NUST Institutions Library Catalogue

Domain Generating Algorithm (DGA) Malware Detection using Deep Learning Model / Muhammad Awais Javed

By: Javed, Muhammad AwaisContributor(s): Supervised by Dr. Imran RashidMaterial type: TextTextPublisher: Rawalpindi, MCS (NUST), 2026Description: xix, 139 pSubject(s): PhD Information Security Thesis | PhD IS ThesisDDC classification: 005.8,JAV
Contents:
Advanced Persistent Threats (APTs) represent one of the most captious challenges to modern cybersecurity paradigm, particularly for critical information infrastructures (CIIs), governments and military networks. These threats employ stealthy and persistent attack strategies that evade conventional defenses, with Domain Generating Algorithms (DGAs) serving as a core technique to ensure uninterrupted communication with Command and Control (C&C) servers. DGAs are capable of dynamically generating large volumes of domain names, making blacklist-based detection techniques ineffective and forcing security systems to operate reactively rather than proactively. The inherent difficulty in identifying malicious domains generated by DGAs necessitates the exploration of intelligent, adaptive detection mechanisms.
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Item type Current location Home library Shelving location Call number Status Date due Barcode Item holds
Thesis Thesis Military College of Signals (MCS)
Military College of Signals (MCS)
Thesis 005.8,JAV (Browse shelf) Available MCSPhD IS-18
Total holds: 0

Advanced Persistent Threats (APTs) represent one of the most captious
challenges to modern cybersecurity paradigm, particularly for
critical information infrastructures (CIIs), governments and military
networks. These threats employ stealthy and persistent attack strategies
that evade conventional defenses, with Domain Generating Algorithms
(DGAs) serving as a core technique to ensure uninterrupted
communication with Command and Control (C&C) servers. DGAs
are capable of dynamically generating large volumes of domain names,
making blacklist-based detection techniques ineffective and forcing security
systems to operate reactively rather than proactively. The inherent
difficulty in identifying malicious domains generated by DGAs
necessitates the exploration of intelligent, adaptive detection mechanisms.

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