| dc.description.abstract | Traditional firewall systems remain the building blocks of network security, but they
are proving to be increasingly ineffective against dynamic and sophisticated types of
cyberattacks. This weakness is caused by the fact that they use static rule based filtering
processes which are pre-defined. This paper presents a concept of the brain of the
firewall, which suggests a gradual incorporation of artificial intelligence (AI) into the
already established firewall systems. It aims to transform these systems into active,
self-learning, and versatile defence systems. The AI component, serving as the "brain"
of the system, can monitor big amounts of traffic within the network to detect minor
anomalies and predict possible attack by the time it happens, thus avoiding significant
harm, before it can happen. This is a machine-learning-based solution which will
use historical network information and known patterns of user behavior to identify
suspicious behavior automatically and correctly. Such activities involve unauthorized
access, data exfiltration, and covert malware communications, thus providing solutions
to threats that go beyond the face of conventional firewalls. The system can also
read the more intricate security alerts, correlate information among multiple sources,
and take autonomous and informed decisions to isolate malicious traffic due to the
intelligent combination of Natural Language Processing and detailed threat intelligence
feeds. Most importantly, the system constantly evolves and tunes its defenses depending
on past incidents, which improves its overall capabilities without human intervention.
The brain of the firewall offers unmatched efficiency, a substantial reduction in false
positive, and must-have real-time flexibility to emerging threats. Finally, this research
shows an active paradigm shift in cybersecurity, which basically involves the integration
of old methods of security with new intelligent automation to enhance organizational
security. | en_US |
