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Advanced Heuristics to Detect Zero-Day Attacks

by Ensar SekerMarch 28th, 2021

Too Long; Didn't Read

Zero-Day Exploits that are developed, distributed, and executed by Zero Day Attacks are becoming increasingly common and widespread in the world of cybersecurity and security research and development. Traditional protection tools depend on binary malware signatures or external URLs and server reputations. The best defense against zero-day attacks is one that focuses on detection and response, as prevention efforts typically fail on unknown vulnerabilities and exploits. An anomaly-based detection system tracks the processes of any abnormal behavior on a host computer if suspicious behavior is detected, a warning alert may be raised that the malware might be present.

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With the growing number of zero-day attacks of various kinds that traditional antivirus software has not been able to detect effectively, a whole new market segment has emerged. Zero-Day Exploits that are developed, distributed, and executed by Zero Day Attacks, are becoming increasingly common and widespread in the world of cybersecurity and security research and development.

What Is A Zero-Day Exploit and Attack?

A zero-day exploit is an undisclosed security or device loophole that an attacker at risk may use malicious code to address.

A zero-day attack is for the attacker to develop a malicious program that targets the zero-day vulnerability. The attack can be carried out via email, social media, and other means such as using a social engineering technique.

Once the exploit has been developed, the next step is to deliver the malware to the target system to perform the zero-day attack. The malware is detected by the Threat Emulation Engine so that the hacker can use evasive techniques that try to bypass the sandbox.

Defense Against Zero-Day Attacks

The best defense against zero-day attacks is one that focuses on detection and response, as prevention efforts typically fail on unknown vulnerabilities and exploits. Because attackers can exploit zero-day exploits, most organizations are slow to respond to newly discovered vulnerabilities.

Traditional protection tools depend on binary malware signatures or external URLs and server reputations. These protections only recognize known, confirmed threats by design. Code-morphing and obfuscation strategies produce new malware variants more quickly than conventional defense companies would create new signatures. And spam filters do not stop attacks by spear-phishing, which are limited in number. Operating system-level protection including Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) is also being decreased. Simultaneously, operating system level protection is being less effective.

Innovative solutions that combine advanced heuristics to protect infections from targeted attacks, such as those used in zero-day attacks of various types, and advanced detection methods.

Existing approaches can be divided into three categories:

Statistical-based approach: The statistical approach to real-time detection for zero-day vulnerabilities focuses on historical evidence dependent on attack profiles. This strategy generally does not fit in well with shifts in zero-day exploit trends. Any changes in a zero-day exploits pattern would require a new profile to be learned by the system.
In signature-based detection, a maintained archive of all signature files is the primary prerequisite of the method. The exactness is based entirely on the system's signature database. The new malware cannot be detected using the signature-based detection technique when no new virus information is available in the database. Although this method of intrusion detection is quick and reliable since there is a very small risk for false alarms. Overall, the ability to generate accurate signatures that match real malware is key to determining whether a signature-based approach actually works to detect zero-day exploits. One of the only ways to use signature recognition as protection against zero-day attacks is to use machine learning and similar algorithms to generate real-time signatures that may match and thus recognize currently unknown malware.
An anomaly-based detection system tracks the processes of any abnormal behavior on a host computer. If suspicious behavior is detected, a warning alert may be raised that the malware might be present. The machine uses the heuristics it collects to classifies an operation as natural or malicious in this detection technique. While the likelihood of false alarms in this system is comparatively greater, it is more accurate since it can also detect new viruses.

There is no guarantee that these methods detect malicious activities, so antivirus solutions that use heuristic analysis can be a great weapon against zero-day malware. Products that can use heuristic techniques to detect new malware by recognizing its similarities to known malware and other features.

The Heuristic Method

Unfortunately, zero-day exploits cannot be detected by antivirus signatures, and existing solutions are unable to detect the possibilities that lead to zero-day attacks on networks. In the future, however, we could detect zero-days from behaviors - tracking algorithms that detect suspicious malicious behavior. By monitoring behavior instead of signatures, antivirus software can detect undetected malware and effectively fight zero-day attacks. A good antivirus also uses a technique called heuristics analysis

Heuristic detection can scan files for suspicious characters and detect new malware without signature recognition. It checks files for features that the system finds questionable rather than requiring an accurate file signature match. This can be performed statically or by emulation where the anti-virus uses a low clocking cycle to simulate the execution of the file.

In this approach, the "suspicious" behavior is mostly a perception dependent on the software's risk thresholds. Since several features that have been observed together may give an alert, heuristic mechanisms to detect legitimate files are noted as malware.

Antivirus heuristics that detect suspicious activity - looking for activity - can also block zero-day attacks. The fact that Anti-Virus Solutions protect against advanced zero-day attacks should underscore the need for effective runtime protection. An ideal security solution for the protection of the runtime should be able to detect a zero-day attack without generating false positive alarms at the same time.

The heuristic engine used by an antimalware program includes rules for the following:

a program that tries to copy itself into other programs (in other words, a classic computer virus)
a program that tries to write directly to the disk
a program that tries to remain resident in memory after it has finished executing
a program that decrypts itself when running (a method often used by malware to avoid signature scanners)
a program that binds to a TCP/IP port and listens for instructions over a network connection (this is pretty much what a bot—also sometimes called drones or zombies—do)
a program that attempts to manipulate (copy, delete, modify, rename, replace, and so forth) files that are required by the operating system
a program that is similar to programs already known to be malicious

To be able to detect modern zero-day attacks, the solution must be able to monitor as many events as possible, including, but not limited to, monitoring of all system processes including hidden, existing hooks and floating-point vulnerabilities.

Advanced heuristic protection algorithms to detect behavior that indicates malicious activity even before an attack attempt is made within zero days.

Cloud technology now offers a complementary feature to file and device behavioral research. It is also one of some of the top anti-virus packages on the market.

How cloud-based heuristics detection work?

Data was collected on secured computers from endpoints – that is to say, customers with lightweight anti-virus system installations. This could provide relevant information about the file configuration and how it operates on the endpoint system.
The recorded data is analyzed on the platform of the cloud service, which may include numerous computers and connections to online databases.
Any unusual behavior found on client endpoint devices from non-malicious files is applied and integrated into subsequent review into the cloud service database.

In a nutshell, traditional antivirus software is usually only effective in fending off known threats and is therefore often ineffective in protecting against zero-day exploits. Zero-day attacks do not give security analysts and developers enough time to overcome the threat. Detecting zero-day attacks should be faster and give hackers less time to develop exploit code.

Although virus protection software manufacturers are aware of the dangers of zero-day exploits, not all software has been developed with this goal in mind when it comes to protecting themselves against them. The advantage of a heuristic analysis of code is it can detect not just variants (modified forms) of existing malicious programs but new, previously-unknown malicious programs, as well.