, the alarm signal will only be generated after a minimum number of consecutive violations of the threshold. This, however, has increased the false negative rate when the network is under attack. In order to reduce this false negative rate, MATA adapted the baseline traffic info of the network infrastructure. The comparative analysis of MATA and ATA are performed through the measurement of false negative rate, and accuracy of detection rate. Our experimental results show that MATA is able to reduce false negative rates up to 17.74% and increase the detection accuracy of 16.11%over the various types of flooding attacks at the transport layer.
Deep Learning based Threat / Intrusion detection systemAffine Analytics
The article is about a Threat/Intrusion Detection System, which could be used to detect such data leaks/breaches & take a preventive action to contain, if not stop the damage due to breach.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
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.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Distributed Denial of Service (DDoS) attack is the most severe cyber-attack that
affects the availability of critical applications. The attackers identify the weakness in
the machines and compromise them to involve in the flooding attack. During the
DDOS attack generation, they also gain access to secret information. These
computers are then used to wage a DDoS Attack in host’s computer. Through many
security measures have been taken in order to stop DDOS Attack to be protect our
data, the attackers have developed new techniques and attack methodology. Hence it
is very important that instead of reacting to new attacks, it is necessary to build a
complete DDoS solution that will defend all types of DDoS attacks. So, the
researchers must understand the cyber space and methods utilized to block the DDoS
attacks. The proposed system provides a unique method to detect DDoS attack using
Splunk. We propose two methods for prevention of DDoS attack. One is using
Randomly generated Captchas and other one is using Linux bash script to prevent
DDoS attack by automatically blocking IP of the client, who is sending multiple
request at a time.
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 of Detection DDOS Attack Detection Using Naive Bayes Classifier for Ne...journalBEEI
Distributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and more costs mitigation to increase in this era. Attackers used many zombie computers to exhaust the resources available to a network, application or service so that authorize users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. In the Network Forensic, a crime that occurs in the system network services can be sued in the court and the attackers will be punished in accordance with law. This research has the goal to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Naive Bayes method. Data were taken from the training and testing of network traffic in a core router in Master of Information Technology Research Laboratory University of Ahmad Dahlan Yogyakarta. The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks.
FLOODING ATTACK DETECTION AND MITIGATION IN SDN WITH MODIFIED ADAPTIVE THRESH...IJCNCJournal
Flooding attack is a network attack that sends a large amount of traffic to the victim networks or services to cause denial-of-service. In Software-Defined Networking (SDN) environment, this attack might not only breach the hosts and services but also the SDN controller. Besides, it will also cause a disconnection of links between the controller and the switches. Thus, an effective detection and mitigation technique of flooding attacks is required. Statistical analysis techniques are widely used for the detection and mitigation of flooding attacks. However, the effectiveness of these techniques strongly depends on the defined threshold. Defining the static threshold is a tedious job and most of the time produces a high false positive alarm .In this paper, we proposed the dynamic threshold which is calculated using modified adaptive threshold algorithm (MATA). The original ATA is based on the Exponential Weighted Moving Average (EWMA) formula which produces the high number of false alarms. To reduce the false alarms, the alarm signal will only be generated after a minimum number of consecutive violations of the threshold. This, however, has increased the false negative rate when the network is under attack. In order to reduce this false negative rate, MATA adapted the baseline traffic info of the network infrastructure. The comparative analysis of MATA and ATA are performed through the measurement of false negative rate, and accuracy of detection rate. Our experimental results show that MATA is able to reduce false negative rates up to 17.74% and increase the detection accuracy of 16.11%over the various types of flooding attacks at the transport layer.
Deep Learning based Threat / Intrusion detection systemAffine Analytics
The article is about a Threat/Intrusion Detection System, which could be used to detect such data leaks/breaches & take a preventive action to contain, if not stop the damage due to breach.
