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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Karachi
BEGIN:STANDARD
DTSTART:20091031T230000
TZOFFSETFROM:+0600
TZOFFSETTO:+0500
TZNAME:PKT
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BEGIN:VEVENT
DTSTAMP:20231011T092302Z
UID:B82E7C43-8B64-408B-968B-976BE20624AB
DTSTART;TZID=Asia/Karachi:20231003T140000
DTEND;TZID=Asia/Karachi:20231003T150000
DESCRIPTION:By enabling remote connectivity via the Internet\, industrial c
 ontrol systems have been developed more efficiently. This progress has\, h
 owever\, increased the susceptibility of industrial systems\, as evidenced
  by several cyberattacks within the realm of industrial networks. Moreover
 \, modern industrial protocols have introduced countless vulnerabilities a
 nd attacks on industrial control systems which indicates the importance of
  security mechanisms such as intrusion detection Systems. Multiple techniq
 ues have been developed for an intrusion detection system for industrial c
 ontrol networks. In this research\, we propose an ensemble intrusion detec
 tion system\, where we deploy three different intrusion detection techniqu
 es\, working concurrently and generating alerts in the event of attacks. W
 e use Snort as a signature-based tool to develop a rule set to detect sign
 ature-based attacks. The Kalman filter has been used to detect the ICS sen
 sor and actuator attacks. For the last technique\, we have been using an L
 STM-based autoencoder\, an AI-based anomaly detector\, for real-time syste
 m awareness. This IDS has been tested on data coming from a tank level-con
 trol testbed. The IDS successfully detected all the attacks launched on th
 e testbed.\n\nCo-sponsored by: Cyber-Physical Systems Laboratory\, PIEAS\n
 \nRoom: SE-105\, Bldg: DTD Building\, Cyber Physical Systems Lab\, PIEAS\,
  Islamabada\, Islamabad Capital Territory\, Pakistan
LOCATION:Room: SE-105\, Bldg: DTD Building\, Cyber Physical Systems Lab\, P
 IEAS\, Islamabada\, Islamabad Capital Territory\, Pakistan
ORGANIZER:gm@pieas.edu.pk
SEQUENCE:12
SUMMARY:Intrusion Detection in Industrial Control Networks
URL;VALUE=URI:https://events.vtools.ieee.org/m/378081
X-ALT-DESC:Description: <br /><p>By enabling remote connectivity via the In
 ternet\, industrial control systems have been developed more efficiently. 
 This progress has\, however\, increased the susceptibility of industrial s
 ystems\, as evidenced by several cyberattacks within the realm of industri
 al networks. Moreover\, modern industrial protocols have introduced countl
 ess vulnerabilities and attacks on industrial control systems which indica
 tes the importance of security mechanisms such as intrusion detection Syst
 ems. Multiple techniques have been developed for an intrusion detection sy
 stem for industrial control networks. In this research\, we propose an ens
 emble intrusion detection system\, where we deploy three different intrusi
 on detection techniques\, working concurrently and generating alerts in th
 e event of attacks. We use Snort as a signature-based tool to develop a ru
 le set to detect signature-based attacks. The Kalman filter has been used 
 to detect the ICS sensor and actuator attacks. For the last technique\, we
  have been using an LSTM-based autoencoder\, an AI-based anomaly detector\
 , for real-time system awareness. This IDS has been tested on data coming 
 from a tank level-control testbed. The IDS successfully detected all the a
 ttacks launched on the testbed.</p>
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