[HTML][HTML] Leveraging ai for network threat detection—a conceptual overview
Network forensics is commonly used to identify and analyse evidence of any illegal or
unauthorised activity in a given network. The collected information can be used for …
unauthorised activity in a given network. The collected information can be used for …
Explainable AI-based innovative hybrid ensemble model for intrusion detection
U Ahmed, Z Jiangbin, A Almogren, S Khan… - Journal of Cloud …, 2024 - Springer
Cybersecurity threats have become more worldly, demanding advanced detection
mechanisms with the exponential growth in digital data and network services. Intrusion …
mechanisms with the exponential growth in digital data and network services. Intrusion …
[PDF][PDF] Adaptive Ensemble Learning Framework for Robust Intrusion Detection in Wireless Sensor Networks
V Veeramachaneni - Journal of Advancement in Parallel …, 2025 - researchgate.net
In recent years, the security of Wireless Sensor Networks (WSNs) has faced significant
challenges due to their susceptibility to various cyber threats. This study introduces an …
challenges due to their susceptibility to various cyber threats. This study introduces an …
Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection
There are serious security issues with the quick growth of IoT devices, which are
increasingly essential to Industry 4.0. These gadgets frequently function in challenging …
increasingly essential to Industry 4.0. These gadgets frequently function in challenging …
Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes
massive volumes of high-dimensional data, presenting significant data and privacy security …
massive volumes of high-dimensional data, presenting significant data and privacy security …
Cyberattack event logs classification using deep learning with semantic feature analysis
Event logs play a crucial role in cybersecurity by detecting potentially malicious network
activities and preventing data loss or theft. Previous work did not place a high value on log …
activities and preventing data loss or theft. Previous work did not place a high value on log …
A Machine Learning Sentiment Analysis Approach on News Headlines to Evaluate the Performance of the Pakistani Government
The growing amount of unstructured online data presents challenges in efficiently
organizing and summarizing relevant information, hindering knowledge development and …
organizing and summarizing relevant information, hindering knowledge development and …
A Comprehensive Approach to Intrusion Detection in IoT Environments Using Hybrid Feature Selection and Multi-Stage Classification Techniques
The rapid expansion of Internet of Things (IoT) devices has led to an increasingly complex
threat landscape, challenging traditional Intrusion Detection Systems (IDS) to effectively …
threat landscape, challenging traditional Intrusion Detection Systems (IDS) to effectively …
[HTML][HTML] Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification
DK Roy, HK Kalita - Journal of Cybersecurity and Privacy, 2025 - mdpi.com
Intrusion detection has been a vast-surveyed topic for many decades as network attacks are
tremendously growing. This has heightened the need for security in networks as web-based …
tremendously growing. This has heightened the need for security in networks as web-based …
A Deep Learning-Driven Approach for Detecting Lung and Colon Cancer Using Pre-Trained Neural Networks
Lung and colon cancers are two of the most common and deadliest cancers worldwide,
claiming countless lives each year. The ability to diagnose these cancers early can make a …
claiming countless lives each year. The ability to diagnose these cancers early can make a …