The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …

Utilizing social media data is imperative in comprehending critical insights on the Russia–
Ukraine cyber conflict due to their unparalleled capacity to provide real-time information …

Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) are a class of Recurrent
Neural Networks (RNN) suitable for sequential data processing. Bidirectional LSTM …

Cyber threats are projected to cause USD 10.5 trillion in damage to the global economy in
2025. Comprehending the level of threat is core to adjusting cyber posture at the personal …

Cybersecurity researchers have contributed to the automated extraction of CTI from textual
sources, such as threat reports and online articles describing cyberattack strategies …

As various types of network threats have increased recently, manual threat response by
security analysts has become a limitation. To compensate for this, the importance of security …

There is an urgent need in many critical infrastructure sectors, including the energy sector,
for attaining detailed insights into cybersecurity features and compliance with cybersecurity …

Since the onset of the COVID-19 crisis, scholarly investigations and policy formulation have
harnessed the potent capabilities of artificial intelligence (AI)-driven social media analytics …

Time-relevant and accurate threat information from public domains are essential for cyber
security. In a constantly evolving threat landscape, such information assists security …

Purpose Text classification is a widely accepted and adopted technique in organizations to
mine and analyze unstructured and semi-structured data. With advancement of …