The Implementation of Deep Neural Networks Algorithm for Malware Classification

Malware is very dangerous while attacked a device system. The device that can be attacked by malware is a Mobile Phone such an Android. Antivirus in the Android device is able to detect malware that has existed but antivirus has not been able to detect new malware that attacks an Android device. In this issue, malware detection techniques are needed that can grouping the files between malware or non-malware (benign) to improve the security system of Android devices. Deep Learning is the proposed method for solving problems in malware detection techniques. Deep Learning algorithm such as Deep Neural Network has succeeded in resolving the malware problem by producing an accuracy rate of 99.42%, precision level 99% and recall 99.4%

Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges

Advancements in microelectronics have increased the popularity of mobile devices like cellphones, tablets, e-readers, and PDAs. Android, with its open-source platform, broad device support, customizability, and integration with the Google ecosystem, has become the leading operating system for mobile devices. While Android's openness brings benefits, it has downsides like a lack of official support, fragmentation, complexity, and security risks if not maintained. Malware exploits these vulnerabilities for unauthorized actions and data theft. To enhance device security, static and dynamic analysis techniques can be employed. However, current attackers are becoming increasingly sophisticated, and they are employing packaging, code obfuscation, and encryption techniques to evade detection models. Researchers prefer flexible artificial intelligence methods, particularly deep learning models, for detecting and classifying malware on Android systems. In this survey study, a detailed literature review was conducted to investigate and analyze how deep learning approaches have been applied to malware detection on Android systems. The study also provides an overview of the Android architecture, datasets used for deep learning-based detection, and open issues that will be studied in the future

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Evaluating Convolutional Neural Network for Effective Mobile Malware Detection

In last years smartphone and tablet devices have been handling an increasing variety of sensitive resources. As a matter of fact, these devices store a plethora of information related to our every-day life, from the contact list, the received email, and also our position during the day (using not only the GPS chipset that can be disabled but only the Wi-Fi/mobile connection it is possible to discover the device geolocalization).This is the reason why mobile attackers are producing a large number of malicious applications targeting Android (that is the most diffused mobile operating system), often by modifying existing applications, which results in malware being organized in families, where each application belonging to the same family exhibit the same malicious behaviour. These behaviours are typically information gathering related, for instance a very widespread malicious behaviour in mobile is represented by sending personal information (as examples: the contact list, the received and send SMSs, the browser history) to a remote server managed by the attackers.In this paper, we investigate whether deep learning algorithms are able to discriminate between malicious and legitimate Android samples. To this end, we designed a method based on convolutional neural network applied to syscalls occurrences through dynamic analysis. We experimentally evaluated the built deep learning classifiers on a recent dataset composed of 7100 real-world applications, more than 3000 of which are widespread malware belonging to several different families in order to test the effectiveness of the proposed method, obtaining encouraging results. (C) 2017 The Authors. Published by Elsevier B.V

Strategies Universities’ and Colleges’ IT Leaders Use to Prevent Malware Attacks

Information systems at universities and colleges are not exempt from the threat of malware. Preventing and mitigating malware attacks is important to universities’ and colleges’ IT leaders to protect sensitive data confidentiality. Grounded in general system theory, the purpose of this exploratory multiple case study was to explore strategies universities’ and colleges’ information technology (IT) leaders use to prevent and mitigate malware attacks. Participants consisted of 6 IT leaders from 3 universities and colleges in Southern California responsible for preventing and mitigating malware attacks. Data were collected through semistructured video teleconferences and 7 organizational documents. Three significant themes emerged through thematic analysis: personnel issues, security planning, and security management practices. A key recommendation is for IT leaders to implement a training and awareness program to address personnel issues. The implications for positive social change include IT leaders potential to secure students’, parents’, and faculty\u27s confidential information, thereby reducing IT protection costs and preventing identity theft