Malware detection calls for the use of tools and techniques to identify, alert, block and respond to malware threats, such as downloading a malicious code. Basic malware detection methods can help identify and restrict known threats while advanced malware detection tools use machine learning and artificial intelligence (AI) to seek out and identify new and unknown threats.
Signature-based detection is a traditional malware detection method that matches files against a database of known malware signatures. What that in mind, let’s take a closer look at what you need to know about signature-based malware detection.
Understanding Malware Detection
Malicious actors use malware to infiltrate computers, networks, or systems to cause widespread damage. There are many different types of malware used to achieve different objectives. Spyware, for example, will gather information from devices while the aim of trojans is to gain a persistent hold on a system. Ransomware, on the other hand, will encrypt information and the owners will be forced to pay up to retrieve it.
The above underlines the importance of using and understanding the importance of malware detection software. Cybersecurity professionals will utilise a number of tools and techniques to identify malware threats. Antivirus programs are the most effective method of detecting malware. They can scan software, identify its signature, and compare it to signatures of known malware. Read on to find out more.
Network Intrusion Detection System Types
Network intrusion detection systems (NIDS) detect malicious traffic on a network. NIDS require full network access to analyse all traffic, including unicast traffic. NIDS do not interfere with the traffic they monitor and are in essence passive devices. Let’s take a look at three NIDS types:
Signature-based detection uses a unique signature, or digital footprint, from software programs running on a protected system. Antivirus programs scan software, identify the signature then compare it to signatures of known malware.
Antivirus programs have a vast library of known malware signatures which is updated on a regular basis. When an antivirus program pinpoints software that matches a known signature, it will either delete or quarantine it.
Anomaly-Based Intrusion Detection
An anomaly-based intrusion detection system is used for both computer and network intrusions, and misuse by monitoring system activity, classifying it as either anomalous or normal. The classification is based on rules or heuristics, as opposed to signatures or patterns, and detects any kind of misuse outside of normal system operation. This differs from signature-based detection systems which only detect attacks where a signature has previously been created.
Hybrid Intrusion Detection
A hybrid intrusion detection system is developed to overcome the disadvantages of signature-based and anomaly-based detection systems. The hybrid system integrates both detection systems to detect both known and unknown attacks. When used simultaneously in a hybrid system, anomaly-based detection is used to identify unseen intrusions while signature-based detection is used to identify known attacks.
Benefits of Signature-Based Malware Detection
Signature-based malware detection uses signatures and the best way to describe them is like the ‘fingerprint’ of a virus which is unique to that specific virus. This makes signature-based malware detection accurate in identifying known threats, as it matches the threat with its known code.
Signature-based malware detection is a very effective technique used against known and frequent attacks, such as phishing, malware, or denial-of-service. It is also very easy to install and maintain, as it relies on regular updates of the signature database from security experts or vendors.
Pros and Cons of Signature-based Malware Detection
Pros: Like described in the fingerprint analogy above, signature-based malware detection captures the actions unique to any given attack. This approach focuses on specific attacks and is extremely accurate at lowering the rate of false positives. It is easy to implement and manage and is constantly being updated.
Cons: Signature-based malware detection does have its drawbacks. The main downside is that it can only detect known attacks. Internet worms like Nimda and Code Red underline the need for systems that can detect and prevent unknown attacks. It also fails to detect variants of existing attacks that do not match signatures already in the database. There is also a high rate of false positives when legitimate traffic is mistaken for an attack.
Combining Signature-Based Detection with Other Methods
The most effective approach to malware detection is, of course, a hybrid model that combines the best of both protection methodologies. Hybrid methods using both signature-based and anomaly-based intrusion detection cover both known and unknown attacks while keeping the number of false positives to a minimum.
IT managers and cybersecurity professionals should be aware that combining signature-based detection with other methods helps to better protect servers, files, and data. A hybrid approach provides protection at all levels to ensure company assets are secure. It’s also important to note that the greatest possible protection is provided by combining the broadest and best layers of security for a defence-in-depth strategy.
The Future of Malware Detection: Emerging Technologies
Malware has become a serious threat to organisations all around the world. The level of complexity is rising, and the sheer number is becoming worrisome. The development of malware detection technology is happening at a phenomenal rate but so are the methods of malicious actors at the same time. That’s why it’s so important that researchers and cybersecurity professionals are able to utilise emerging technologies to stop malware in its tracks.
Providing new developments and trends in malware detection technology is fundamentally important. Ensuring that state-of-the-art of malware detection methods are always a priority, like those mentioned above. Exploring the challenges and limitations of AI and machine learning implementation on malware detection is key. Utilising AI and machine learning in malware detection combines the modelling of both good and bad behaviour. It can be a very powerful weapon against even the most advanced malware.
How RiskXchange Can Help You
RiskXchange’s comprehensive threat detection software protects organisations of any size against both known and unknown malware attacks. With full visibility over your eco-systems entire attack surface in near real-time, you can regularly monitor and mitigate risks to prevent unnecessary exposures. Our passive data collection methods are effective and have no impact on your network performance.
Malware Detection FAQs
What is the difference between signature-based and anomaly-based intrusion detection systems?
Signature-based detection is used for known threats while anomaly-based detection is used for changes in behaviour.
What is signature vs rule-based detection?
When comparing signature-based detection to rule-based detection, the latter relies more on technology and less on manual interventions.
What are common malware detection techniques?
Common malware detection techniques include signature-based detection, anomaly-based intrusion detection, static file analysis, dynamic malware analysis, dynamic monitoring of mass file operations, file extensions blocklist/blocklisting, application allowlist/allowlisting, malware honeypot/honeypot files, checksumming/cyclic redundancy check (CRC) and more.
Get in touch with RiskXchange to find out more about signature-based malware detection.