Anomaly Detection Software on the Ground

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Updated: Oct 17, 2021
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Anomaly Detection Software on the Ground essay

In today’s world, business and informational organizations have never been more reliant on software systems for their daily operations and computing tasks. Every day, computer users discover things out of the ordinary, or deviations, that could potentially harm their organization’s security. It could be something as small as a miscalculation or something more serious, like malware. On top of the varying threats, the users and the systems are often tasked with clashing objectives, like increasing productivity while decreasing time and cost. In some businesses, the use of multiple systems has been utilized to address some of these concerns, but it risks increasing the stress level of the users. All of these points are considered vulnerabilities, where a threat could strike and potentially corrupt the system and cripple the organization.

Data security threats can be unpredictable, but one way that an organization can prevent such a catastrophe is through utilization of an anomaly detection and prediction system. It is a cognitive computing system that identifies points and events that deviate from an expected pattern, normal baseline, or dataset. It can quickly recognize and identify outliers in expected patterns or behaviors within a network. It helps to detect intrusions in the network traffic, fraudulent transactions, and monitors unusual activities. It is available in the forms of plug-in toolkits, computer software, and online or cloud-based applications.

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The cognitive systems can employ many anomaly detection techniques, ranging from simple statistic to advanced autoencoding. Recent advancements in anomaly detection include development of new techniques in the fields of neural networks and deep learning. These new techniques are expected to improve anomaly detection in more complex and large-scale systems, such as aviation systems, but they can also help smaller businesses and organizations due to better adaptability compared to conventional machine learning methods. If used well, anomaly detection cognitive systems and its advancements could help any company to reduce time and money spent on the remediation of future mistakes.

In light of the world’s current situations, anomaly detection systems could help healthcare workers reduce the stress load on all fronts in the fight against a viral pandemic. It can monitor health systems to detect mistakes, duplication, or negligence. In another use of anomaly detection systems, it could be used to monitor traffic cameras. This way, it can alert local authorities of accidents, natural disasters, or riots as they take place so that actions could be taken more swiftly and efficiently.

In my experience, I was introduced to anomaly detection technology by a company that I worked with in the past. It has influenced me to always be aware of all the processes that are taking place in an organizational setting. It taught me about the weak link in the chain, and how to direct the anomaly detection software to identify faults in a system.

In conclusion, Anomaly detection is a brilliant, readily available technology that should be utilized more widely in today’s world. It could help reduce the time, money, and stress that incurs upon mistakes that are waiting to happen in any organization or business.

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Anomaly Detection Software on the Ground. (2021, Oct 17). Retrieved from