2015
DOI: 10.18034/ajtp.v2i3.493
|View full text |Cite
|
Sign up to set email alerts
|

Crossing Point of Artificial Intelligence in Cybersecurity

Abstract: There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
34
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
8
2

Relationship

2
8

Authors

Journals

citations
Cited by 43 publications
(34 citation statements)
references
References 9 publications
0
34
0
Order By: Relevance
“…When the value of R becomes less than 0.0135, the three input switch sends the preset value to keep the system stable. This protection scheme not only protects the system during cyber-attack but also is much faster than manually tuned PID controller (Donepudi, 2015). For LFC neural network tuned PID controller, mitigate frequency disturbance 48% faster than manually tuned PID controller.…”
Section: Solution For Lfcmentioning
confidence: 94%
“…When the value of R becomes less than 0.0135, the three input switch sends the preset value to keep the system stable. This protection scheme not only protects the system during cyber-attack but also is much faster than manually tuned PID controller (Donepudi, 2015). For LFC neural network tuned PID controller, mitigate frequency disturbance 48% faster than manually tuned PID controller.…”
Section: Solution For Lfcmentioning
confidence: 94%
“…To begin with, workloads must be made having a single virtual machine as the major computing entity, depending on as meager outside data and correspondence as could be expected under the circumstances. Second, new programming models that represent the dynamic and heterogeneous nature, and the massive size of cloud frameworks, need to rise to moderate calculation advancement unpredictability and to keep away from nonstop manual refactoring subject to the unstable fundamental stages and organizations (Donepudi, 2015). For instance, trying to understand mathematical improvement as depicted in Iorio, a successful layering methodology can be highly advantageous.…”
Section: Simulation and Algorithm Scalability In Cloudmentioning
confidence: 99%
“…From this textual portrayal, one can determine automation like Figure 2. Modeling the volume of the opening hole so far by the machine begins in the state "Build" where the volume of the opening is expanded consistently (Donepudi, 2015). When the opening has arrived at the ideal size (the automata needs to make the limit specific), seeking behavior starts.…”
Section: Matingmentioning
confidence: 99%