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Journal articles on the topic 'Malware (Software de computador)'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

Cahyaningrum, Yuliana, and Indrastanti Ratna Widiasari. "Analisis Performa Container Berplatform Docker atas SeranganMalicious Software (Malware)." Jurnal Buana Informatika 11, no. 1 (May 1, 2020): 47. http://dx.doi.org/10.24002/jbi.v11i1.3279.

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Abstract. As a new virtualization technology, many things about container technology need to be explored . One of them is data security issue when this technology is applied in a network. The study aims to discover a container performance when a server is being attacked by a malware. In this research, the container is installed natively on Windows Server 2016 and using Docker as the platform. Two groups of malware are used that each group has different effect on the server system. The results show that the malware used in this research does not affect the container performance yet it affects the network used by the container. The calculation results point out an increasing delay at HTTP protocol when the server is being attacked by malware group A which is from 0.028335 ms to 2.2698161 ms. The attack of group B malware on the server caused the website inside the container inaccessible. This is because group B malware also attacked the network server where the container is holding to.Keywords : Virtualization, Container, Malware, Native, Windows Server 2016, Docker.Abstrak. Sebagai teknologi virtualisasi yang baru, banyak hal yang perlu digali tentang teknologi container. Salah satunya adalah masalah keamanan data jika teknologi ini diterapkan dalam jaringan. Penelitian bertujuan untuk mengetahui performa container bila server mendapat serangan dari malware. Pada penelitian ini container dipasang secara native pada Windows Server 2016 dan menggunakan Docker sebagai platform. Dua kelompok malware digunakan dalam penelitian ini dimana setiap kelompok memiliki efek yang berbeda pada sistem server. Hasil menunjukkan bahwa malware yang digunakan dalam penelitian ini tidak mempengaruhi kinerja container, tetapi mempengaruhi network yang digunakan oleh container. Hasil penghitungan menunjukkan kenaikan delay pada protokol HTTP pada saat server mengalami serangan malware kelompok A yaitu dari 0.028335 ms sampai 2.2698161 ms. Serangan malware kelompok B pada server menyebabkan website yang ada di dalam container tersebut tidak dapat diakses. Hal ini disebabkan malware kelompok B juga menyerang network server dimana container tersebut menginduk.Kata Kunci : Virtualisasi, Container, Malware, Native, Windows Server 2016, Docker.

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Kalash, Mahmoud, Mrigank Rochan, Noman Mohammed, Neil Bruce, Yang Wang, and Farkhund Iqbal. "A Deep Learning Framework for Malware Classification." International Journal of Digital Crime and Forensics 12, no. 1 (January 2020): 90–108. http://dx.doi.org/10.4018/ijdcf.2020010105.

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In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.

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Bavishi, Ujaliben Kalpesh, and Bhavesh Madanlal Jain. "Malware Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (January 3, 2018): 27. http://dx.doi.org/10.23956/ijarcsse.v7i12.507.

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Malware, also known as malicious software affects the user’s computer system or mobile devices by exploiting the system’s vulnerabilities. It is a major threat to the security of the computer systems. Some of the types of malwares that are most commonly used are viruses, trojans, worms, etc. Nowadays, there is a widespread use of malware which allows malware author to get sensitive information like bank details, contact information which is a serious threat in the world. Most of the malwares are spread through internet because of its frequent use which can destroy large systems piercing through network. Hence, in this paper, we focus on analyzing malware using different tools which can analyze the malware in a restricted environment. Since many malware authors uses self-modifying code and obfuscation, it is very difficult for the traditional antivirus software to detect the malware which identifies that it is under scan and it can change its execution sequence. So, in order to address the shortcomings of the traditional antivirus software, we will be discussing some of the analysis tools which runs analysis on the malware in an effective manner and helps us to analyze the malware which can help us to protect our system’s information.

