<![CDATA[Intrusion Detection Systems (IDS) are crucial for protecting network infrastructures from advanced cyber threats. Traditional IDS, largely reliant on static signature detection, fail to effectively counter novel cyber attacks, leading to high false positive rates and missed zero-day exploits. This study investigates the integration of deep learning technologies into IDS to enhance their detection capabilities. By employing advanced deep learning frameworks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and other algorithms , the research explores their efficacy in identifying complex data patterns and anomalies. Furthermore, the use of big data analytics is assessed for its potential to significantly augment the predictive power of these systems, aiming to set new benchmarks in cybersecurity defenses tailored for contemporary threats.]]>
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