NEXT-GENERATION FIREWALLS: ADVANCING NETWORK SECURITY TO COMBAT EVOLVING AND SOPHISTICATED CYBER THREATS

Security Network Engineer

In the realm of traditional firewalls, they are simply limited to basic packet filtering and rule-based security; hence, they would be ineffective if faced with more advanced cyber threats such as ransomware, malware, and encrypted attacks. old methods keep failing in real-time detection of threats because they lack deep packet inspection and because they can't make a distinction between legitimate and malicious applications-they left a security gap. A Next-Generation Firewall (NGFW) framework MLP-LSTM-based is suggested in this paper to address these issues. It integrates Multi-Layer Perceptron (MLP) to extract features and Long Short-Term Memory (LSTM) to learn the sequential pattern, thus greatly improving intrusion detection. Using deep learning techniques will indeed increase anomaly detection accuracy and decrease false positives. The datasets are complete, i.e., CICIDS2017 and UNSW-NB15, which build a model that is trained and evaluated for robustness in the detection of a range of cyber threats including DDoS, phishing, and zero-day exploits. The promised NGFW framework glorifies the practicality of detection accuracy at a high level and a proper approach to adaptive response to threats as against traditional firewalls. The experimental results give convincing evidence that the model under the proposal is fine-tuning and enhanced defence against cyber threats, which is worthy of consideration by modern-day network protection solutions.

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