Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques

Ensemble Models for Intrusion Detection System Classification

Using data analytics in the problem of Intrusion Detection and Prevention Systems (IDS/IPS) is a continuous research problem due to the evolutionary nature of the problem and the changes in major influencing factors. The main challenges in this area are designing rules that can predict malware in unknown territories and dealing with the complexity of the problem and the conflicting requirements regarding high accuracy of detection and high efficiency. In this scope, we evaluated the usage of state-of-the-art ensemble learning models in improving the performance and efficiency of IDS/IPS. We compared our approaches with other existing approaches using popular open-source datasets available in this area

An intelligent system to detect slow denial of service attacks in software-defined networks

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

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

TOWARDS A HOLISTIC EFFICIENT STACKING ENSEMBLE INTRUSION DETECTION SYSTEM USING NEWLY GENERATED HETEROGENEOUS DATASETS

With the exponential growth of network-based applications globally, there has been a transformation in organizations\u27 business models. Furthermore, cost reduction of both computational devices and the internet have led people to become more technology dependent. Consequently, due to inordinate use of computer networks, new risks have emerged. Therefore, the process of improving the speed and accuracy of security mechanisms has become crucial.Although abundant new security tools have been developed, the rapid-growth of malicious activities continues to be a pressing issue, as their ever-evolving attacks continue to create severe threats to network security. Classical security techniquesfor instance, firewallsare used as a first line of defense against security problems but remain unable to detect internal intrusions or adequately provide security countermeasures. Thus, network administrators tend to rely predominantly on Intrusion Detection Systems to detect such network intrusive activities. Machine Learning is one of the practical approaches to intrusion detection that learns from data to differentiate between normal and malicious traffic. Although Machine Learning approaches are used frequently, an in-depth analysis of Machine Learning algorithms in the context of intrusion detection has received less attention in the literature.Moreover, adequate datasets are necessary to train and evaluate anomaly-based network intrusion detection systems. There exist a number of such datasetsas DARPA, KDDCUP, and NSL-KDDthat have been widely adopted by researchers to train and evaluate the performance of their proposed intrusion detection approaches. Based on several studies, many such datasets are outworn and unreliable to use. Furthermore, some of these datasets suffer from a lack of traffic diversity and volumes, do not cover the variety of attacks, have anonymized packet information and payload that cannot reflect the current trends, or lack feature set and metadata.This thesis provides a comprehensive analysis of some of the existing Machine Learning approaches for identifying network intrusions. Specifically, it analyzes the algorithms along various dimensionsnamely, feature selection, sensitivity to the hyper-parameter selection, and class imbalance problemsthat are inherent to intrusion detection. It also produces a new reliable dataset labeled Game Theory and Cyber Security (GTCS) that matches real-world criteria, contains normal and different classes of attacks, and reflects the current network traffic trends. The GTCS dataset is used to evaluate the performance of the different approaches, and a detailed experimental evaluation to summarize the effectiveness of each approach is presented. Finally, the thesis proposes an ensemble classifier model composed of multiple classifiers with different learning paradigms to address the issue of detection accuracy and false alarm rate in intrusion detection systems