Denial of Service Attacks: Detecting the Frailties of Machine Learning Algorithms in the Classification Process

Telecommunication has registered strong and rapid growth in the past decade. Accordingly, the monitoring of computers and networks is too complicated for network administrators. Hence, network security represents one of the biggest serious challenges that can be faced by network security communities. Taking into consideration the fact that e-banking, e-commerce, and business data will be shared on the computer network, these data may face a threat from intrusion. The purpose of this research is to propose a methodology that will lead to a high level and sustainable protection against cyberattacks. In particular, an adaptive anomaly detection framework model was developed using deep and machine learning algorithms to manage automatically-configured application-level firewalls. The standard network datasets were used to evaluate the proposed model which is designed for improving the cybersecurity system. The deep learning based on Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) and machine learning algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) algorithms were implemented to classify the Denial-of-Service attack (DoS) and Distributed Denial-of-Service (DDoS) attacks. The information gain method was applied to select the relevant features from the network dataset. These network features were significant to improve the classification algorithm. The system was used to classify DoS and DDoS attacks in four stand datasets namely KDD cup 199, NSL-KDD, ISCX, and ICI-ID2017. The empirical results indicate that the deep learning based on the LSTM-RNN algorithm has obtained the highest accuracy. The proposed system based on the LSTM-RNN algorithm produced the highest testing accuracy rate of 99.51% and 99.91% with respect to KDD Cup’99, NSL-KDD, ISCX, and ICI-Id2017 datasets, respectively. A comparative result analysis between the machine learning algorithms, namely SVM and KNN, and the deep learning algorithms based on the LSTM-RNN model is presented. Finally, it is concluded that the LSTM-RNN model is efficient and effective to improve the cybersecurity system for detecting anomaly-based cybersecurity.

Download Full-text

An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9317 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3765-3769

Author(s):

N. P. Ponnuviji ◽

M. Vigilson Prem

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Denial Of Service ◽

Learning Algorithms ◽

Attack Detection ◽

Machine Learning Algorithms ◽

Distributed Denial Of Service ◽

Detection Techniques ◽

Network Bandwidth ◽

Ddos Attack

Cloud Computing has revolutionized the Information Technology by allowing the users to use variety number of resources in different applications in a less expensive manner. The resources are allocated to access by providing scalability flexible on-demand access in a virtual manner, reduced maintenance with less infrastructure cost. The majority of resources are handled and managed by the organizations over the internet by using different standards and formats of the networking protocols. Various research and statistics have proved that the available and existing technologies are prone to threats and vulnerabilities in the protocols legacy in the form of bugs that pave way for intrusion in different ways by the attackers. The most common among attacks is the Distributed Denial of Service (DDoS) attack. This attack targets the cloud’s performance and cause serious damage to the entire cloud computing environment. In the DDoS attack scenario, the compromised computers are targeted. The attacks are done by transmitting a large number of packets injected with known and unknown bugs to a server. A huge portion of the network bandwidth of the users’ cloud infrastructure is affected by consuming enormous time of their servers. In this paper, we have proposed a DDoS Attack detection scheme based on Random Forest algorithm to mitigate the DDoS threat. This algorithm is used along with the signature detection techniques and generates a decision tree. This helps in the detection of signature attacks for the DDoS flooding attacks. We have also used other machine learning algorithms and analyzed based on the yielded results.

Download Full-text

Proposed approach to detect distributed denial of service attacks in software defined network using machine learning algorithms

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.8.10488 ◽

2018 ◽

Vol 7 (2.8) ◽

pp. 472 ◽

Cited By ~ 1

Author(s):

Shruti Banerjee ◽

Partha Sarathi Chakraborty ◽

. .

Keyword(s):

Machine Learning ◽

Denial Of Service ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Paper Machine ◽

Software Defined Network ◽

Distributed Denial Of Service ◽

Control Plane ◽

Data Plane ◽

And Control

SDN (Software Defined Network) is rapidly gaining importance of ‘programmable network’ infrastructure. The SDN architecture separates the Data plane (forwarding devices) and Control plane (controller of the SDN). This makes it easy to deploy new versions to the infrastructure and provides straightforward network virtualization. Distributed Denial-of-Service attack is a major cyber security threat to the SDN. It is equally vulnerable to both data plane and control plane. In this paper, machine learning algorithms such as Naïve Bayesian, KNN, K Means, K-Medoids, Linear Regression, use to classify the incoming traffic as usual or unusual. Above mentioned algorithms are measured using the two metrics: accuracy and detection rate. The best fit algorithm is applied to implement the signature IDS which forms the module 1 of the proposed IDS. Second Module uses open connections to state the exact node which is an attacker and to block that particular IP address by placing it in Access Control List (ACL), thus increasing the processing speed of SDN as a whole.

Download Full-text

Using Machine Learning to Build a Classification Model for IoT Networks to Detect Attack Signatures

International journal of Computer Networks & Communications ◽

10.5121/ijcnc.2020.12607 ◽

2020 ◽

Vol 12 (6) ◽

pp. 99-116

Author(s):

Mousa Al-Akhras ◽

Mohammed Alawairdhi ◽

Ali Alkoudari ◽

Samer Atawneh

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Denial Of Service ◽

Learning Algorithms ◽

Naïve Bayes ◽

Machine Learning Algorithms ◽

Classification Model ◽

Security And Privacy ◽

K Nearest Neighbors ◽

Detection Model

Internet of things (IoT) has led to several security threats and challenges within society. Regardless of the benefits that it has brought with it to the society, IoT could compromise the security and privacy of individuals and companies at various levels. Denial of Service (DoS) and Distributed DoS (DDoS) attacks, among others, are the most common attack types that face the IoT networks. To counter such attacks, companies should implement an efficient classification/detection model, which is not an easy task. This paper proposes a classification model to examine the effectiveness of several machine-learning algorithms, namely, Random Forest (RF), k-Nearest Neighbors (KNN), and Naïve Bayes. The machine learning algorithms are used to detect attacks on the UNSW-NB15 benchmark dataset. The UNSW-NB15 contains normal network traffic and malicious traffic instants. The experimental results reveal that RF and KNN classifiers give the best performance with an accuracy of 100% (without noise injection) and 99% (with 10% noise filtering), while the Naïve Bayes classifier gives the worst performance with an accuracy of 95.35% and 82.77 without noise and with 10% noise, respectively. Other evaluation matrices, such as precision and recall, also show the effectiveness of RF and KNN classifiers over Naïve Bayes.

Download Full-text