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Rani, Sangeeta, and Kanwalvir Singh Dhindsa. "Android application security: detecting Android malware and evaluating anti-malware software." International Journal of Internet Technology and Secured Transactions 10, no. 4 (2020): 491. http://dx.doi.org/10.1504/ijitst.2020.10028988.

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Rani, Sangeeta, and Kanwalvir Singh Dhindsa. "Android application security: detecting Android malware and evaluating anti-malware software." International Journal of Internet Technology and Secured Transactions 10, no. 4 (2020): 491. http://dx.doi.org/10.1504/ijitst.2020.108142.

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6

Pranoto, Wisnu. "Malicious Software Analysis." Cyber Security dan Forensik Digital 1, no. 2 (March 12, 2019): 62–66. http://dx.doi.org/10.14421/csecurity.2018.1.2.1374.

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Analisis sebuah malware pada perangkat android yang terdapat pada aplikasi iCalender menggunakan teknik analisis static dan teknik komputer forensik. Penelitian ini sangat berguna agar user dapat mengenal aplikasi android lainnya dan menghindari masuknya malware pada mernagkat android milik user. Berbagai tahapan analisis pada aplikasi android dimulai dengan merename apk menjadi zip, kemudian mengkestrak file Zip menjadi format Dex. Filte Dex dikonvert menjadi file Jar menggunakan tool Dex2jar. Selanjutnya hasil file Jar didecomplie dengan tool JD-GUI untuk melihat source code java pada aplikasi iCalender, kemudian dianalisis.

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Christodorescu, Mihai, Somesh Jha, Johannes Kinder, Stefan Katzenbeisser, and Helmut Veith. "Software transformations to improve malware detection." Journal in Computer Virology 3, no. 4 (July 26, 2007): 253–65. http://dx.doi.org/10.1007/s11416-007-0059-8.

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Zou, Deqing, Yueming Wu, Siru Yang, Anki Chauhan, Wei Yang, Jiangying Zhong, Shihan Dou, and Hai Jin. "IntDroid." ACM Transactions on Software Engineering and Methodology 30, no. 3 (May 2021): 1–32. http://dx.doi.org/10.1145/3442588.

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Android, the most popular mobile operating system, has attracted millions of users around the world. Meanwhile, the number of new Android malware instances has grown exponentially in recent years. On the one hand, existing Android malware detection systems have shown that distilling the program semantics into a graph representation and detecting malicious programs by conducting graph matching are able to achieve high accuracy on detecting Android malware. However, these traditional graph-based approaches always perform expensive program analysis and suffer from low scalability on malware detection. On the other hand, because of the high scalability of social network analysis, it has been applied to complete large-scale malware detection. However, the social-network-analysis-based method only considers simple semantic information (i.e., centrality) for achieving market-wide mobile malware scanning, which may limit the detection effectiveness when benign apps show some similar behaviors as malware. In this article, we aim to combine the high accuracy of traditional graph-based method with the high scalability of social-network-analysis--based method for Android malware detection. Instead of using traditional heavyweight static analysis, we treat function call graphs of apps as complex social networks and apply social-network--based centrality analysis to unearth the central nodes within call graphs. After obtaining the central nodes, the average intimacies between sensitive API calls and central nodes are computed to represent the semantic features of the graphs. We implement our approach in a tool called IntDroid and evaluate it on a dataset of 3,988 benign samples and 4,265 malicious samples. Experimental results show that IntDroid is capable of detecting Android malware with an F-measure of 97.1% while maintaining a True-positive Rate of 99.1%. Although the scalability is not as fast as a social-network-analysis--based method (i.e., MalScan ), compared to a traditional graph-based method, IntDroid is more than six times faster than MaMaDroid . Moreover, in a corpus of apps collected from GooglePlay market, IntDroid is able to identify 28 zero-day malware that can evade detection of existing tools, one of which has been downloaded and installed by more than ten million users. This app has also been flagged as malware by six anti-virus scanners in VirusTotal, one of which is Symantec Mobile Insight .

