As advancement in the digital world accelerates, the attacking strategies of cybercriminals also evolve. With the advent of IoT and 5G, malware tend to exploit devices, servers and networks. Malware variants range from ransomware that encrypts or locks our files to spyware that silently gathers information from the devices. The digital world is exploded with malware threats. An increase in the sophistication of malware attacks, brings financial and reputation damage to both individuals as well as organizations alike. Fortunately, security organizations and anti-virus companies have developed many techniques over the time to combat these threats. These techniques are imperative to attain cybersecurity and prevent the impact of malware attacks as much as possible. Some of these techniques are discussed in detail as follows:
1. Signature-Based Detection
Signature-based detection is one of the most traditional and popular techniques in the world of malware. It basically matches the signature of a file or software's code with a database of known malware signatures. If it finds any matching result, then it will flag the file as malware otherwise it flags the file as benign.
This technique works effectively against known/existing malware threats because once a malware is identified and its signature is added in the database, detection is quick and precise. However, the database requires continuous updations to detect novel malware. Moreover, it neither detects zero-day threats (malware for which signatures don’t exist) nor the malware revised using polymorphic and metamorphic techniques.
2. Heuristics analysis
Heuristics-based detection goes a step ahead to overcome the limitations of signature based techniques because it identifies suspicious behavior and possible threats using rules and patterns instead of looking for perfect matches. This method is efficient in identifying variants of known as well as unknown malware threats as it evaluates the sample based on suspicious patterns, not on predefined signatures. This technique is popular for detecting unknown malware variants. On the other hand, it may create a lot of false positives as there are chances that legitimate files may exhibit behavior similar to malware.
3. Behavioral-Based Detection
Behavioral-based detection focuses on the behavior of malware instead of its code. It assesses the actions performed by a sample such as changes to critical system files, initiated remote connections with malicious servers, and encryption of data. In case of any suspicious behavior, a flag on the sample is raised. This technique is highly beneficial against malware changing their behavior after a certain amount of time and evades detection by traditional methods. Moreover, this method is highly beneficial in case of fileless malware that leaves no traces on the hard disk but operates only through Random Access Memory. However, this technique is resource-intensive in nature and slows down system performance. Furthermore, there is high likelihood that the system could get infected by the malware, posing a serious threat to sensitive files.
4. Sandboxing
This is a containment-based approach where, in order to observe the behavior of potentially malicious file, the file gets executed in an environment that is controlled and isolated. This environment is known as the "sandbox environment". If the sandbox identifies the malicious behavior of a sample, then it would classify the file as malware and prevent it from affecting the actual system. This approach provides a safe environment for malware analysis without putting the system at risk but at the same point of time, it is resource-intensive and delays the detection process. Additionally, malware may not reveal its actual behavior in the presence of sandbox.
5. Machine Learning and AI-Based Detection
Machine learning and artificial intelligence have shown immense potential in malware detection. Initially, machine learning based malware detection systems process a large amount of data, identify complex patterns and simultaneously learn from new malware samples. These types of systems learn from both, malicious and non-malicious data; hence, they are able to detect unknown threats with high accuracy without any prior knowledge about them. However, their implementation is complex and requires large datasets for training.
6. Cloud-Based Detection
In cloud-based detection, malware analysis and detection are done on remote servers. This facilitates quicker scanning and real-time updations when new threats are detected. Cloud-based detection techniques transfer the burden of processing from the local machines to the remote servers and utilize collective intelligence to detect malware. These techniques facilitate real-time updates and reduce usage of system resources but require internet connectivity. Moreover, they may raise privacy concerns in case of sensitive details.
Conclusion
The proliferation of malware necessitates the development of a multilevel approach to malware detection. Classic methods, such as signature-based detection, integrated with advanced techniques such as AI, machine learning, and behavioral analysis offer a more comprehensive solution against the new generation of threats. Updated information about the latest detection methods helps both users and organizations to protect themselves against ever-present malware threats.
Dr. Prachi
Research Areas: Cyber Security, Digital Forensics, Malware Detection using Machine Learning
Associate Professor