Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

Today, deep learning approaches are widely used to build Intrusion Detection Systems for securing IoT environments. However, the models’ hidden and complex nature raises various concerns, such as trusting the model output and understanding why the model made certain decisions. Researchers generally publish their proposed model’s settings and performance results based on a specific dataset and a classification model but do not report the proposed model’s output and findings. Similarly, many researchers suggest an IDS solution by focusing only on a single benchmark dataset and classifier. Such solutions are prone to generating inaccurate and biased results. This paper overcomes these limitations in previous work by analyzing various benchmark datasets and various individual and hybrid deep learning classifiers towards finding the best IDS solution for IoT that is efficient, lightweight, and comprehensive in detecting network anomalies. We also showed the model’s localized predictions and analyzed the top contributing features impacting the global performance of deep learning models. This paper aims to extract the aggregate knowledge from various datasets and classifiers and analyze the commonalities to avoid any possible bias in results and increase the trust and transparency of deep learning models. We believe this paper’s findings will help future researchers build a comprehensive IDS based on well-performing classifiers and utilize the aggregated knowledge and the minimum set of significantly contributing features

DDoS: DeepDefence and Machine Learning for identifying attacks

Distributed Denial of Service (DDoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through the network is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this work and developments have been made on proactive detection of attacks. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset published in year 2017,2018 and CICDDoS2019 and program has been developed in Matlab R17b, utilizing Wireshark for features extraction from the datasets. Network Intrusion attacks on critical oil and gas industrial installation become common nowadays, which in turn bring down the giant industrial sites to standstill and suffer financial impacts. This has made the production companies to started investing millions of dollars revenue to protect their critical infrastructure with such attacks with the active and passive solutions available. Our thesis constitutes a contribution to such domain, focusing mainly on security of industrial network, impersonation and attacking with DDoS

An intelligent, distributed and collaborative DDoS defense system

The Distributed Denial-of-Service (DDoS) attack is known as one of the most destructive attacks on the Internet. With the advent of new computing paradigms, such as Cloud and Mobile computing, and the emergence of pervasive technology, such as the Internet of Things, on one hand, these revolutionized technologies enable the availability of services and applications to everyone. On the other hand, these techniques also benefit attackers to exploit the vulnerabilities and deploy attacks in more efficient ways. Latest network security reports have shown that distributed Denial of Service (DDoS) attacks have been growing dramatically in volume, frequency, sophistication and impact, making it one of the most challenging threats in the Internet. An unfortunate state of affairs is that the remediation strategies have fallen behind attackers. The severe impact caused by recent DDoS attacks strongly indicates the need for an effective DDoS defense system. We study the current existing solution space, and summarize three fundamental requirements for an effective DDoS defense system: 1) an accurate detection with minimal false alarms; 2) an effective inline inspection and instant mitigation, and 3) a dynamic, distributed and collaborative defense infrastructure. This thesis aims at providing such a defense system that fulfills all the requirements. In this thesis, we explore and address the problem from three directions: 1) we strive to understand the existing detection strategies and provide a survey of an empirical analysis of machine learning based detection techniques; 2) we develop a novel hybrid detection model which ensembles a deep learning model for a practical flow by flow detection and a classic machine learning model that is aware of the network status, and 3) we present the design and implementation of an intelligent, distributed and collaborative DDoS defense system that effectively mitigate the impact of DDoS attacks. The performance evaluation results show that our proposed defense system is capable of effectively mitigating DDoS attacks impacts and maintaining a limited disturbing for legitimate services