Towards Effective Detection of Botnet Attacks using BoT-IoT Dataset

In the world of cybersecurity, intrusion detection systems (IDS) have leveraged the power of artificial intelligence for the efficient detection of attacks. This is done by applying supervised machine learning (ML) techniques on labeled datasets. A growing body of literature has been devoted to the use of BoT-IoT dataset for IDS based ML frameworks. A few number of related works have recognized the need for a balanced dataset and applied techniques to alleviate the issue of imbalance. However, a significant amount of related research works failed to treat the imbalance in the BoT-IoT dataset. A lack of unanimity was observed in the literature towards the definition of taxonomy for balancing techniques. The study presented here seeks to explore the degree to which the imbalance of the dataset has been treated and to determine the taxonomy of techniques used. In this thesis, a comparison analysis is performed by using a small subset of an entire dataset to determine the threshold sample limit at which the model achieves the highest accuracy. In addition to this analysis, a study was conducted to determine the extent to which each feature of the dataset has an impact on the threshold performance. The study is implemented on the BoT-IoT dataset using three supervised ML classifiers: K-nearest Neighbor, Random Forest, and Logistic Regression. The four principal findings of this thesis are: existing taxonomies are not understood and imbalance of the dataset is not treated; high performance across all metrics is achieved on a highly imbalanced dataset; model is able to achieve the threshold performance using a small subset of samples; certain features had varying impact on the threshold value using different techniques

Review of Detection Denial of Service Attacks using Machine Learning through Ensemble Learning

Today's network hacking is more resource-intensive because the goal is to prohibit the user from using the network's resources when the target is either offensive or for financial gain, especially in businesses and organizations. That relies on the Internet like Amazon Due to this, several techniques, such as artificial intelligence algorithms like machine learning (ML) and deep learning (DL), have been developed to identify intrusion and network infiltration and discriminate between legitimate and unauthorized users. Application of machine learning and ensemble learning algorithms to various datasets, consideration of homogeneous ensembles using a single algorithm type or heterogeneous ensembles using several algorithm types, and evaluation of the discovery outcomes in terms of accuracy or discovery error for detecting attacks. The survey literature provides an overview of the many approaches and approaches of one or more machine-learning algorithms used in various datasets to identify denial of service attacks. It has also been shown that employing the hybrid approach is the most common and produces better attack detection outcomes than using the sole approaches. Numerous machine learning techniques, including support vector machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning like random forest (RF), bagging, and boosting, are illustrated in this work (DT). That is employed in several articles to identify different denial of service (DoS) assaults, including the trojan horse, teardrop, land, smurf, flooding, and worm. That attacks network traffic and resources to deny users access to the resources or to steal confidential information from the company without damaging the system and employs several algorithms to obtain high attack detection accuracy and low false alarm rates

Multi-layer Perceptron Model for Mitigating Distributed Denial of Service Flood Attack in Internet Kiosk Based Electronic Voting

Distributed Denial-of-Service (DDoS) flood attack targeting an Internet Kiosk voting environment can deprive voters from casting their ballots in a timely manner. The goal of the DDoS flood attack is to make voting server unavailable to voters during election process. In this paper, we present a Multilayer Perceptron (MLP) algorithm to mitigate DDoS flood attack in an e-voting environment and prevent such attack from disrupting availability of the vulnerable voting server. The developed intelligent DDoS flood mitigation model based on MLP Technique was simulated in MATLAB R2017a. The mitigation model was evaluated using server utilization performance metrics in e-voting. The results after the introduction of the developed mitigation model into the DDoS attack model reduced the server utilization from 1 to 0.4 indicating normal traffic. MLP showed an accuracy of 95% in mitigating DDoS flood attacks providing availability of voting server resources for convenient and timely casting of ballots as well as provide for credible delivery of electronic democratic decision making

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.publishedVersio

Isolation of DDoS Attacks and Flash Events in Internet Traffic Using Deep Learning Techniques

The adoption of network function visualization (NFV) and software-defined radio (SDN) has created a tremendous increase in Internet traffic due to flexibility brought in the network layer. An increase in traffic flowing through the network poses a security threat that becomes tricky to detect and hence selects an appropriate mitigation strategy. Under such a scenario occurrence of the distributed denial of service (DDoS) and flash events (FEs) affect the target servers and interrupt services. Isolating the attacks is the first step before selecting an appropriate mitigation technique. However, detecting and isolating the DDoS attacks from FEs when happening simultaneously is a challenge that has attracted the attention of many researchers. This study proposes a deep learning framework to detect the FEs and DDoS attacks occurring simultaneously in the network and isolates one from the other. This step is crucial in designing appropriate mechanisms to enhance network resilience against such cyber threats. The experiments indicate that the proposed model possesses a high accuracy level in detecting and isolating DDoS attacks and FEs in networked systems

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Detection and Explanation of Distributed Denial of Service (DDoS) Attack Through Interpretable Machine Learning

Distributed denial of service (DDoS) is a network-based attack where the aim of the attacker is to overwhelm the victim server. The attacker floods the server by sending enormous amount of network packets in a distributed manner beyond the servers capacity and thus causing the disruption of its normal service. In this dissertation, we focus to build intelligent detectors that can learn by themselves with less human interactions and detect DDoS attacks accurately. Machine learning (ML) has promising outcomes throughout the technologies including cybersecurity and provides us with intelligence when applied on Intrusion Detection Systems (IDSs). In addition, from the state-of-the-art ML-based IDSs, the Ensemble classifier (combination of classifiers) outperforms single classifier. Therefore, we have implemented both supervised and unsupervised ensemble frameworks to build IDSs for better DDoS detection accuracy with lower false alarms compared to the existing ones. Our experimentation, done with the most popular and benchmark datasets such as NSL-KDD, UNSW-NB15, and CICIDS2017, have achieved at most detection accuracy of 99.1% with the lowest false positive rate of 0.01%. As feature selection is one of the mandatory preprocessing phases in ML classification, we have designed several feature selection techniques for better performances in terms of DDoS detection accuracy, false positive alarms, and training times. Initially, we have implemented an ensemble framework for feature selection (FS) methods which combines almost all well-known FS methods and yields better outcomes compared to any single FS method.The goal of my dissertation is not only to detect DDoS attacks precisely but also to demonstrate explanations for these detections. Interpretable machine learning (IML) technique is used to explain a detected DDoS attack with the help of the effectiveness of the corresponding features. We also have implemented a novel feature selection approach based on IML which helps to find optimum features that are used further to retrain our models. The retrained model gives better performances than general feature selection process. Moreover, we have developed an explainer model using IML that identifies detected DDoS attacks with proper explanations based on effectiveness of the features. The contribution of this dissertation is five-folded with the ultimate goal of detecting the most frequent DDoS attacks in cyber security. In order to detect DDoS attacks, we first used ensemble machine learning classification with both supervised and unsupervised classifiers. For better performance, we then implemented and applied two feature selection approaches, such as ensemble feature selection framework and IML based feature selection approach, both individually and in a combination with supervised ensemble framework. Furthermore, we exclusively added explanations for the detected DDoS attacks with the help of explainer models that are built using LIME and SHAP IML methods. To build trustworthy explainer models, a detailed survey has been conducted on interpretable machine learning methods and on their associated tools. We applied the designed framework in various domains, like smart grid and NLP-based IDS to verify its efficacy and ability of performing as a generic model