With the growth of computer networking, electronic commerce and web services, security networking systems have become very important to protect infomation and networks againts malicious usage or attacks. In this report, it is designed an Intrusion Detection System using two artificial neural networks: one for Intrusion Detection and the another for Attack Classification.
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.
Optimised malware detection in digital forensicsIJNSA Journal
On the Internet, malware is one of the most serious threats to system security. Most complex issues and
problems on any systems are caused by malware and spam. Networks and systems can be accessed and
compromised by malware known as botnets, which compromise other systems through a coordinated
attack. Such malware uses anti-forensic techniques to avoid detection and investigation. To prevent systems
from the malicious activity of this malware, a new framework is required that aims to develop an optimised
technique for malware detection. Hence, this paper demonstrates new approaches to perform malware
analysis in forensic investigations and discusses how such a framework may be developed.
Various OSI Layer Attacks and Countermeasure to Enhance the Performance of WS...IDES Editor
Wireless sensor networks are networks having non
wired infrastructure and dynamic topology. In OSI model each
layer is prone to various attacks, which halts the performance
of a network .In this paper several attacks on four layers of
OSI model are discussed and security mechanism is described
to prevent attack in network layer i.e wormhole attack. In
Wormhole attack two or more malicious nodes makes a covert
channel which attracts the traffic towards itself by depicting a
low latency link and then start dropping and replaying packets
in the multi-path route. This paper proposes promiscuous mode
method to detect and isolate the malicious node during
wormhole attack by using Ad-hoc on demand distance vector
routing protocol (AODV) with omnidirectional antenna. The
methodology implemented notifies that the nodes which are
not participating in multi-path routing generates an alarm
message during delay and then detects and isolate the
malicious node from network. We also notice that not only
the same kind of attacks but also the same kind of
countermeasures can appear in multiple layer. For example,
misbehavior detection techniques can be applied to almost all
the layers we discussed.
Distributed Denial of Service (DDoS) attack is the most severe cyber-attack that
affects the availability of critical applications. The attackers identify the weakness in
the machines and compromise them to involve in the flooding attack. During the
DDOS attack generation, they also gain access to secret information. These
computers are then used to wage a DDoS Attack in host’s computer. Through many
security measures have been taken in order to stop DDOS Attack to be protect our
data, the attackers have developed new techniques and attack methodology. Hence it
is very important that instead of reacting to new attacks, it is necessary to build a
complete DDoS solution that will defend all types of DDoS attacks. So, the
researchers must understand the cyber space and methods utilized to block the DDoS
attacks. The proposed system provides a unique method to detect DDoS attack using
Splunk. We propose two methods for prevention of DDoS attack. One is using
Randomly generated Captchas and other one is using Linux bash script to prevent
DDoS attack by automatically blocking IP of the client, who is sending multiple
request at a time.
HOMER Energy: Perspectives on Advanced Energy StorageHOMER Energy
HOMER Energy CEO Peter Lilienthal gave two invited presentations on storage recently – at Battery Power 2010 and at the Utility Wind Integration Group’s Annual Meeting. A variety of new storage technologies are being developed that will support the operation of smart, grid-connected microgrids.
A System for Denial of Service Attack Detection Based On Multivariate Corelat...IJCERT
in computing world, a denial-of-service (DoS) or is an process to make a machine or network resource unavailable to its regular users.DoS attack minimizes the efficiency of the server, inorder to increase the efficiency of the server it is necessary to identify the dos attacks hence MULTIVARIATE CORRELATION ANALYSIS(MCA)is used, this approach employs triangle area for obtaining the correlation information between the ip address. Based on extracted data the denial of service-attack is discovered and the response to the particular user is blocked, this maximizes the efficiency. Our proposed system is examined using KDD Cup 99 data set, and the influence of data on the performance of the proposed system is examined.
