In this era of fast-paced technological advancement, protecting computer networks from malicious invaders is paramount to the integrity of any establishment. One of the sophisticated ways of preventing such threats is through Anomaly Detection in Network Traffic, a process often referred to as Network Anomaly Detection (NAD).

Network Anomaly Detection is a strategy employed to detect unusual patterns that do not conform to expected network activity. By utilizing machine learning and artificial intelligence, anomalies that could indicate potential threats or breaches are spotted and acted upon – all in real-time. These abnormalities may include brute force attacks, Denial of Service (DoS) attacks, port scans, or even the unauthorized exportation of data.

- **Automated Identification:** Network Anomaly Detection involves automated identification of unusual networking activity by comparing current traffic data against a baseline of normal network behavior. - **Real-time Monitoring:** A constant and active network monitor is a central feature of NAD, identifying and reporting anomalies as they arise. - **Data-driven:** Network traffic analysis and anomaly detection are driven by robust data analytics capabilities. - **Scalability:** This solution scales as an organization's network expands, providing seamless protection across the growing network environment. - **Multiple Detection Techniques:** Embraces a combination of statistical, machine learning, and artificial intelligence techniques to thoroughly analyze network traffic data.

- **Reduced Risk:** Network Anomaly Detection significantly lowers the risk of experiencing a costly and damaging network breach. By identifying unusual network behavior early, it's possible to neutralize threats quicker, thus limiting potential damages. - **Early Detection:** The real-time alert system engenders timely identification and neutralization of network threats, even before they cause a substantial problem. - **Smart Analysis:** The use of machine learning allows for intelligent analysis of traffic data, which becomes increasingly effective at spotting anomalies as it evolves. - **Flexibility:** Network Anomaly Detection is versatile enough to cope with changes to network environments, making real-time adjustments as an organization's network evolves. - **Scalable:** These network protection solutions scale as a company's network expands, adaptable to changing sizes and increasing traffic. - **Aligning Normal Baselines:** Defining what constitutes normal traffic can be challenging. If normal activities aren't accurately represented, it could result in missed anomalies or false alarms. - **False Positives:** An overly sensitive system may generate too many false positive alerts, requiring unnecessary resources for investigation. - **Delayed Response:** Some anomalies might need a reasonable time to be detected, potentially opening a window for attacks. - **Dependence on Vendor:** Relying on the software vendor for updates, maintenance, and improvements comes with the risk of experiencing unanticipated issues.

Implementing the NAD system requires an in-depth understanding of the network environment and its typical traffic patterns. Building an extensive log of "normal" activities is essential in reducing false alarms. Post identifying the potential anomalies, a decision-making process should be in place to evaluate them and decide on the best courses of action. One of the significant steps of such an implementation involves training the system to ensure its efficiency.

In conclusion, while Network Anomaly Detection may not be without challenges, its benefits far outweigh its drawbacks. By implementing this solution and proactively spotting potential threats, organizations can safeguard against malicious attacks, demonstrating that prevention is indeed better than cure in network security. Careful planning, prudent evaluation and training are the keys to a successful Network Anomaly Detection implementation.