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Chevychelov, A. V., A. V. Burmistrov, and K. Yu Voyshhev. "Detecting malicious software using machine learning." Issues of radio electronics, no. 11 (November 20, 2019): 42–45. http://dx.doi.org/10.21778/2218-5453-2019-11-42-45.

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Today, most malware detection tools (Trojans): trojans, spyware, adware, worms, viruses, and ransomware are based on a signature approach that is ineffective for detecting polymorphs and malware whose signatures have not been recorded in antivirus database. This article explores methods for detecting opcodes in malware using machine learning algorithms. The study is carried on a Microsoft dataset containing 21653 examples of malicious code. The 20 most informative parameters based on the Fisher criterion are distinguished, methods for selecting parameters and various classifiers (logistic decision tree, random forest, naive Bayesian classifier, random tree) are compared, as a result of which an accuracy close to 100% is achieved.

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Stone, W. Ross. "When Your Anti-Malware Software Becomes Malware [From the Screen of Stone]." IEEE Antennas and Propagation Magazine 60, no. 4 (August 2018): 144–47. http://dx.doi.org/10.1109/map.2018.2840434.

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Bhaya, Wesam S., and Mustafa A. Ali. "Review on Malware and Malware Detection ‎Using Data Mining Techniques." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 25, no. 5 (November 29, 2017): 1585–601. http://dx.doi.org/10.29196/jub.v25i5.104.

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Malicious software is any type of software or codes which hooks some: private information, data from the computer system, computer operations or(and) merely just to do malicious goals of the author on the computer system, without permission of the computer users. (The short abbreviation of malicious software is Malware). However, the detection of malware has become one of biggest issues in the computer security field because of the current communication infrastructures are vulnerable to penetration from many types of malware infection strategies and attacks. Moreover, malwares are variant and diverse in volume and types and that strictly explode the effectiveness of traditional defense methods like signature approach, which is unable to detect a new malware. However, this vulnerability will lead to a successful computer system penetration (and attack) as well as success of more advanced attacks like distributed denial of service (DDoS) attack. Data mining methods can be used to overcome limitation of signature-based techniques to detect the zero-day malware. This paper provides an overview of malware and malware detection system using modern techniques such as techniques of data mining approach to detect known and unknown malware samples.

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Mutsenek, V. "Destructive software impacts in the context of information confrontation." National Security and Strategic Planning 2021, no. 1 (May 5, 2021): 111–16. http://dx.doi.org/10.37468/2307-1400-2021-1-111-116.

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Pope, Michael Brian, Merrill Warkentin, and Xin (Robert) Luo. "Evolutionary Malware." International Journal of Wireless Networks and Broadband Technologies 2, no. 3 (July 2012): 52–60. http://dx.doi.org/10.4018/ijwnbt.2012070105.

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Much as information systems themselves evolve and incorporate innovation, so too has malicious software, or “malware.” The increasing threat to those who use and trust in these systems is dangerous to overlook. This article examines recent trends in malware development. Reviewing statistics of dangerous infections of various malware families, it also expands on recent developments of actual exploit code. It further expands on the evolution of recent malware development techniques, particularly the use of malware development kits, or “exploit kits.” Mobile exploits taking advantage of smart phones, as well as malicious “polymorphic” code that self-mutates to evade detection are also discussed in detail.

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Zhang, Zhigang, Chaowen Chang, Peisheng Han, and Hongtao Zhang. "Packed malware variants detection using deep belief networks." MATEC Web of Conferences 309 (2020): 02002. http://dx.doi.org/10.1051/matecconf/202030902002.

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Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years. However, modern malware is often protected by software packers, obfuscation, and other technologies, which bring challenges to malware analysis and detection. In this paper, we propose a system call based malware detection technology. By comparing malware and benign software in a sandbox environment, a sensitive system call context is extracted based on information gain, which reduces obfuscation caused by a normal system call. By using the deep belief network, we train a malware detection model with sensitive system call context to improve the detection accuracy.