Statistical Analysis of Left-Censored Geochemical DataMSTomlinson
Geochemical datasets frequently contain left-censored data, i.e., the actual concentration falls in the range between 0 and the detection limit (DL). These data are referred to as nondetects (NDs). An ND does not necessarily mean the analyte was not present but, if it was present, it was at a concentration below the DL. In addition to NDs, contract labs often report estimated values (often flagged with a “J”) which lie between the DL and the reporting limit (RL). The RL is the level at or above which the lab will state the result is quantitative. A common approach to statistically analyzing left-censored data is to use substitution (e.g., ½DL). Although still a common practice, substitution can introduce bias to statistical analyses. Fortunately, there are a number of statistical techniques specifically designed to handle left-censored data that do not compromise the results of statistical analyses by using substitution. All of these techniques work with NDs and some work with estimated data. There are a number of techniques for calculating summary statistics for left-censored data including nonparametric Kaplan-Meier survival statistics, regression on order statistics (ROS), and the Turnbull interval-censored method. As the name implies, the Turnbull method works with interval censored data (i.e., quantitative data ≥RL, DL-RL [estimated], and 0-DL). In the latter two cases, an interval is used, i.e., the true value lies somewhere within the interval but picking a single value such as ½DL is not required. Interval-censored data can also be used on multivariate ordination techniques such as nonmetric multidimensional scaling (NMDS) and the interval-censored score test – an analog of the generalized Wilcoxon test. Kendall’s tau (τ) is a nonparametric correlation analysis that can be applied to left-censored data. For this test, the estimated (J-flagged) values are used. Kendall’s τ is analogous to the familiar parametric Pearson’s r and, like Pearson’s r, the test for Kendall’s τ also provides a measure of the correlation significance. The case study for this presentation will include the geochemical data and statistical results from the Hawaiʻi Ordnance Reef Follow-Up investigation of the U.S. Army’s Remotely Operated Underwater Munitions Recovery System.
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Click and Dragger: Denial and Deception on Android mobilegrugq
A presentation on OPSEC for mobile phones, covering the design and reasoning behind the CryptogenMod ROM and the DarkMatter app.
Source for DarkMatter: https://github.com/grugq/darkmatter
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.
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.
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.
Web server load prediction and anomaly detection from hypertext transfer prot...IJECEIAES
As network traffic increases and new intrusions occur, anomaly detection solutions based on machine learning are necessary to detect previously unknown intrusion patterns. Most of the developed models require a labelled dataset, which can be challenging owing to a shortage of publicly available datasets. These datasets are often too small to effectively train machine learning models, which further motivates the use of real unlabeled traffic. By using real traffic, it is possible to more accurately simulate the types of anomalies that might occur in a real-world network and improve the performance of the detection model. We present a method able to predict and categorize anomalies without the aid of a labelled dataset, demonstrating the model’s usability while also gathering a dataset from real noisy network traffic. The proposed long short-term memory (LTSM) based intrusion detection system was tested in a real-world setting of an antivirus company and was successful in detecting various intrusions using 5-minute windowing over both the predicted and real update curves thereby demonstrating its usefulness. Our contribution was the development of a robust model generally applicable to any hypertext transfer protocol (HTTP) traffic with almost real-time anomaly detection, while also outperforming earlier studies in terms of prediction accuracy.
APPLICATION-LAYER DDOS DETECTION BASED ON A ONE-CLASS SUPPORT VECTOR MACHINEIJNSA Journal
Application-layer Distributed Denial-of-Service (DDoS) attack takes advantage of the complexity and
diversity of network protocols and services. This kind of attacks is more difficult to prevent than other kinds
of DDoS attacks. This paper introduces a novel detection mechanism for application-layer DDoS attack
based on a One-Class Support Vector Machine (OC-SVM). Support vector machine (SVM) is a relatively
new machine learning technique based on statistics. OC-SVM is a special variant of the SVM and since
only the normal data is required for training, it is effective for detection of application-layer DDoS attack.
