000			01352nam a22001697a 4500
003			NUST
082			_a005.8,JAV
100			_aJaved, Muhammad Awais _9133637
245			_aDomain Generating Algorithm (DGA) Malware Detection using Deep Learning Model / _cMuhammad Awais Javed
260			_aRawalpindi, _bMCS (NUST), _c2026
300			_axix, 139 p
505			_aAdvanced 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.
650			_aPhD Information Security Thesis _9132793
651			_aPhD IS Thesis _9132794
700			_aSupervised by Dr. Imran Rashid _9132473
942			_2ddc _cTHE
999			_c616600 _d616600