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Bai, Jin Rong, Shi Guang Mu, and Guo Zhong Zou. "The Application of Machine Learning to Study Malware Evolution." Applied Mechanics and Materials 530-531 (February 2014): 875–78. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.875.

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Malware evolves for the same reasons that ordinary software evolves. Like any other software product, the standard genetic operators selection, crossover and mutation are applied to evolve new malware. Recognizing and modeling how these malware evolve and are related is an important problem in the area of malware analysis. Grouping individual malware samples into malware families is not a new idea, and content-based comparison approaches have been proposed. Content-based approaches are hard to identify the real behavior of malware and it is inherently susceptible to inaccuracies due to polymorphic and metamorphic techniques. In this paper, we leveraged dynamic analysis approach to classify malware variants. The results demonstrate that our technique is able to recognize and group malware programs that behave similarly, achieving a better precision than previous approaches. The major advantage of our approach is that it can precisely tracks the sensitive information of malware behavior and is immune to obfuscation attempts. Our research is conducive to study the problem of malware classification, malware naming, and the phylogeny of malware.

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Andrade da Silva, Evandro. "SOFTWARE LIVRE." Revista Dissertar 1, no. 16 e 17 (June 1, 2009): 67–70. http://dx.doi.org/10.24119/16760867ed1894.

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Com a popularização do computador e o acesso à internet através do computador pessoal, cada vez mais se tem acesso a um enorme conteúdo de sites, nos quais a cultura e a informação estão ás vistas de todos. Existem os Partidários, que devem ser elogiados, que ao mencionarem dispositivo constitucional no qual explicita que a informação é um direito de todo brasileiro, portanto o direito autoral, segundo alguns doutrinadores ficaria prejudicado diante daquele preceito constitucional de que todos têm direito à informação e à cultura na grande rede. Recentemente até vimos novas regras com relação à propaganda eleitoral quebrando paradigmas e extrapolando o que dantes a lei colocava numa coleira ou numa redoma, agora poderão ser livremente tratados através de propaganda eleitoral, mas, na grande rede. Fala-se tanto em software livre, opensource, freeware, shareware e várias outras definições, mas a grande maioria não sabe de seu direito e quais são os direitos de seus originadores, programadores e o que estes imputam aos usuários.

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GIERSZEWSKI, Tomasz. "Malware - Malicious Software in IT/OT Systems." AUTOMATYKA, ELEKTRYKA, ZAKLOCENIA 7, no. 4(26)2016 (December 31, 2016): 158–69. http://dx.doi.org/10.17274/aez.2016.26.09.

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Moussas, Vassilios, and Antonios Andreatos. "Malware Detection Based on Code Visualization and Two-Level Classification." Information 12, no. 3 (March 11, 2021): 118. http://dx.doi.org/10.3390/info12030118.

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Malware creators generate new malicious software samples by making minor changes in previously generated code, in order to reuse malicious code, as well as to go unnoticed from signature-based antivirus software. As a result, various families of variations of the same initial code exist today. Visualization of compiled executables for malware analysis has been proposed several years ago. Visualization can greatly assist malware classification and requires neither disassembly nor code execution. Moreover, new variations of known malware families are instantly detected, in contrast to traditional signature-based antivirus software. This paper addresses the problem of identifying variations of existing malware visualized as images. A new malware detection system based on a two-level Artificial Neural Network (ANN) is proposed. The classification is based on file and image features. The proposed system is tested on the ‘Malimg’ dataset consisting of the visual representation of well-known malware families. From this set some important image features are extracted. Based on these features, the ANN is trained. Then, this ANN is used to detect and classify other samples of the dataset. Malware families creating a confusion are classified by a second level of ANNs. The proposed two-level ANN method excels in simplicity, accuracy, and speed; it is easy to implement and fast to run, thus it can be applied to antivirus software, smart firewalls, web applications, etc.

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Liu, Lan, Ryan K. L. Ko, Guangming Ren, and Xiaoping Xu. "Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks." Security and Communication Networks 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2910310.

