Anomaly Detection in Cyber Network Security

Introduction to Cyber Network Security

It is becoming difficult to identify Cybersecurity attacks. These attacks can originate internally due to malicious intent or negligent actions or externally by malware, target attacks, and APT (Advanced Persistent Threats). But insider threats are more challenging and can cause more damage than external threats because they have already entered the network. These activities present unknown threats and can steal, destroy or alter the assets or enterprises. Hence this is a serious concern for the enterprises to enable Anomaly Detection for Cyber Network Security.

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Earlier firewalls, web gateways, and some other intrusion prevention tools are enough to be secure, but now hackers and cyber attackers can bypass approximately all these defense systems. Therefore with making these prevention systems strong, it is also equally essential to use detection. So that if hackers get into the network, the system should be able to detect their presence.

What are the solutions for Anomaly Detection?

Behavior Anomaly detection provides real-time detection of cyber attack threats. It monitors anomalous user behavior that protects the enterprises against threats. Activities are detected that are anomalous to the standard user behavior. Its techniques are essential for cyber network security. It can help to notice unusual behavior. It can detect and prevent theft of data or Intellectual Property (IP). User Behaviour, an solution, can quickly identify when a user behaves abnormally and then can take appropriate action to limit what they can do or alert the situation for managerial attention. It can predict when anomalous behavior is likely to occur and identify. It is easy for attackers to steal credentials but very difficult to act like the user whose credentials they stole.

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Use-Case of Anomaly Detection for Cyber Network Security

Some of the cases where it is necessary to use Use behavior Anomaly detection:

Detecting Anomalous log-on Patterns

In an organization, employees commonly need to travel to different multi-country places for their office work. These employees have access to the company's Intellectual property that can be confidential such as pricing and competitive insights. They need to access these all from different countries through different devices such as laptops, mobiles, or other public systems(hostel systems); therefore, the system's security is always not fully controlled by the organization. It is a challenge for the existing security systems to detect whether an authorized person is accessing the data or an attacker. The attacker can steal the credentials via social or targeted malware. To detect the attacker, it is required to analyze user behavior because abnormal user behavior can help detect that the person accessing the assets is an attacker.

For example, it is possible to detect that an employee appears to have traveled an impossible distance between log-on attempts or accessing the data that they have never used before. If an anomaly like this is flagged up, action can be taken immediately, such as suspending user access rights immediately.

Network Intrusion Detection

It is tough to detect threats in large heterogeneous environments, especially covert threats. An organization having complex global nature, absolute perimeter defense is not possible. By building user behavior-based anomaly detection, attacks can be predicted when the network has been compromised. Network Intrusion Detection can predict the risks of advanced threats inside the organization.

Abnormal finance Activities Detection

Financial activities and assets are essential for any organization. Most of the organizations give financial authority only to selected employees. But sometimes, these selected employees also can be compromised. There can be several decisions, such as via social manipulation and blackmail or for personal gain. Therefore organizations need the way to detect and react quickly to these abnormal situations. It is difficult to detect the employees' abnormal behavior, but Machine Learning can be used for behavior-based anomaly detection. Analyze the historical and current data and detected anomalies using ML. It can detect fraud in multiple centers by seemingly unconnected employee groups or take appropriate precautions when a cluster of employees has had poor appraisals.

Advanced Penetration Detection

Organizations use various techniques to stop attackers but still gain access to key user accounts in the network, thus compromising the landing point. Attackers can use this to move and use the organization's network. These movements can generate anomalous network traffic and give system access. Thus after getting into the network and having access, the attacker can use and control its assets. As the attacker uses correct credentials, it becomes complex to detect the attack for classic defense systems. To tackle the problem, organizations can monitor their logs and networks and track user behavior. Use the ML technique to detect any change in the behavior of the user. Detect any unusual activities by comparing the previous and current behavior.

Protecting Web-based Business

Many of the businesses are going online and becoming web-based. But these web base models are highly reliable on the platforms, and DoS ( Denial-of-Service) attacks on these platforms are increasing. This can impact the business a lot. Detect these attacks by analyzing network traffic and thus minimize these attacks.

What are its benefits?

User behavior Anomaly prediction systems make the security systems more precise and accurate. It can help make sense of varied information provided by security systems and identify potential risks. Some of its benefits:

Behaviour Anomaly detection can do real-time detection of cyber threats.
Remove operational error by reducing operational risks.
Track anomalous activities in the network.
It minimizes the time and labor involved in identifying and resolving threats.
Detecting the presence of hackers quickly reduces the damage.

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Conclusion

The trend of cybersecurity and protection against various types of cyber-attacks has been ever rising. The main reason is the Internet-of-Things (IoT), the rapid growth of computer networks, and the various other relevant applications that individuals or groups use for personal or commercial use. Therefore, removing the gap between identifying anomalies and transforming them into actionable data Anomaly Detections is necessary for Cyber Network Security.