Phddissertation
Uploaded on Sep 23, 2019
Category
Education
1.AI is promising in the of detecting intrusions are cross-site scripting attacks, SQL injections, Denial of Service (DoS), Ransomware, etc. 2.Deep learning models able to accurately predict and recognize “normal activity” or malicious activity faster and generate a much lower percentage of false positives 3.Real-time IDS systems must not have a single fixed algorithm instead the algorithms must be updated regularly
Category
Education
Tips for developing Academically sound IDS Models and algorithms for Your IEEE Publication 2019
SEP 23, 2019
MACHINE LEARNING MODELS
FOR INTRUSION DETECTION
SYSTEMS(IDS)
Tips for developing academically sound IDS
models and algorithms for Your IEEE Publication
2019
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TOPICS TO BE DISCUSSED
About Us
Intrusion Detection System
Types Of IDS Techniques
Pre-Processing IDS Data
IDS classification
Summary
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PhD Lab @
PhD Assistance
Engineering and Technology:
E&T Lab at PhD Assistance is involved in exploring novel research
areas by conducting dynamic research. It promotes innovation in all
fields of engineering by advancing the technology with structured
and continuous research. The problems and challenges faced by the
existing technologies and trends are explored by our researchers
exists in scholarly literature, in theory, or in practices that needs
deliberate investigation. These problems are identified and fixed by
our researchers by suggesting better novel alternatives with
appropriate tools, technologies and approaches, thereby proving
their effectiveness in real time applications.
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INTRUSION
Unauthorised activities on
the organisation's local
computer network
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Ransomware
Denial of
Service
(DoS) MOST COMMON
SQL INTRUSIONS
injections
Cross-site
scripting
attacks
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Monitors
network
traffic for
suspicious
activity and
issues alerts.
Scans a
network or INTRUSION It varies from
a system for a single
harmful DETECTION computer to
activity. large SYSTEM network.
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Creates a
log
IPS detect the INTRUSION
Blocks IP
intrusion & PREVENTION address source
prevent them
SYSTEM
Restrict the access of
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TYPES OF IDS TECHNIQUES
Network based
(NIDS)
Host based
intrusion
(HIDS)
Vulnerability
assessment-
based
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PRE-PROCESSING IDS DATA
Performed through data set(KDD’99,
DARPA dataset)
Dataset size should be huge & balanced
Prevent intrusions in real-time traffic
Easier to categorize, reduce operation
time and improves classification
performance and accuracy.
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Statistical anomaly
detection algorithms
Machine learning (ML)
algorithm [Artificial Neural
Networks]
Knowledge-based detection
Data mining Algorithms
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IDS CLASSIFICATION
SUMMARY
Building an IDS is
extremely challenging
A constant update will
improve the accuracy and
reduce the error rates.
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PhD Lab @
PhD
Assistance
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