Anomoly Detection With Machine Learning: What You Need To Know?

The human brain loves to see something amiss; our brains are programmed to just look for the irregularities g. But, anomalies can be the most significant threats that enterprises may encounter when it comes to cybersecurity. 

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Let’s take an example to understand what an anomaly can look like for digital space?

The tweet- “Shoplifters, beware. Japan’s new AI software 

@vaak_inc

 says it can spot potential thieves, even before they steal #リテールテック.”

https://twitter.com/QuickTake/status/1102751999215521794

As per this tweet, Japan has developed an Artificial Intelligence(AI)-based software that analyzes human behavioral patterns and detects anomalies as per the data. These anomalies lead to the detection of the customer’s suspicious behavior, and a shop assistant will ask them if help is needed. If the shoplifter is approached, it has been noticed in most cases that they would simply walk away. 

Similarly, there can be many different types of anomalies like bulk transactions, several login attempts, or even unusual network traffic. In this article, we study how machine learning can help identify anomalies? But, before we do that, let’s understand what an anomaly is in terms of cybersecurity?

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What is an Anomaly? 

Anomalies are often a pattern that is different from standard behavior in a data set. Here is a graphical representation of the data sets. N1 and N2 regions represent standard patterns of data set clusters, while other objects can be deemed anomalies. 

The differentiation between novel patterns or good patterns and anomalies or malicious data sets is the most crucial challenge in modern cybersecurity systems. An anomaly can help attackers leak essential data and even steal user information for manipulations. We have seen many phishing attacks, cyber frauds, identity thefts, and data leaks over the years due to the introduction of malicious or negative patterns in a network or system. 

In July 2020, many celebrities and politicians’ Twitter accounts got hacked. More than 130 Twitter accounts were held hostage by hackers, including Joe Biden, the 46th United States President, Barack Obama, Elon Musk, Bill Gates, Kanye West, Michael Bloomberg, and Apple. 

Types of Anomalies

Anomalies can manifest in various ways, and having a comprehensive understanding of their nature is crucial for developing effective anomoly detection machine learning algorithms. The following are the key types of anomalies that are commonly encountered in anomoly detection:

1. Point Anomalies: Point anomalies refer to individual instances or data points that significantly deviate from the normal behaviour of the dataset. These anomalies can be identified by examining the values or features of individual data points and comparing them to the expected range or distribution. For example, in a network traffic dataset, a point anomaly could be a single connection with an unusually large amount of data transfer compared to the average connections.
2. Contextual Anomalies: Contextual anomalies occur when the deviation from normal behaviour is dependent on the context or specific conditions. These anomalies are detected by considering the contextual information surrounding the data points. For instance, a transaction that is usually considered normal may become anomalous if it occurs at an unusual time or location, given the user’s historical behaviour.
3. Collective Anomalies: Collective anomalies, also known as group anomalies, involve a collection of data points that exhibit anomalous behaviour when considered as a group. These anomalies are not apparent when analyzing individual data points in isolation but become evident when examining the relationships or interactions between them. An example of collective anomalies is a sudden increase in the number of failed login attempts across multiple user accounts, indicating a potential coordinated attack.
4. Temporal Anomalies: Temporal anomalies occur when abnormal behaviour is related to time and sequential patterns. These anomalies are detected by analyzing the time series data and identifying patterns that deviate from the expected temporal order. For instance, in a system log dataset, a temporal anomaly could be a sequence of events that occur in an unusual order or with unexpected time intervals.
5. Statistical Anomalies: Statistical anomalies are identified based on statistical properties and distributions of the data. These anomalies are detected by comparing the statistical characteristics of data points or features to the expected distribution. For example, if the distribution of transaction amounts in a financial dataset follows a normal distribution, any transaction falling significantly outside the expected range may be considered a statistical anomaly.

