Abdulbasit A. Darem^*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 461-479, 2022, DOI:10.32604/cmc.2022.023566

Abstract Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The deep learning-based approach is recently… More >

Huizhong Sun¹, Guosheng Xu^1,*, Zhimin Wu², Ruijie Quan³

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 585-600, 2022, DOI:10.32604/iasc.2022.023874

Abstract With the rapid development of Android devices, Android is currently one of the most popular mobile operating systems. However, it is also believed to be an entry point of many attack vectors. The existing Android malware detection method does not fare well when dealing with complex and intelligent malware applications, especially those based on feature detection systems which have become increasingly elusive. Therefore, we propose a novel feature selection algorithm called frequency differential selection (FDS) and weight measurement for Android malware detection. The purpose is to solve the shortcomings of the existing feature selection algorithms in detection and to filter… More >

Liwei Wang^1,2,3, Jiankun Sun^1,2,3, Xiong Luo^1,2,3,*, Xi Yang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 1003-1016, 2022, DOI:10.32604/cmes.2022.018492

Abstract With the development of information technology, malware threats to the industrial system have become an emergent issue, since various industrial infrastructures have been deeply integrated into our modern works and lives. To identify and classify new malware variants, different types of deep learning models have been widely explored recently. Generally, sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability. However, in current practical applications, an ample supply of data is absent in most specific industrial malware detection scenarios. Transfer learning as an effective approach can be used to alleviate the influence of the… More >

Hae-Seon Jeong¹, Jin Kwak^2,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 467-481, 2022, DOI:10.32604/iasc.2022.021038

Abstract Owing to the development of next-generation network and data processing technologies, massive Internet of Things (IoT) devices are becoming hyperconnected. As a result, Linux malware is being created to attack such hyperconnected networks by exploiting security threats in IoT devices. To determine the potential threats of such Linux malware and respond effectively, malware classification through an analysis of the executed code is required; however, a limitation exists in that each heterogeneous architecture must be analyzed separately. However, the binary codes of a heterogeneous architecture can be translated to a high-level intermediate representation (IR) of the same format using binary lifting… More >

M. Prabhavathy^1,*, S. UmaMaheswari²

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 101-114, 2022, DOI:10.32604/iasc.2022.018257

Abstract Cloud computing utilizes various Internet-based technologies to enhance the Internet user experience. Cloud systems are on the rise, as this technology has completely revolutionized the digital industry. Currently, many users rely on cloud-based solutions to acquire business information and knowledge. As a result, cloud computing services such as SaaS and PaaS store a warehouse of sensitive and valuable information, which has turned the cloud systems into the obvious target for many malware creators and hackers. These malicious attackers attempt to gain illegal access to a myriad of valuable information such as user personal information, password, credit/debit card numbers, etc., from… More >

Sohail Khan^1,*, Mohammad Nauman², Suleiman Ali Alsaif¹, Toqeer Ali Syed³, Hassan Ahmad Eleraky¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.021271

Abstract Mobile phones are an essential part of modern life. The two popular mobile phone platforms, Android and iPhone Operating System (iOS), have an immense impact on the lives of millions of people. Among these two, Android currently boasts more than 84% market share. Thus, any personal data put on it are at great risk if not properly protected. On the other hand, more than a million pieces of malware have been reported on Android in just 2021 till date. Detecting and mitigating all this malware is extremely difficult for any set of human experts. Due to this reason, machine learning–and… More >

Pagnchakneat C. Ouk¹, Wooguil Pak^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3099-3116, 2022, DOI:10.32604/cmc.2022.020202

Abstract The Android operating system has become a leading smartphone platform for mobile and other smart devices, which in turn has led to a diversity of malware applications. The amount of research on Android malware detection has increased significantly in recent years and many detection systems have been proposed. Despite these efforts, however, most systems can be thwarted by sophisticated Android malware adopting obfuscation or native code to avoid discovery by anti-virus tools. In this paper, we propose a new static analysis technique to address the problems of obfuscating and native malware applications. The proposed system provides a unified technique for… More >

Hani Mohammed Alshahrani^*

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 739-766, 2022, DOI:10.32604/cmc.2022.019623

Abstract One of the most rapidly growing areas in the last few years is the Internet of Things (IoT), which has been used in widespread fields such as healthcare, smart homes, and industries. Android is one of the most popular operating systems (OS) used by IoT devices for communication and data exchange. Android OS captured more than 70 percent of the market share in 2021. Because of the popularity of the Android OS, it has been targeted by cybercriminals who have introduced a number of issues, such as stealing private information. As reported by one of the recent studies Android malware… More >

Marwan Albahar^*, Mohammed Thanoon, Monaj Alzilai, Alaa Alrehily, Munirah Alfaar, Maimoona Algamdi, Norah Alassaf

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2181-2202, 2021, DOI:10.32604/cmc.2021.018260

Abstract Malicious Portable Document Format (PDF) files represent one of the largest threats in the computer security space. Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction. These approaches are time consuming, require substantial prior knowledge, and the list of features must be updated with each newly discovered vulnerability individually. In this study, we propose two models for PDF malware detection. The first model is a convolutional neural network (CNN) integrated into a standard deviation based regularization model to detect malicious PDF documents. The second model is a support vector machine (SVM)… More >

Huizhong Sun¹, Guosheng Xu^1,*, Hewei Yu², Minyan Ma³, Yanhui Guo¹, Ruijie Quan⁴

Journal of Quantum Computing, Vol.3, No.2, pp. 55-63, 2021, DOI:10.32604/jqc.2021.017365

Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is… More >