TY  - JOUR
AU  - G Raju
AU  - Talari Swapna
AU  - Katam Pavani
AU  - Peetla Sai Kumar
AU  - Yadava Sai Charan
AU  - Bannoth Ramesh Naik
PY  - 2026
DA  - 2026/04/25
TI  - Automated Identification And Profiling Of Emerging Cyber Threats Using Natural Language Processing (NLP)
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 6
IS  - 4
AB  - The time window between the disclosure of a new cyber vulnerability and its use by cybercriminals has 
been getting smaller and smaller over time. Recent episodes, such as Log4j vulnerability, exemplifies 
this well. Within hours after the exploit being released, attackers started scanning the internet looking 
for vulnerable hosts to deploy threats like cryptocurrency miners and ransomware on vulnerable 
systems. Thus, it becomes imperative for the cybersecurity defense strategy to detect threats and their 
capabilities as early as possible to maximize the success of prevention actions. Although crucial, 
discovering newthreats is a challenging activity for security analysts due to the immense volume of data 
and information sources to be analyzed for signs that a threat is emerging. In this sense, we present 
a framework for automatic identification and profiling of emerging threats using Twitter messages 
as a source of events and MITRE ATT&CKasasource of knowledge for threat characterization. The 
framework comprises three main parts: identification of cyber threats and their names; profiling the 
identified threat in terms of its intentions or goals by employing two machine learning layers to filter 
and classify tweets; and alarm generation based on the threat’s risk. The main contribution of our 
work is the approachtocharacterize or profile theidentified threats in terms of their intentions or goals, 
providing additional context on the threat and avenues for mitigation. In our experiments, the profiling 
stage reached an F1 score of 77% in correctly profiling discovered threats.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1300
DO  - 10.33425/3066-1226.1300