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As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q<qc. The results showed that our model is effective, and the results may help to decide the SDN control strategy to defend against network malware and provide a theoretical basis to reduce and prevent network security incidents.

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Zhao, Yanjie, Li Li, Haoyu Wang, Haipeng Cai, Tegawendé F. Bissyandé, Jacques Klein, and John Grundy. "On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection." ACM Transactions on Software Engineering and Methodology 30, no. 3 (May 2021): 1–38. http://dx.doi.org/10.1145/3446905.

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Malware detection at scale in the Android realm is often carried out using machine learning techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to yield high detection rates when assessed against well-known datasets. Unfortunately, such datasets may include a large portion of duplicated samples, which may bias recorded experimental results and insights. In this article, we perform extensive experiments to measure the performance gap that occurs when datasets are de-duplicated. Our experimental results reveal that duplication in published datasets has a limited impact on supervised malware classification models. This observation contrasts with the finding of Allamanis on the general case of machine learning bias for big code. Our experiments, however, show that sample duplication more substantially affects unsupervised learning models (e.g., malware family clustering). Nevertheless, we argue that our fellow researchers and practitioners should always take sample duplication into consideration when performing machine-learning-based (via either supervised or unsupervised learning) Android malware detections, no matter how significant the impact might be.

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Rodriguez, Miranda. "All Your IP Are Belong to Us." Texas A&M Law Review 3, no. 3 (May 2016): 663–89. http://dx.doi.org/10.37419/lr.v3.i3.7.

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The cybersecurity and cybercrime industries are tied together in an arms race where both seek out new security vulnerabilities to exploit on offense or to remediate on defense. Malware (malicious software) offers one of the primary weapons pioneering new computer technologies on both sides. However, the average Internet user sees malware at best as an annoyance that is merely the price of surfing the web. It is clear that cybersecurity is a business and a successful one. The cybersecurity industry maintains copyrights and patents on our cyber defense technologies— antivirus software, firewalls, intrusion prevention systems, and more. There are no federal copyrights and patents on malware, even regarding the cybersecurity industry’s creations. From an intellectual property perspective, there is no difference between ordinary software and malicious software. Malware, as offensive software, can and should be protected, just as we protect our defensive software.

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Waliulu, Raditya Faisal, and Teguh Hidayat Iskandar Alam. "Reverse Engineering Analysis Statis Forensic Malware Webc2-Div." Insect (Informatics and Security): Jurnal Teknik Informatika 4, no. 1 (August 23, 2019): 15. http://dx.doi.org/10.33506/insect.v4i1.223.

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At this paper focus on Malicious Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successful user to realized about malware infect. This research about reverse engineering. A few steps there are scanning, suspected packet in network and analysis of malware behavior and disassembler body malware.

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Kong, Deguang, and Guanhua Yan. "Discriminant malware distance learning on structuralinformation for automated malware classification." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (June 14, 2013): 347–48. http://dx.doi.org/10.1145/2494232.2465531.

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Setia, Tesa Pajar, Aldy Putra Aldya, and Nur Widiyasono. "Reverse Engineering untuk Analisis Malware Remote Access Trojan." Jurnal Edukasi dan Penelitian Informatika (JEPIN) 5, no. 1 (April 23, 2019): 40. http://dx.doi.org/10.26418/jp.v5i1.28214.

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Para hacker menggunakan malware Remote Access Trojan untuk merusak sistem kemudian mencuri data para korbannya. Diperlukan analisis mendalam mengenai malware baru-baru ini karena malware dapat berkamuflase seperti sistem tidak dicurigai. Penggunaan teknik basic analysis sangat tergantung pada perilaku malware yang dianalisis, analisis akan sulit ketika ditemukan malware baru yang menggunakan suatu teknik baru. Reverse engineering merupakan salah satu solusi untuk melakukan analisis malware karena menggunakan teknik reverse engineering kode pada malware dapat diketahui. Malware Flawed ammyy ini merupakan software yang disalahgunakan dari Ammyy Admin versi 3 oleh hacker TA505. Penelitian ini bertujuan untuk bagaimana alur untuk melakukkan identikasi malware kususnya malware RAT dengan teknik reverse engineering dan tools yang bias digunakan. Penelitian ini menggunakan metodologi deskriptif,. Hasil dari penelitian menunjukan bahwa alur untuk melakukan reverse engineering dan tools yang dapat digunakan.

