What are the Ransomware Detection Techniques?
Ransomware is a type of malware that is put on a victim's device or devices with the goal of stealing and/or encrypting sensitive information. A ransom must be paid in order for a victim to recover access to their data and systems, as the term implies. When a victim of ransomware is confronted with two alternatives, they must either rebuild their systems from the ground up, risking the attacker leaking their data online, or paying the ransom.
Ransomware attackers use a variety of techniques to obtain access to a victim's database. However, social engineering techniques such as phishing emails are one of the most popular. Cybercriminals can make these emails appear to be from reputable sources, deceiving users into downloading malicious software onto their devices.
When ransomware infects a company's computer system, it may inflict significant harm to the company's financial line and public impression. When security professionals discover ransom requests, the harm has already been done. In the fight against ransomware, prevention is crucial.
Like other malware, ransomware is meant to infect a computer and remain undiscovered until its goal is met. The attacker's objective with ransomware is for the victim to be unaware of the infection until they get the ransom demand.
Anti-ransomware software is intended to detect ransomware infections early in the process, maybe before any harm is done. They achieve this by employing a range of ransomware detection approaches to circumvent ransomware's stealth and defensive evasion capabilities.
The detection of ransomware is the first line of protection against harmful software. Ransomware remains concealed in an infected machine until data are blocked or encrypted. In this article, we are going to briefly explain some of the detection techniques for Ransomwares under the following headings:
Detection By Signature
Detection By Data Behavior
Detection By Abnormal Traffic
Figure 1. What are the Ransomware Detection Techniques?
1. Detection By Signature
Malware has a unique signature made up of information such as domain names, IP addresses, and other identifiers. Signature-based detection compares active files on a system to a library of these signatures. This is the most basic approach to malware detection, however, it is not always effective.
For each attack, ransomware perpetrators may develop fresh copies of malware with new signatures. Malware detection relying on signatures cannot detect what it does not recognize. As a result, computers are vulnerable to each new malware type.
Signature-based ransomware detection compares the hash of a ransomware sample to known signatures. It performs fast static analysis of files in a given context. Data from within an executable can be captured by security platforms and antivirus software to evaluate the chance that it is ransomware vs an authorized executable. This is a step that most antivirus software does while scanning for dangerous malware.
To get a file's hash, security teams can utilize the
Windows PowerShell cmdlet Get-FileHash or open-source intelligence tools such as
VirusTotal. Security professionals may use current hashing methods to compare a file's hash to known malware samples.
Signature-based detection, on the other hand, is becoming less and less helpful. Signature-based detection has never been effective against new malware since no signatures for the malware strain have been established. Today, ransomware gangs frequently employ new versions of their malware (with different file hashes, command and control infrastructure, and so on) for each attack campaign, rendering signature-based detection worthless.
2. Detection By Data Behavior
Malware analysts use signature-based malware detection to detect patterns or sequences of bytes unique to the malware in its code. Similar sequences are saved in a database, and anti-malware software attempts to identify such patterns in executable files during scanning. Signature-based malware detection systems have historically been popular due to their low false-positive ratio. They are, however, incapable of dealing with obfuscated code in malware and cannot identify new strains until they have been thoroughly examined by an analyst.
Behavior-based detection, on the other hand, is the idea of studying how malware functions. During this sort of detection, the anti-malware software searches for certain behaviors that may signal the existence of dangerous software.
Because it shows basic behavioral patterns required for a data encryption attack that do not alter from variation to variant or family to family, behavioral-based analysis has been proven to be particularly successful for crypto-ransomware detection. These behavior traits can be divided into two categories: suspicious setup and data encryption.
- Suspicious setup behavior: Many of the characteristics of ransomware are similar to those of other malware, notably the way it installs itself before delivering the payload. This consistent behavior may be considered a generic "recipe for success" that malware makers follow, which can be broken down into six distinct characteristics:
- Payload persistence: To guarantee that an attack is completed, it must persist between reboots and be able to restart once it has been started.
- Anti-system restores: Malware may attempt to deactivate system restore capabilities in order to prevent any harmful operations from being undone.
- Stealth techniques: Malware will attempt to execute invisibly in order to avoid being discovered by virus scanners or being observed by the user.
- Environment mapping: When malware is run, it may attempt to map its system environment before beginning its setup phase. This is usually done to see if it's operating on a real machine or if it's in a sandbox environment that's trying to analyze it.
- Network traffic: Ransomware that needs to connect to the internet does so for one of two reasons: to download payload-related files and/or to communicate the encryption key.
- Privilege elevation: Executing harmful system-related operations may need access permissions that are greater than those granted to the victim's user account.
