Cite this DOI

P, V., & R .S, S. (2021). A Highly Accurate Internal Intrusion Detection and ProtectionSystem Using Time Linked Access Profiles. *Journal of Science & Technology*, *06*(01), 141–147. https://doi.org/10.46243/jst.2021.v6.i1.pp141-147

@article{p2021highly,
  author    = {P, Veena and R .S, SyamDev},
  title     = {{A Highly Accurate Internal Intrusion Detection and ProtectionSystem Using Time Linked Access Profiles}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {jan},
  volume    = {06},
  number    = {01},
  pages     = {141--147},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i1.pp141-147},
  url       = {https://doi.org/10.46243/jst.2021.v6.i1.pp141-147},
  language  = {en},
  abstract  = {Because most intrusion detection systems and firewalls identify and separate malicious traits that only come from the externalenvironment of the system. It is difficult to differentiate between the actual system users, the internal attackers who access the device.Also, studies claim that these commands can be recognized by analyzing the system calls produced by these commands.This system, therefore, includes an Intrusion Detection and Protection System (IDPS) security system to detect internal attacks on the System Call (SC)using data mining and forensic techniques.However, some attacks have improved their method, further providing security and preventing the user from tracking user access profiles and patterns. In this work,a new technique is proposed to prevents profile attacks by disconnecting access patterns from users for a specified period based onTime Linked Access Profiles (TLAP).To evaluate the performance detection accuracy is calculated and it is improved when compared to the convolution neural network.}
}

TY  - JOUR
TI  - A Highly Accurate Internal Intrusion Detection and ProtectionSystem Using Time Linked Access Profiles
AU  - P, Veena
AU  - R .S, SyamDev
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/01/08/
VL  - 06
IS  - 01
SP  - 141
EP  - 147
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - Because most intrusion detection systems and firewalls identify and separate malicious traits that only come from the externalenvironment of the system. It is difficult to differentiate between the actual system users, the internal attackers who access the device.Also, studies claim that these commands can be recognized by analyzing the system calls produced by these commands.This system, therefore, includes an Intrusion Detection and Protection System (IDPS) security system to detect internal attacks on the System Call (SC)using data mining and forensic techniques.However, some attacks have improved their method, further providing security and preventing the user from tracking user access profiles and patterns. In this work,a new technique is proposed to prevents profile attacks by disconnecting access patterns from users for a specified period based onTime Linked Access Profiles (TLAP).To evaluate the performance detection accuracy is calculated and it is improved when compared to the convolution neural network.
DO  - 10.46243/jst.2021.v6.i1.pp141-147
UR  - https://doi.org/10.46243/jst.2021.v6.i1.pp141-147
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i1.pp141-147",
    "title": "A Highly Accurate Internal Intrusion Detection and ProtectionSystem Using Time Linked Access Profiles",
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    "language": "en",
    "abstract": "Because most intrusion detection systems and firewalls identify and separate malicious traits that only come from the externalenvironment of the system. It is difficult to differentiate between the actual system users, the internal attackers who access the device.Also, studies claim that these commands can be recognized by analyzing the system calls produced by these commands.This system, therefore, includes an Intrusion Detection and Protection System (IDPS) security system to detect internal attacks on the System Call (SC)using data mining and forensic techniques.However, some attacks have improved their method, further providing security and preventing the user from tracking user access profiles and patterns. In this work,a new technique is proposed to prevents profile attacks by disconnecting access patterns from users for a specified period based onTime Linked Access Profiles (TLAP).To evaluate the performance detection accuracy is calculated and it is improved when compared to the convolution neural network.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i1.pp141-147 gives all four in one JSON answer.