An Anomaly also known as an outlier, abnormalities, or rare event, is a data object that deviates from other objects in the dataset and does not conform to the normal behaviour in a dataset [1]. Therefore, when data suddenly deviates from the predefined dataset, this can be an alert flag of system faults, attacks, or exploitable vulnerabilities. Although data anomalies may not necessarily always indicate a threat, it is important to investigate each one of them in order to have a better understanding of why a deviation happened and whether or not the anomaly in question is a legitimate point within the dataset.
Anomaly detection is the process of identifying data points, events, or observations that deviate from the dataset’s normal behaviour patterns, i.e., the process of finding outliers in a given dataset. In general, because of the massive volume of data, it is difficult for organizations to manually monitor all of the inputs and outputs that are stored in or moving through their systems. This is a challenge that may be overcome, however, by using automation. A growing number of firms are incorporating anomaly detection into their existing security protocols in order to improve their ability to identify and respond to potential threats.
The process of anomaly detection using deep learning makes it possible for security teams to become aware of previously undetectable events or data points that indicate a statistically significant deviation from normal operating patterns. In many cases, teams require real-time data monitoring capabilities in order to respond to data deviations and maybe stop an attack, detect fraud, or monitor the health of the system. The identification of anomalous data points assists teams in discovering the root of security incidents as quickly as possible. Anomaly detection is necessary for businesses in order to evaluate security threats, identify security incidents, and improve their overall security to prevent data leakage. It can help in many different areas, such as blockchain, IT analytics, network intrusion analytics, preventing financial fraud, and many more [2].
Methods for detecting anomalies can be broken down further according to the kinds of training data they demand. It is anticipated, in the vast majority of applications, that outlier samples make up a relatively insignificant portion of the entire dataset. Therefore, normal data samples are easier to obtain than anomalous data samples, even when labeled data is available. This is the case whether or not the data is being labeled.
Supervised Learning
For supervised anomaly detection, it is necessary to train a model using data that has already been labeled. These datasets contain prepared samples of normal data as well as examples of accurately defined outliers. This is likely to make an anomaly detection system effective at finding abnormalities in data that were expected. It will not, however, account for abnormalities that security teams cannot predict or have never encountered before. In addition, the majority of labeled datasets do not contain enough outlier data to train the system effectively.
Unsupervised learning
Unsupervised anomaly detection is the approach of choice when trying to define system baselines because the majority of businesses do not have access to pre-labeled data. Security teams have the choice of either feeding the algorithm with unlabeled data sets and enabling the system to identify whether the data constitute outliers or allowing the algorithm to evolve naturally by monitoring a system operating in its natural environment.
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References:
[1] Deep Learning for Anomaly Detection, https://ff12.fastforwardlabs.com/
[2] Varun Chandola et al. (2009), “Anomaly Detection, A Survey” , ACM Computing Surveys 41(3), https://dl.acm.org/doi/10.1145/1541880.1541882