- Data encryption: The capacity to convert large volumes of data from a useable to an unusable state is at the heart of crypto-ransomware behavior. Although behavioral-based detection approaches focused on detecting bulk file encryption may be successful, they may come at a high cost in terms of resources.
In addition to recognizing known ransom notes or known ransomware file extensions, analyzing file activity behavior can reveal more immediate proof of a ransomware assault. To make it easier for the victim to pay the ransom, the ransomware must notify the victim that a ransomware attack has happened and offer instructions on how to restore the victim's data. This is commonly accomplished by printing out ransom notes in a variety of forms and locations around the user's directory.
As a result, one technique to identify ransomware is to look for dropped ransom notes. To discover ransom-related material, such a technique necessitates static analysis of suspicious files. For example, a bag-of-words technique (like those used in spam detection) might be used to uncover correlations between terms common in ransom notes (such "pay ransom," "encrypted," and "Bitcoin") and phrases found in an unknown dumped file.
Behavioral features connected with data transformation and file operation might give useful information for identifying ransomware. It can't, however, be depended on entirely.
3. Detection By Abnormal Traffic
Abnormal traffic detection is similar to behavior-based detection, except it operates on a network level. Ransomware assaults are frequently two-fold: they encrypt data to hold it for ransom, but they also steal data before encrypting it to use as leverage. As a result, significant amounts of data are sent to other systems.
While ransomware can hide its tracks and transfers, it may also generate network activity that can be detected. Abnormal traffic detection may be traced directly to the machine's malware, allowing users to remove it.
Behavior-based detection is effective at the end-point. Organizations, on the other hand, can detect ransomware at the network level by monitoring network traffic for any malicious activities or irregularities.
Modern ransomware is programmed to steal or exfiltrate important information from your network before encrypting it and demanding payment. To carry out a large-scale data breach, the attacker will need to transport a lot of data out of the network parameter.
Despite the fact that ransomware runs invisibly, these transfers can cause anomalous network spikes that an abnormal traffic monitoring system can identify.
Security teams should look for unusual patterns in traffic, such as if any software is connecting to shady file-sharing sites and when this happens. Teams should also determine whether traffic volume has lately grown and where it is headed. To receive command and control instructions and exchange decryption keys, ransomware requires network access to off-site servers.
While beneficial, this form of detection produces false positives and necessitates analysis time. In addition, attackers may utilize genuine file-sharing sites that have been approved by the affected organization to avoid detection.
Why is Early Detection of Ransomware Important?
Ransomware attacks are becoming more common by the day. While large-scale assaults or disasters affecting public services or essential infrastructure may receive the most attention, anybody can be a target, and businesses should be prepared.
Expert advice is sound: Use defense-in-depth, back up frequently (and make sure your systems can be restored from backups), keep all software and systems up to date and patched, educate your employees about phishing and other potential threats, and have an incident response plan in place if and when you are attacked. The importance of early identification, however, is frequently overlooked in ordinary talks.
Strong protection and good cyber hygiene are obviously vital, as are frequent backups, but ransomware attacks will continue to evolve and become more successful as new zero-day vulnerabilities are discovered and exploited.
Long before any information is taken and encrypted, security teams must be able to spot the initial attack. As a result, rather than depending on daily reports, every business should use some form of an always-on inline monitoring system that can search for anomalous activity and respond in near real-time.
How to Detect Ransomware on Your Network?
Although no company is immune to cybersecurity threats, there are a few best practices that may help you lower your chance of being a victim of malware and detect ransomware attacks in progress.
- Educate your employees: Give staff a checklist of what to do if they get a questionable email or website. Teach them to look for red indicators in phishing emails, such as the following:
- Email accounts with a professional appearance
- Attachments to files that are suspicious
- External URLs with suspicious linkages
- Monitor your systems: Keep an eye on your systems for any suspicious activity by performing the following activities:
- Scanning file systems for unusual behavior, such as hundreds of unsuccessful file changes is a good example.
- All incoming and outgoing traffic should be recorded.
- Determine the regular user activity baseline and search for anomalies ahead of time.
- Investigate any strange activities right away.
- Create honeypots: Honeypots are decoys or phony file repositories that appear to be authentic. Honeypots will be targeted by hackers, allowing you to recognize them. Early detection aids in the safe eradication of malware and saves your infrastructure from being hacked. The use of honeypots in File Server Resource Manager (FSRM) ransomware defense is a great example.
- Make use of the software: Use whitelisting software in conjunction with anti-virus and anti-ransomware software to detect risks.
- Analyze the email's content: Analyze and filter spam or questionable email content in a systematic manner:
- Configure email settings so that incoming mail is automatically filtered and questionable messages are not delivered to a user's mailbox.
- Allow files with specific extensions, such as executable files, to be attached to emails.