@Article{jcs.2023.040186,
AUTHOR = {Jalaj Pateria, Laxmi Ahuja, Subhranil Som},
TITLE = {Seeded Transfer Learning for Enhanced Attack Trace and Effective Deception},
JOURNAL = {Journal of Cyber Security},
VOLUME = {4},
YEAR = {2022},
NUMBER = {4},
PAGES = {223--238},
URL = {http://www.techscience.com/JCS/v4n4/53754},
ISSN = {2579-0064},
ABSTRACT = {Cyberattacks have reached their peak during COVID-19, and
intruders urge to gain the upper hand in the cybersecurity battlefield, even
gaining dominance. Now intruders are trying harder to elude behavior
analysis techniques, which in turn gets organization security to come for a
toss. This phenomenon is even more prevalent in agentless environments (IOT
devices, mobile devices), where we do not have any access to edge devices and
rely on packet data to predict any attack and its actors. In this paper, we
shall be discussing enhancing the accuracy of anomalous behavior detection
techniques for efficient threat intelligence and revamping deception using a
unique machine learning model training technique termed “Seeded Transfer
Learning”, in this technique data is reshaped into knowledge to fit to the target
domain in small capsules of information in real-time or near real-time. In this
method we shall be using seeds of data or real-time small data sequences to
train machine learning models along with that sustaining on the ideology of
positive transfer learning techniques where previous learning will be made
more effective by taking advantage of a new training set. Comprehensive
experiments are done on ANN-(Artificial Neural Network) and result revels
best incremental performance from ∼91% to ∼97%. Experiment summarized
that while training model on data seeds we have achieved great accuracy with
limited computing resources and time, additionally model is trained on latest
attack dataset which helped in identifying attacks effectively which in turns
translates to effective defense against future unknow attacks.},
DOI = {10.32604/jcs.2023.040186}
}