In this detection strategy, we first extract 7 features from normal users’ sessions. Then, we build normal
users’ browsing models by using OC-SVM. Finally, we use these models to detect application-layer DDoS
attacks. Numerical results based on simulation experiments demonstrate the efficacy of our detection
method.
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.
WEB ATTACK PREDICTION USING STEPWISE CONDITIONAL PARAMETER TUNING IN MACHINE ...IJCNCJournal
There is a rapid growth in internet and website usage. A wide variety of devices are used to access
websites, such as mobile phones, tablets, laptops, and personal computers. Attackers are finding more and
more vulnerabilities on websites that they can exploit for malicious purposes. A web application attack
occurs when cyber criminals gain access to unauthorized areas. Typically, attackers look for
vulnerabilities in web applications at the application layer. SQL injection attacks and Cross Site script
attacks is used to access web applications to obtain sensitive data. A key objective of this work is to
develop new features and investigate how automatic tuning of machine learning techniques can improve
the performance of Web Attack detections that use HTTP CSIC datasets to block and detect attacks. The
Stepwise Conditional parameter tuning in machine learning algorithms is a proposed model. This model is
a dynamic and automated parameter choosing and tuning based on the better outcome. This work also
compares two datasets for performance of the proposed model.
Web Attack Prediction using Stepwise Conditional Parameter Tuning in Machine ...IJCNCJournal
There is a rapid growth in internet and website usage. A wide variety of devices are used to access websites, such as mobile phones, tablets, laptops, and personal computers. Attackers are finding more and more vulnerabilities on websites that they can exploit for malicious purposes. A web application attack occurs when cyber criminals gain access to unauthorized areas. Typically, attackers look for vulnerabilities in web applications at the application layer. SQL injection attacks and Cross Site script attacks is used to access web applications to obtain sensitive data. A key objective of this work is to develop new features and investigate how automatic tuning of machine learning techniques can improve the performance of Web Attack detections that use HTTP CSIC datasets to block and detect attacks. The Stepwise Conditional parameter tuning in machine learning algorithms is a proposed model. This model is a dynamic and automated parameter choosing and tuning based on the better outcome. This work also compares two datasets for performance of the proposed model.
BE CS,IS FINAL YEAR PROJECT LIST FOR ACADEMIC YEAR 2019-2020IGEEKS TECHNOLOGIES
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)).
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)).
BE,ME MECHANICAL DESIGN AND THERMAL BASED PROJECTS 2019-2020IGEEKS TECHNOLOGIES
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)).
BE ECE,EEE,BIO MEDICAL,INSTRUMENTATION PROJECT TITLES FOR 2019-20220IGEEKS TECHNOLOGIES
IGeekS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)).
IEEE 2017-18 Final Year Project List by igeeks technologies,bangalore,www.makefinalyearproject.com,Cal Mr.Nandu Project Director-09590544567,Email :nanduigeeks2010@gmail.com
IGEEKS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines.
IGEEKS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines
IGEEKS Technologies is a company located in Bangalore, India. We have being recognized as a quality provider of hardware and software solutions for the student’s in order carry out their academic Projects. We offer academic projects at various academic levels ranging from graduates to masters (Diploma, BCA, BE, M. Tech, MCA, M. Sc (CS/IT)). As a part of the development training, we offer Projects in Embedded Systems & Software to the Engineering College students in all major disciplines
BE Mechanical Engineering Final Year Academic Projects BangaloreIGEEKS TECHNOLOGIES
We provides latest 2013 - 2014 Mini and Main Mechanical Engineering Projects, Project Ideas, Project Topics for final Year Mechanical and Automobile Engineering Students with Abstract, Source Code and Reports on Pneumatics, Hydraulics and Fluid mechanics Bangalore.
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.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
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.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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.
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.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
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.
