Ronald Seoh ronaldseoh

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Analyzing Practical Effectiveness of Neural NIDS solutions: Reproducing “Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems”

Members: Samuel Englert, Pinaki Mohanty, Ronald Seoh, Akshaj Uppala, and Han Zhu

Introduction

In this project, we examined practical effectiveness and applicability of deep learning-based network intrusion detection systems (NIDS). While there have been significant advances in neural NIDS lately, it is yet unclear how they achieve superiority over previous approaches such as signature-based NIDS. More specifically, we need more insights into their potential drawbacks, and how well the method could potentially fit into real-life networking scenarios. Hence, we chose the state-of-the-art neural NIDS model and the evaluation results from Hashemi and Keller 2020 to conduct our analysis: Hashemi and Keller introduced the Reconstruction from Partial Observation (RePO) technique of leve

	'''
	This program is free software: you can redistribute it and/or modify
	it under the terms of the GNU General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.
	This program is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	GNU General Public License for more details.
	You should have received a copy of the GNU General Public License

	'''
	This program is free software: you can redistribute it and/or modify
	it under the terms of the GNU General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.

	This program is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
	GNU General Public License for more details.

	'''
	MIT License

	Copyright (c) 2019 Ronald Seoh

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	'''
	MIT License

	Copyright (c) 2020 Ronald Seoh

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	## System76 Scheduler
	# URL: https://github.com/pop-os/system76-scheduler
	# LICENSE(PKGBUILD): MIT
	pkgname=system76-scheduler
	pkgver="0.1.0"
	pkgrel=1
	pkgdesc="Auto-configure CFS for improved desktop responsiveness when on AC (based on Zen CFS settings)"
	arch=('any')
	url="https://github.com/pop-os/system76-scheduler"
	license=('MPL')