Abstract
With an increase in the number of internet users, the number of cyber-attacks happening in organizations is increasing day by day. Most of the cyber-attacks involve the use of malicious software known as malware to steal personal information, gain unauthorized access to the computer systems and carry out malicious activities which can cause huge financial losses to the organizations. Viruses, worms, rootkits, adware or anything that performs malicious activities is classified as malware. Detecting malware is a major challenge faced by the anti-malware industry as the signature-based malware detection methods may not provide accurate detection of malware. In this paper, an artificial neural network approach for malware detection is presented to overcome the shortcomings of signature-based malware detection methods. The proposed method can be used as a base model for the malware detection process and can be further developed to enhance the functionality.
Highlights
Various organizations and people’s daily tasks are heavily dependent on the internet. This allows hackers to develop malicious software known as malware which is used to perform malicious activities in organizations and people’s systems through the internet
Whenever the file enters a network or a computer system, the anti-malware engine generates the unique value of the file entered and compares that unique value against its database
A biological neural network consists of neurons, an artificial neural network consists of artificial neurons
Summary
In recent years the use of the internet has grown exponentially. Various organizations and people’s daily tasks are heavily dependent on the internet. Entries in the database the malware detection engine blocks the file and classifies it as malicious The problem with this approach is that it can be bypassed, the change of a single byte in the malicious file or a change in the code of malicious file will change the unique value of the malware making it undetectable. From the past few years, the anti-malware industry has started using behavioral analysis and machine learning approaches to detect malware [6]. In the case of malware, the data is non-linear, non-sequential This makes the ANN to be the most suitable approach for the malware detection process.
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