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Yang, Hongyu, and Ruiwen Tang. "Power Consumption Based Android Malware Detection." Journal of Electrical and Computer Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/6860217.

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In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM) was built by using Mel frequency cepstral coefficients (MFCC). Then, the GMM was used to analyze power consumption; malicious software can be classified and detected through classification processing. Experiment results demonstrate that the function of an application and its power consumption have a close relationship, and our method can detect some typical malicious application software accurately.

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Hosseini, Soodeh, Mohammad Abdollahi Azgomi, and Adel Torkaman Rahmani. "Malware propagation modeling considering software diversity and immunization." Journal of Computational Science 13 (March 2016): 49–67. http://dx.doi.org/10.1016/j.jocs.2016.01.002.

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Jo, Min Jae, and Ji Sun Shin. "MWMon: A Software Defined Network-based Malware Monitor." Journal of the Korea Industrial Information Systems Research 20, no. 5 (October 31, 2015): 37–44. http://dx.doi.org/10.9723/jksiis.2015.20.5.037.

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Mowbray, Miranda. "Moral Status for Malware! The Difficulty of Defining Advanced Artificial Intelligence." Cambridge Quarterly of Healthcare Ethics 30, no. 3 (June 10, 2021): 517–28. http://dx.doi.org/10.1017/s0963180120001061.

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AbstractThe suggestion has been made that future advanced artificial intelligence (AI) that passes some consciousness-related criteria should be treated as having moral status, and therefore, humans would have an ethical obligation to consider its well-being. In this paper, the author discusses the extent to which software and robots already pass proposed criteria for consciousness; and argues against the moral status for AI on the grounds that human malware authors may design malware to fake consciousness. In fact, the article warns that malware authors have stronger incentives than do authors of legitimate software to create code that passes some of the criteria. Thus, code that appears to be benign, but is in fact malware, might become the most common form of software to be treated as having moral status.

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Wu, Bozhi, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, and Michael R. Lyu. "Why an Android App Is Classified as Malware." ACM Transactions on Software Engineering and Methodology 30, no. 2 (March 2021): 1–29. http://dx.doi.org/10.1145/3423096.

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Machine learning–(ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly used features. In practice, most of the ML classifications only provide a binary label to mobile users and app security analysts. However, stakeholders are more interested in the reason why apps are classified as malicious in both academia and industry. This belongs to the research area of interpretable ML but in a specific research domain (i.e., mobile malware detection). Although several interpretable ML methods have been exhibited to explain the final classification results in many cutting-edge Artificial Intelligent–based research fields, until now, there is no study interpreting why an app is classified as malware or unveiling the domain-specific challenges. In this article, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal ) to classify malware with high accuracy and explain the classification result meanwhile. (1) The first classification phase of XMal hinges multi-layer perceptron and attention mechanism and also pinpoints the key features most related to the classification result. (2) The second interpreting phase aims at automatically producing neural language descriptions to interpret the core malicious behaviors within apps. We evaluate the behavior description results by leveraging a human study and an in-depth quantitative analysis. Moreover, we further compare XMal with the existing interpretable ML-based methods (i.e., Drebin and LIME) to demonstrate the effectiveness of XMal . We find that XMal is able to reveal the malicious behaviors more accurately. Additionally, our experiments show that XMal can also interpret the reason why some samples are misclassified by ML classifiers. Our study peeks into the interpretable ML through the research of Android malware detection and analysis.

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Arslan, Recep Sinan. "AndroAnalyzer: android malicious software detection based on deep learning." PeerJ Computer Science 7 (May 10, 2021): e533. http://dx.doi.org/10.7717/peerj-cs.533.

