NetGPT: Generative Pretrained Transformer for Network Traffic

Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as traffic classification, attack detection, resource scheduling, protocol analysis, and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. Expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks, and outperform state-of-the-art baselines by a wide margin

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