With the invention of a powerful, portable and lightweight device called a smartphone, there has been a very high number of its usage and a subsequent rise in the security issues involved in using a smartphone. Smartphones have gone from being non-existent many years ago to being heavily relied upon by a lot of people globally. This is because it is highly functional and contains various features. It is used for many different things like internet banking, entertainment, ecommerce, communication, mathematical calculations and many other things. Various types of smartphones and their distinct features were also identified, with android smartphone, iPhones, Window and Symbian being the top popular types of Mobile devices. Irrespective of the type of smartphone, there is a lot of data stored on it and most of the data stored are sensitive and susceptible to attack. There are a number of causes for smartphone security issues which can vary based on the type of the smartphone. Some of these causes were identified to be outdated OS or third-party apps, use of public WiFi, low security network protocols, physical breach and convergence. There are many examples of the security issues of smartphones which includes; malware attack, phishing attacks, spyware, identity theft, data invasion and theft and OS exploits. In the research work, we looked into ways to prevent these security issues which include; timely updates of OS and third-party apps, use of secure WiFi, use of antivirus, authentication and authentication. Desk research involving the literature review of “Smartphone” and “Smartphone security challenges and prevention” journals and articles were used. The research paper aimed to provide a concise knowledge and clear understanding of smartphones, its security and the prevention of the security challenges and also highlight the preventive measures that could be put in place to secure smartphones
Most cyberattacks including data breaches, identity theft, fraud, and other issues, are known to be caused by malware. Some of the malware attacks are categories as adware, spyware, virus, worm, trojan, rootkit, backdoor, ransomware and command and control (C&C) bot, based on its purpose and behaviour. Malware detectors still utilise signature-based approaches to detect malicious software, which can only detect known malware. Attacks by malware pose a serious threat to people's and organizations' cybersecurity globally. These attacks are occurring more frequently and more frequently lately. Over eight billion malware attacks occurred in 2020, up 4% over the previous year, according to a Symantec report. It is crucial that computer users safeguard their computers with a malware detector like an antivirus, anti-spyware, etc. When creating a machine learning model to differentiate between malicious and benign files, it might be challenging to use domain-level expertise to extract the necessary attributes. This research aims to create a malware detector that uses a trained random forest classifier model to find malware and stop zero-day assaults. A dataset (including both harmful and benign software PE header information) was obtained from virusshare.com and used to train the random forest classifier in order to create this malware detector. The Random Forest Classifier generate greater accuracy when compared with other machine learning classifiers, such as KNN (K-Nearest Neighbors), Decision Tree, Logistic Regression etc., the random forest classifier gives a better accuracy of 99.4%. The Classifier model used here will be a better option to use in order to efficiently and effectively detect malware, it shows that the methodology can be utilized as the basis for an operational system for detecting an unknown malicious executable.
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