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Background Technological developments have a significant effect on the development of smart devices. The use of smart devices has become widespread due to their extensive capabilities. The Android operating system is preferred in smart devices due to its open-source structure. This is the reason for its being the target of malware. The advancements in Android malware hiding and detection avoidance methods have overridden traditional malware detection methods. Methods In this study, a model employing AndroAnalyzer that uses static analysis and deep learning system is proposed. Tests were carried out with an original dataset consisting of 7,622 applications. Additional tests were conducted with machine learning techniques to compare it with the deep learning method using the obtained feature vector. Results Accuracy of 98.16% was achieved by presenting a better performance compared to traditional machine learning techniques. Values of recall, precision, and F-measure were 98.78, 99.24 and 98.90, respectively. The results showed that deep learning models using trace-based feature vectors outperform current cutting-edge technology approaches.

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Nguyen, Khoi Tan. "DEVELOPING A MALWARE ANALYSIS SYSTEM ON DISTRIBUTED ENVIRONMENT." Journal of Science and Technology: Issue on Information and Communications Technology 3, no. 2 (December 31, 2017): 41. http://dx.doi.org/10.31130/jst.2017.52.

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Malware is one of the major threats on the Internet today. To protect from the rapid propagation of malware in the network, we need to focus on determining how malware can be analyzed, detected, and blocked analyze and detect. As distributed processing model have been recently developed due to the cloud computing platform and the cluster filesystem, they could be usefully applied to analyzing malware. In this paper, we propose a malware analysis method based on the MapReduce software framework of the distributed processing platform. The proposed solution allows to reduce the time of analyzing and identifying malware. The experimental results show that the MapReduce-based flow analysis method improves the performance when analyzing a large number of malware.

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Heiser, Jay G. "Understanding today's malware." Information Security Technical Report 9, no. 2 (April 2004): 47–64. http://dx.doi.org/10.1016/s1363-4127(04)00025-1.

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Anscombe, T. "Mobile Malware Menace." ITNOW 56, no. 1 (February 24, 2014): 14–15. http://dx.doi.org/10.1093/itnow/bwu006.

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Barbosa, Bernardo Bucher B., and Júlio César Silva. "Interação Humano - Computador usando Visão Computacional." Revista Eletrônica TECCEN 2, no. 1 (April 1, 2009): 09. http://dx.doi.org/10.21727/198409932009.teccen.v2i1.09-16.

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Este trabalho visa estudar maneiras de se explorar a Interação Humano Computador, usando Visão Computacional. A idéia tem como objetivo um esforço para tornar o computador mais interativo com o usuário, sem a necessidade da compra de um hardware ou acessório específico para tal. O produto final deste trabalho em desenvolvimento é um software que contempla esta funcionalidade, tornando o computador mais interativo.

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Barbosa, Bernardo Bucher B., and Júlio César Silva. "Interação Humano - Computador usando Visão Computacional." Revista Eletrônica TECCEN 2, no. 1 (October 3, 2016): 09. http://dx.doi.org/10.21727/teccen.v2i1.223.

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Este trabalho visa estudar maneiras de se explorar a Interação Humano Computador, usando Visão Computacional. A idéia tem como objetivo um esforço para tornar o computador mais interativo com o usuário, sem a necessidade da compra de um hardware ou acessório específico para tal. O produto final deste trabalho em desenvolvimento é um software que contempla esta funcionalidade, tornando o computador mais interativo.

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Barbosa, Bernardo Bucher B., and Júlio César Silva. "Interação Humano - Computador usando Visão Computacional." Revista Eletrônica TECCEN 2, no. 1 (April 1, 2009): 09. http://dx.doi.org/10.21727/teccen.v2i1.71.

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Este trabalho visa estudar maneiras de se explorar a Interação Humano Computador, usando Visão Computacional. A idéia tem como objetivo um esforço para tornar o computador mais interativo com o usuário, sem a necessidade da compra de um hardware ou acessório específico para tal. O produto final deste trabalho em desenvolvimento é um software que contempla esta funcionalidade, tornando o computador mais interativo.

