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Smart Computer Security Audit: Reinforcement Learning with a Deep Neural Network Approximator
A significant challenge in modern computer security is the growing skill gap as intruder capabilities increase, making it necessary to begin automating elements of penetration testing so analysts can contend with the growing number of cyber threats. In this paper, we attempt to assist human analysts by automating a single host penetration attack. To do so, a smart agent performs different attack sequences to find vulnerabilities in a target system. As it does so, it accumulates knowledge, learns new attack sequences and improves its own internal penetration testing logic. As a result, this agent (AgentPen for simplicity) is able to successfully penetrate hosts it has never interacted with before. A computer security administrator using this tool would receive a comprehensive, automated sequence of actions leading to a security breach, highlighting potential vulnerabilities, and reducing the amount of menial tasks a typical penetration tester would need to execute. To achieve autonomy, we apply an unsupervised machine learning algorithm, Q-learning, with an approximator that incorporates a deep neural network architecture. The security audit itself is modelled as a Markov Decision Process in order to test a number of decisionmaking strategies and compare their convergence to optimality. A series of experimental results is presented to show how this approach can be effectively used to automate penetration testing using a scalable, i.e. not exhaustive, and adaptive approach
Aerated blast furnace slag filters for enhanced nitrogen and phosphorus removal from small wastewater treatment plants
Rock filters (RF) are a promising alternative technology for natural
wastewater treatment for upgrading WSP effluent. However, the application
of RF in the removal of eutrophic nutrients, nitrogen and phosphorus, is very
limited. Accordingly, the overall objective of this study was to develop a lowcost
RF system for the purpose of enhanced nutrient removal from WSP
effluents, which would be able to produce effluents which comply with the
requirements of the EU Urban Waste Water Treatment Directive (UWWTD)
(911271lEEC) and suitable for small communities. Therefore, a combination
system comprising a primary facultative pond and an aerated rock filter
(ARF) system-either vertically or horizontally loaded-was investigated at
the University of Leeds' experimental station at Esholt Wastewater
Treatment Works, Bradford, UK.
Blast furnace slag (BFS) and limestone were selected for use in the ARF
system owing to their high potential for P removal and their low cost. This
study involved three major qperiments: (1) a comparison of aerated
vertical-flow and horizontal-flow limestone filters for nitrogen removal; (2) a
comparison of aerated limestone + blast furnace slag (BFS) filter and
aerated BFS filters for nitrogen and phosphorus removal; and (3) a
comparison of vertical-flow and horizontal-flow BFS filters for nitrogen and
phosphorus removal.
The vertical upward-flow ARF system was found to be superior to the
horizontal-flow ARF system in terms of nitrogen removal, mostly thiough
bacterial nitrification processes in both the aerated limestone and BFS filter
studies. The BFS filter medium (whieh is low-cost) showed a much higher
potential in removing phosphortls from pond effluent than the limestone
medium. As a result, the combination of a vertical upward-flow ARF system
and an economical and effective P-removal filter medium, such as BFS,
was found to be an ideal optionfor the total nutrient removal of both nitrogen
and phosphorus from wastewater.
In parallel with these experiments, studies on the aerated BFS filter effective
life and major in-filter phosphorus removal pathways were carried out. From
the standard batch experiments of Pmax adsorption capacity of BFS, as well
as six-month data collection of daily average P-removal, it was found that
the effective life of the aerated BFS filter was 6.5 years. Scanning electron
microscopy and X-ray diffraction spectrometric analyses on the surface of
BFS, particulates and sediment samples revealed that the apparent
mechanisms of P-removal in the filter are adsorption on the amorphous
oxide phase of the BFS surface and precipitation within the filter
ScaRR: Scalable Runtime Remote Attestation for Complex Systems
The introduction of remote attestation (RA) schemes has allowed academia and
industry to enhance the security of their systems. The commercial products
currently available enable only the validation of static properties, such as
applications fingerprint, and do not handle runtime properties, such as
control-flow correctness. This limitation pushed researchers towards the
identification of new approaches, called runtime RA. However, those mainly work
on embedded devices, which share very few common features with complex systems,
such as virtual machines in a cloud. A naive deployment of runtime RA schemes
for embedded devices on complex systems faces scalability problems, such as the
representation of complex control-flows or slow verification phase.
In this work, we present ScaRR: the first Scalable Runtime Remote attestation
schema for complex systems. Thanks to its novel control-flow model, ScaRR
enables the deployment of runtime RA on any application regardless of its
complexity, by also achieving good performance. We implemented ScaRR and tested
it on the benchmark suite SPEC CPU 2017. We show that ScaRR can validate on
average 2M control-flow events per second, definitely outperforming existing
solutions.Comment: 14 page
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