A system for denial of-service attack detection based on multivariate correlation analysis
1. A SYSTEM FOR DENIAL-OF-
SERVICE ATTACK DETECTION
BASED ON MULTIVARIATE
CORRELATION ANALYSIS
Zhiyuan Tan, Aruna Jamdagni, Xiangjian He‡,
Senior Member, IEEE,
Priyadarsi Nanda, Member, IEEE, and Ren Ping Liu,
Member, IEEE,
2. ABSTRACT
computing servers etc, are now under threads from network attackers. As
one of most common and aggressive means, Denial-of-Service (DoS)
attacks cause serious impact on these computing systems. In this paper,
we present a DoS attack detection system that uses Multivariate
Correlation Analysis (MCA) for accurate network traffic characterization by
extracting the geometrical correlations between network traffic features.
Our MCA-based DoS attack detection system employs the principle of
anomaly-based detection in attack recognition. This makes our solution
capable of detecting known and unknown DoS attacks effectively by
learning the patterns of legitimate network traffic only. Furthermore, a
triangle-area-based technique is proposed to enhance and to speed up
the process of MCA. The effectiveness of our proposed detection system
is evaluated using KDD Cup 99 dataset, and the influences of both non-
normalized data and normalized data on the performance of the proposed
detection system are examined. The results show that our system
outperforms two other previously developed state-of-the-art approaches in
terms of detection accuracy.
3. EXISTING SYSTEM
Interconnected systems, such as Web servers, database servers, cloud
computing servers etc., are now under threads from network attackers. As
one of most common and aggressive means, Denial-of-Service (DoS)
attacks cause serious impact on these computing systems.
4. DISADVANTAGES OF THE EXISTING
SYSTEM
The existing method of detecting a DoS attack involves a complicated
and labor intensive task of keeping the signature database updated
because signature generation is a manual process and heavily involves
network security expertise.
The present system relies on Misuse-based detection, which detect
attacks by monitoring network activities and looking for matches with
the existing attack signatures. This method fails to detect novel-attacks.
5. PROPOSED SYSTEM
We present a DoS attack detection system that uses Multivariate Correlation
Analysis (MCA) for accurate network traffic characterization by extracting the
geometrical correlations between network traffic features. Our MCA-based
DoS attack detection system employs the principle of anomaly-based
detection in attack recognition. This makes our solution capable of detecting
known and unknown DoS attacks effectively by learning the patterns of
legitimate network traffic only. Furthermore, a triangle-area-based technique
is proposed to enhance and to speed up the process of MCA. The
effectiveness of our proposed detection system is evaluated using KDD Cup
99 dataset, and the influences of both non-normalized data and normalized
data on the performance of the proposed detection system are examined.
The results show that our system outperforms two other previously developed
state-of-the-art approaches in terms of detection accuracy.
6. ADVANTAGES OF PROPOSED
SYSTEM
This makes our solution capable of detecting known and unknown DoS
attacks effectively by learning the patterns of legitimate network traffic
only.
The results show that our system outperforms two other previously
developed state-of-the-art approaches in terms of detection accuracy.
To find various attacks from the user to avoid Network Intrusion.
7. PROBLEM STATEMENT
The objective of DDoS attacks is to consume resources, such as memory,
CPU processing space, or network bandwidth, in an attempt to make them
unreachable to end users by blocking network communication or denying
access to services.
8. IMPLEMENTATION
Implementation is the stage of the project when the theoretical design is
turned out into a working system. Thus it can be considered to be the
most critical stage in achieving a successful new system and in giving the
user, confidence that the new system will work and be effective.
The implementation stage involves careful planning, investigation of
the existing system and it’s constraints on implementation, designing of
methods to achieve changeover and evaluation of changeover methods
9. ARCHITECTURE
STEP 1:Basic
feature Generation
of Individual
Records
Triangle Area
Map
Generation for
Individual
RecordsNo
Normal Profile
Generation
Attack Detection
for Individual
Records
Tested Profile
Generation for
Individual Records
Feature
Normalization
Normal
Profiles
Normalized
Features
Raw/Original
Features
Training Phase
Test Phase
Network
Traffic
STEP 2:Multivariate Correlation
Analysis
STEP 3:Decision Making
10. MODULES
User Module
Multivariate Correlation Analysis
Detection Mechanisms
Computational complexity And Time Cost Analysis
11. MODULE DESCRIPTION
User Module
In this module, Users are having authentication and security to access the
detail which is presented in the ontology system. Before accessing or
searching the details user should have the account in that otherwise they
should register first.
