1,925 research outputs found
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
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