Cybercrimes are growing at an exponential level. According to eSentire, the cost of cybercrime will increase to $8 trillion in 2023, which is predicted to grow to $11 trillion by 2025.
Threats are becoming quite sophisticated, and traditional measures are no longer effective in preventing ransomware and social engineering attacks. So, there is a need to leverage advanced threat detection methods.
You can’t overlook threat identification because if you do, you will have to pay the high breach cost.
What are the Three Main Detection Types? Explained
Attackers are evolving day by day. They know all about basic detection programs and find a way around them. They create new malware or new version of old malware to create a zero-day for your enterprise. Whether your organization is big or small, you are vulnerable. If you want to detect a threat, you must complete the ecosystem.
Today, when discussing detection, it’s not about what method you employ to spot the vulnerability. But it is based more on which data you use for threat intelligence. There is a need for your team to have 360 visibility into all networks, clouds, and endpoints, so there is no blindspot for finding suspicious activities.
What are the Three Main Detection Types? Let’s Uncover
Here are the three most common methods the IT team uses these days to prevent cyber threats.
Signature-Based DetectionIt is a method in which a signature database is used to pinpoint malware. When a program relies on this technique, it will detect malware through unique identifiers. It could be a specified code of string, hash of known malware, etc.
In simple words, known malware has specific codes, hashes, names of files, etc. The system scans the file and apps and compares them with known malware. If a match occurs, then malware is removed, and it’s how you can stop an attack.
It is a traditional method that is employed in most antivirus programs. This method works like magic when preventing attacks on the network and endpoint.
Thankfully, there are vast libraries that make it easy for threat hunters to cross-reference malware indicators. This technique secures your organization from a known attack.
The biggest issue with this method is that it won’t allow you to spot unknown threats. This method becomes ineffective if an attacker employs a new variation of old malware or polymorphic viruses.
Behavior AnalysisIt is another primary type of threat detection. A program with a behavior analysis tool, such as an EDR, helps you identify abnormal behavior that may or may not be a malicious attack on your endpoint.
This system creates baseline data by continuously looking at the endpoint user activities. For example, it records when users log in, what actions they perform, or what privileges they have. Besides, it will also keep location data stored.
Once an intruder tries to cause a security breach, the system alerts security analysts who compare standard baseline data with nonstandard actions. For example, suppose an endpoint is accessed commonly by its user in New York, and someone tries to access it from Japan. In that case, it’s an unusual activity - given that the user doesn’t travel there or has never visited this location before.
Although it is the most helpful type of detecting threats, it’s still ineffective. It requires your team to create regular baseline updates. User behavior changes constantly, and if a system doesn’t get an update, then information from this program is useless. If you need a program with this method, it should automatically create a complete behavior data baseline.
Machine LearningIt would be best to rely on this detection type to become efficient with your cybersecurity approach. The practice leverages extensive structured data from the network to identify services, cloud, and endpoints. ML-based programs supervise all the data and offer visibility to your security analysis.
For example, an EDR is based on a machine-learning technique. It allows your team to monitor all endpoints and keep their activity data stored continuously. It offers insight into a host endpoint and lets them perform analysis of datasets.
This method alone doesn’t work great but offers insight, but when it comes to prioritizing alerts and getting to know a threat, you need context and data from other programs such as SIEM.
If your security program is based on machine learning, it should offer an easy-to-understand mathematical output so an analyst can consume it. In other words, data and information from the tool should be used for further investigation.
What are the Three main detection types? Key Takeaway
Cyber threats are rising exponentially, and if you want to prevent sophisticated attacks such as ransomware on your organization, you should understand all three main detection types. Signature-based type helps deal with known threats, and you can easily spot unknown attacks through Machine learning and behavior analysis technique-based programs such as XcitiumEDR.