12. MULTIVARIATE CORRELATION
ANALYSIS
DoS attack traffic behaves differently from the legitimate network traffic,
and the behavior of network traffic is reflected by its statistical properties.
To well describe these statistical properties, we present a novel
Multivariate Correlation Analysis (MCA) approach in this section. This
MCA approach employs triangle area for extracting the correlative
information between the features within an observed data object.
13. DETECTION MECHANISMS
We present a threshold-based anomaly detector, whose normal profiles are
generated using purely legitimate network traffic records and utilized for
future comparisons with new incoming investigated traffic records. The
dissimilarity between a new incoming traffic record and the respective normal
profile is examined by the proposed detector. If the dissimilarity is greater
than a pre-determined threshold, the traffic record is flagged as an attack.
Otherwise, it is labeled as a legitimate traffic record. Clearly, normal profiles
and thresholds have direct influence on the performance of a threshold-based
detector. A low quality normal profile causes an inaccurate characterization to
legitimate network traffic. Thus, we first apply the proposed trianglearea-
based MCA approach to analyze legitimate network traffic, and the generated
TAMs are then used to supply quality features for normal profile generation.
14. COMPUTATIONAL COMPLEXITY AND
TIME COST ANALYSIS
We conduct an analysis on the computational complexity and the time
cost of our proposed MCA-based detection system. On one hand, as
discussed in, triangle areas of all possible combinations of any two distinct
features in a traffic record need to be computed when processing our
proposed MCA. The former technique extracts the geometrical
correlations hidden in individual pairs of two distinct features within each
network traffic record, and offers more accurate characterization for
network traffic behaviors. The latter technique facilitates our system to be
able to distinguish both known and unknown DoS attacks from legitimate
network traffic.
15. MINIMUM HARDWARE CONFIGURATION
OF THE PROPOSED SYSTEM
Processor : Intel/AMD
Speed : 1.1 GHz
RAM : 256 MB
Hard Disk : 20 GB
Keyboard : Standard Keyboard
Mouse : Standard Mouse
Monitor : SVGA/LCD
16. SOFTWARE CONFIGURATION OF THE
PROPOSED SYSTEM
Operating System : Windows
Java Version : JDK 1.7/1.8
Application Server : Tomcat v6/7
Front End : HTML, Java, JSP, CSS
Scripts : JavaScript
IDE : Eclipse
Database : MySQL 5.5
Database Connectivity : JDBC
17. REFERENCES
V. Paxson, “Bro: A System for Detecting Network Intruders in Realtime,” Computer
Networks, vol. 31, pp. 2435-2463, 1999.
P. Garca-Teodoro, J. Daz-Verdejo, G. Maci-Fernndez, and E. Vzquez, “Anomaly-
based Network Intrusion Detection: Techniques, Systems and Challenges,”
Computers & Security, vol. 28, pp. 18-28, 2009.
D. E. Denning, “An Intrusion-detection Model,” IEEE Transactions on Software
Engineering, pp. 222-232, 1987.
K. Lee, J. Kim, K. H. Kwon, Y. Han, and S. Kim, “DDoS attack detection method
using cluster analysis,” Expert Systems with Applications, vol. 34, no. 3, pp. 1659-
1665, 2008.
A. Tajbakhsh, M. Rahmati, and A. Mirzaei, “Intrusion detection using fuzzy
association rules,” Applied Soft Computing, vol. 9, no. 2, pp. 462-469, 2009.