Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing network forensics with particle swarm and deep learning: The
  particle deep framework   The popularity of IoT smart things is rising, due to the automation they
provide and its effects on productivity. However, it has been proven that IoT
devices are vulnerable to both well established and new IoT-specific attack
vectors. In this paper, we propose the Particle Deep Framework, a new network
forensic framework for IoT networks that utilised Particle Swarm Optimisation
to tune the hyperparameters of a deep MLP model and improve its performance.
The PDF is trained and validated using Bot-IoT dataset, a contemporary
network-traffic dataset that combines normal IoT and non-IoT traffic, with well
known botnet-related attacks. Through experimentation, we show that the
performance of a deep MLP model is vastly improved, achieving an accuracy of
99.9% and false alarm rate of close to 0%.

15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an