In the present digital era, most of our communication and personal sensitive information are transmitted through smart devices and stored on them. Therefore, it becomes imperative to secure both the device and the data from various security and privacy threats. These threats aim to gain unauthorized access to the data, or worse, destroy it. This chapter presents an overview of malware analysis and its mitigation tools. Malware has become a serious threat to computer systems and networks, and it is important to understand how to analyze and mitigate the risks associated with it. Here, the authors discuss malware and its classification, as well as various techniques used in malware analysis, including static and dynamic analysis. The chapter also presents an overview of the mitigation tools available to prevent and detect malware, including antivirus software, firewalls, intrusion detection systems, and sandboxes. Furthermore, the chapter highlights some of the limitations of these tools and provides insights into the future direction of malware analysis and mitigation.
TopIntroduction
Malware attacks are becoming more frequent, and new, advanced varieties are being created daily. These assaults have the potential to do a great deal of harm, including data theft and destruction, monetary losses, and reputational harm. Malware analysis tools are useful for analysing the structure and behaviour of malware attacks to detect and mitigate them (Aboaoja et al., 2022). Malware analysis tools are sophisticated programmes that can look through and evaluate malware to ascertain its features and activity. In this paper, we examine the capabilities of various malware analysis tools.
Tools for Malware Analysis: These programmes' main purpose is to locate and examine malware. Static, dynamic, and hybrid analysis tools are three categories into which these tools can be divided.
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Static Analysis Tools: Without running the malicious code, static analysis tools analyse it. These tools analyse the structure of the malware to look for patterns and signatures that can be used to identify it. They can also identify the routines and API calls the infection employs, which can provide insight into its behaviour. Examples of static analysis tools are IDA Pro, Ghidra, and Radare2.
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Dynamic Analysis Tools: To investigate malware, it is launched in a sandbox or virtual system, which is a controlled environment. These tools can follow the behaviour of the infection and expose its network activities, system calls, and file modifications. Dynamic analysis approaches might provide a more complete picture of the behaviour of the malware than static analysis techniques. For example, dynamic analysis tools like FireEye, VMRay Analyzer, and Cuckoo Sandbox.
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Hybrid Analysis Tools: These tools integrate the capabilities of static and dynamic analysis tools. These tools could analyse the malware code and execute it in a controlled environment to provide a full understanding of the infection's activities. Hybrid analysis methods can provide a more detailed investigation of the malware than static or dynamic analysis tools alone. Examples of hybrid analytic tools are Reversing Labs Titanium Platform, Symantec Advanced Threat Protection, and McAfee Advanced Threat Defence.
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Evaluation Standards: Functionality, usability, and efficacy are three factors that are considered when evaluating the malware analysis programme. Functionality describes a tool's capacities, including the types of analyses it can conduct and the volume of data it can handle. Usability is the ability of a tool to be used effectively and to integrate with other tools and systems. Effectiveness refers to the tool's capacity to identify and analyse malware as well as the reliability of its conclusions.
TopBackground
Malware analysis is the process of looking at malicious software, also referred to as malware, to understand its nature, function, and potential effects. Malware (Komatwar R. & Kokare, M. 2021), which can include viruses (Vermisoglou E. et al., 2020), worms, trojan horses, spyware, and ransomware, is designed to damage, steal, or seize control of the victim's computer system or data.
To identify and lessen the risks that are posed by dangerous software, malware analysis is performed. Malware analysis can be carried out using a variety of methods, including static analysis, dynamic analysis, hybrid analysis, machine learning-based methods (Akhtar, M. S. & Feng, T. 2023) (Qiu J et al., 2020), deep learning methods (Yadav, C. S. et al., 2022), vinayakumar R et al., 2019, and other approaches (Chakkaravarthy et al. These methods entail analysing malware's code and activity to determine its functioning, traits, and potential effects (Gaurav et al., 2022). Mitigation tools (Barsha, F. L., and Shahriar, H. 2023) aim to stop or reduce the harm that malware can do. Some of these tools include firewalls, antivirus programmes, intrusion detection and prevention systems, and sandboxing tools. Antivirus software finds and deletes known infections, whereas intrusion detection and prevention systems monitor network traffic for unusual activity and block or alert administrators of potential attacks. Sandboxing techniques isolate potentially malicious software to prevent it from doing harm, and firewalls prevent unauthorised users from accessing a system or network.