https://hal.archives-ouvertes.fr/hal-03182934/document

Detection of reduction-of-quality DDoS attacks using Fuzzy Logic and machine learning algorithms

Text classification is an essential advance in characteristic dialect processing. It very well may be performed utilizing different classification algorithms. Hadoop Map Reduce is widely utilized in text classification to perform classification on colossal measure of text data. However, Map Reduce required a ton of time to perform the tasks thereby increasing latency and since the data is distributed over the cluster it builds time and thus reducing processing speed. Also Hadoop utilizes long queue of code. Motivated by this, we propose a basic yet compelling machine learning method which uses Naive Bayes classifier for text data. In Machine Learning approach, the classifier is built automatically by learning the properties of categories from a set of pre-defined training data. Hence, it can process complex furthermore, multi assortment information in dynamic situations. Here we propose a naive bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multiclass classification problems. We implemented the presented schemes using Machine Learning tool. The experimental results demonstrate the performance improvement in the classification technique.

Classification and Optimization Scheme for Text Data using Machine Learning Naïve Bayes Classifier

Text classification is an essential advance in characteristic dialect processing. It very well may be performed utilizing different classification algorithms. Hadoop Map Reduce is widely utilized in text classification to perform classification on colossal measure of text data. However, Map Reduce required a ton of time to perform the tasks thereby increasing latency and since the data is distributed over the cluster it builds time and thus reducing processing speed. Also, Hadoop utilizes long queue of code. Motivated by this, we propose a basic yet compelling machine learning method which uses Naive Bayes classifier for text data. In Machine Learning approach, the classifier is built automatically by learning the properties of categories from a set of predefined training data. Hence, it can process complex furthermore, multi assortmentinformation in dynamic situations. Here we propose a naive bayes classifier which scales directly with number of indicators and data points which can be used for both binary and multi-class classification problems. We implemented the presented schemes using Machine Learning tool. The experimental results demonstrate the performance improvement in the classification technique.

Optimization Scheme for Text Classification Using Machine Learning Naïve Bayes Classifier

Detecting Denial of Service (DoS) attacks on web servers has become extremely popular with cybercriminals and organized crime groups. A successful DoS attack on network resources reduces availability of service to a web site and backend resources, and could easily result in a loss of millions of dollars in revenue depending on company size. There are many DoS attack methods, each of which is critical to providing an understanding of the nature of the DoS attack class. There has been a rise in recent years of application-layer DoS attack methods that target web servers and are challenging to detect. An attack may be disguised to look like legitimate traffic, except it targets specific application packets or functions. Slow Read DoS attack is one type of slow HTTP attack targeting the application-layer. Slow Read attacks are often used to exploit weaknesses in the HTTP protocol, as it is the most widely used protocol on the Internet. In this paper, we use Full Packet Capture (FPC) datasets for detecting Slow Read DoS attacks with machine learning methods. All data collected originates in a live network environment. Our approach produces FPC features taken from network packets at the IP and TCP layers. Experimental results show that the machine learners were quite successful in identifying the Slow Read attacks with high detection and low false alarm rates using FPC data. Our experiment evaluates FPC datasets to determine the accuracy and efficiency of several detection models for Slow Read attacks. The experiment demonstrates that FPC features are discriminative enough to detect such attacks.

Detection Methods of Slow Read DoS Using Full Packet Capture Data

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

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WEB DDoS Attack Detection Method Based on Semisupervised Learning

Detecting DDoS attacks at application layer is quite challenging research problem. The recent methods are suffered from the poor accuracy performance of DDoS attack detection at application layer. In this paper, to mitigate current problems, classifier based system is proposed in which packets are captures, extraction of important fields those are required for detection and then apply classifier to detection of attack. In this paper, we studied and discussed the algorithm of Naive Bayes Multinomial for testing and training. The performance of this approach is compared with other existing classifiers into the terms of accuracy, true positive & false positive rates. The outcome of this paper is current method limitations and scope of improvement depicted from overall study and analysis. Additionally, the aim of this paper is to identify the research gap and limitations of studied method with review of previous methods.

Study on web DDOS attacks detection using multinomial classifier

Now days use of internet for the getting and sharing of knowledge is very common. The end users those are accessing internet or system are vulnerable to malicious user attacks which results into legitimate user prevented from accessing the websites. Recently there are several of methods presented for application layer DDoS attacks by considering the different properties of attacks. However most of methods are suffered from the poor accuracy performance of DDoS attack detection at application layer. Hence DDoS attacks has been low volume & act own as a legitimate transaction on layer seven means application layer hence such attacks are not detected easily by IDS (Intrusion Detection Systems) or firewall systems. We believe that, the accuracy and efficiency of attacks detection is based on correctness of capture data traffic. In state-of-art methods, there is no provision to remove the noisy data from the capture logs and hence leads to incorrect detection results. In this paper we presented the real time computer networks data capturing for normal as well as attack infected traffics, then design the preprocessing algorithm to remove irrelevant data to optimize the attack detection performance which helps to naive bayes algorithm to classification. In this architecture, we use LOIC doss attack generator tool to create attack at packet capturing time of communication network. The experimental results show that proposed data pre-processing method with naive bayes multinomial is easy and efficient as compared to state-of-art solutions. To classify normal packets and DDoS attack we use naive bayes multinomial classifier

Shital K. Ajagekar

Papers

Study on web DDOS attacks detection using multinomial classifier

Automated Approach for DDOS Attacks Detection Based on Naive Bayes Multinomial Classifier