Common Challenges in Anomaly Detection

Anomaly detection is a complex task that comes with its fair share of challenges. Overcoming these challenges is vital to ensuring accurate and reliable anomaly detection systems. Here are some of the common challenges faced in anomaly detection:

1. Imbalanced Datasets: Anomaly detection often deals with imbalanced datasets, where normal instances vastly outnumber the anomalies. This creates a challenge as standard machine learning algorithms tend to be biased towards the majority class, leading to poor detection performance for the minority class.
2. Lack of Labeled Anomaly Data: Obtaining labelled anomaly data for training supervised anomaly detection models can be challenging. Anomalies are often rare and may require extensive domain expertise to identify and label accurately. The limited availability of labelled data can hinder the development of effective anomaly detection models.
3. Concept Drift and Evolving Attacks: Anomaly detection systems face the challenge of adapting to evolving attack techniques. Attackers continuously modify their strategies, making it necessary to update detection models to detect new types of anomalies and avoid false negatives caused by concept drift.
4. High False Positive Rates: Anomaly detection machine learning algorithms may produce a high number of false positives, flagging normal instances as anomalies. This can lead to alert fatigue and inefficiency in cybersecurity systems, as security analysts spend valuable time investigating false alarms.
5. Interpretability and Explainability: Machine learning algorithms used in anomaly detection, such as deep neural networks, can be highly complex and difficult to interpret. Understanding the reasons behind an anomaly detection decision and providing explanations to stakeholders is crucial, especially in critical domains such as finance and healthcare.

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So, you can understand the importance of anomaly detection in the digital age of BigData. Now that we have a basic understanding of the anomalies, let’s discover some legacy methods and integrations of AI in anomaly detection.

Intrusion Detection System

It is a software tool that helps detect unauthorized access to any network or system; this tool is a great way to detect all types of malicious usage of networks. It has capabilities to help you detect service attacks, data-driven attacks on any software, and even mobile applications. 

Here, you can see the wireframe infrastructure of a generalized intrusion detection system. There are dedicated security officers at the helm of anomaly detection. The software collects all the network packets (Any network data transmitted across devices is done in packets). Next, it analyzes the network flow for the detection of anomalies among novel patterns. 

Machine Learning algorithms can help create more robust intrusion detection systems; we can use machine learning algorithms to analyze network packets and detect anomalies. The algorithms will use novel patterns as a referendum. 

Signature Technique

A signature technique is one of the most popular methods to detect anomalies. It leverages signatures of malicious objects stored in the repositories to compare with network patterns. The system analyzes the network patterns and tries to find malicious signatures. Although it is an excellent technique to detect anomalies, unknown threats, and attacks go undetected. 

Read: Scope of Cyber Security as a career option

Real-Time Anomaly Detection With ML

Machine Learning algorithms can help with real-time anomaly detection. Google cloud uses this method to create an anomaly detection pipeline, where 150 Megabytes of data is ingested in a 10 minutes window. 

The first step towards real-time anomaly detection in this method is to create a synthetic data flow; this helps create a map of triggers for ingesting or aggregation of anomalies in the flow. Whether it’s your wifi at home or an enterprise network at the office, every network has several subnets and subscriber IDs; this method leverages subnets and subscriber ID data. 

The only problem faced here is subscriber ID data usage, as it violates data regulations. As the subscriber IDs contain PII or Personally identifiable information, it can be revealed to the cloud providers during the ingestion or aggregation of data. For these purposes, cloud services use deterministic encryptions. They use crypto decryptions to decrypt the data that does not detect PII. 

As shown here, it is better to use the BigQuery algorithm to analyze large volumes of data as the algorithm can be trained to analyze data in terms of clusters. Data clustering can help partition the different sets of information like subscriber IDs and subnets according to days, dates, or other filters. So, one can quickly help clustering algorithms to learn from data patterns through filtered information. 

The last step is to detect outliers or anomalies among clustered data. An algorithm will need normalized data for the detection of outliers. So, once the data normalization is conducted, the ML algorithm will identify a centroid in each cluster as a reference and measure the center’s distance to the input vector. 

The distance is measured in terms of standard deviations from its novel path and is deemed an outlier accordingly. 

Also Read: Artificial Intelligence in Cyber Security

Anomaly Detection as a Career

With a significantly soaring demand for cybersecurity professionals coupled with the lucrative salaries they offer, a cybersecurity career is becoming one of the most sought-after career options now. If you want to pursue this profession, upGrad and IIIT-B can help you with a Advanced Certificate Programme in Cyber Security . The course offers specialization in application security, cryptography, data secrecy, and network security.

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Conclusion

Advanced technologies like Artificial Intelligence and Machine Learning algorithms are useful in fighting potential cyber threats, and it is a blossoming career path. So, don’t just rely on age-old encryptions or anti-virus software when you can have real-time anomaly detection systems with advanced AI algorithms. These methods make your business more reliable and secure with an AI-based anomaly detection system.