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Basole, Samanvitha, Fabio Di Troia, and Mark Stamp. "Multifamily malware models." Journal of Computer Virology and Hacking Techniques 16, no. 1 (January 10, 2020): 79–92. http://dx.doi.org/10.1007/s11416-019-00345-8.

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Crutchfield, Steve. "Outsmarting the New Malware." EDPACS 33, no. 9 (March 2006): 18–20. http://dx.doi.org/10.1201/1079.07366981/45851.33.9.20060301/92239.3.

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Plucar, Jan, Jiří Frank, Daniel Walter, and Ivan Zelinka. "Intelligent Malware - Trends and Possibilities." MENDEL 27, no. 1 (June 21, 2021): 18–22. http://dx.doi.org/10.13164/mendel.2021.1.018.

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In recent months and years, with more and more computers and computer systems becoming the target of cyberattacks. These attacks are gaining strength and the sophistication of the approach in terms of how to attack. Attackers and Defenders are increasingly using artificial intelligence methods to maximize the success of their actions. For a successful defence, we must be able to anticipate future threats that may come. For these reasons, our research group is engaged in creating experimental software with artificial intelligence to test the possibilities and capabilities of such malware in the event of its deployment. This software has not only malware capabilities but also antimalware and can be used on both sides. This article introduces the reader to the main principles of our design, which can serve as a future platform for cyber defence systems.

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Song, Fu, and Tayssir Touili. "Pushdown model checking for malware detection." International Journal on Software Tools for Technology Transfer 16, no. 2 (September 30, 2013): 147–73. http://dx.doi.org/10.1007/s10009-013-0290-1.

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Muhtadi, Adib Fakhri, and Ahmad Almaarif. "Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique." International Journal of Advances in Data and Information Systems 1, no. 1 (April 1, 2020): 17–25. http://dx.doi.org/10.25008/ijadis.v1i1.14.

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Malware is a software or computer program that is used to carry out malicious activity. Malware is made with the aim of harming user’s device because it can change user’s data, use up bandwidth and other resources without user's permission. Some research has been done before to identify the type of malware and its effects. But previous research only focused on grouping the types of malware that attack via network traffic. This research analyzes the impact of malware on network traffic using behavior-based detection techniques. This technique analyzes malware by running malware samples into an environment and monitoring the activities caused by malware samples. To obtain accurate results, the analysis is carried out by retrieving API call network information and network traffic activities. From the analysis of the malware API call network, information is generated about the order of the API call network used by malware. Using the network traffic, obtained malware activities by analyzing the behavior of network traffic malware, payload, and throughput of infected traffic. Furthermore, the results of the API call network sequence used by malware and the results of network traffic analysis, are analyzed so that the impact of malware on network traffic can be determined.

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Muhtadi, Adib Fakhri, and Ahmad Almaarif. "Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique." International Journal of Advances in Data and Information Systems 1, no. 1 (March 9, 2020): 17–25. http://dx.doi.org/10.25008/ijadis.v1i1.8.

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Malware is a software or computer program that is used to carry out malicious activity. Malware is made with the aim of harming users because it can change users' data, use up bandwidth and other resources without the user's permission. Some research has been done before to identify the type of malware and its effects. But previous research only focused on grouping the types of malware that attack via network traffic. P. This research analyzes the impact of malware on network traffic using behavior-based detection techniques. This technique analyzes malware by running malware samples into an environment and monitoring the activities caused by malware samples. To obtain accurate results, the analysis is carried out by retrieving API call network information and network traffic activities. From the analysis of the malware call network API , information is generated about the order of the call network API used by malware . Then from the network traffic, obtained malware activities by analyzing the behavior of network traffic malware, payload, and bandwidth of infected traffic. Furthermore, the results of the call network API sequence used by malware and the results of network traffic analysis, are analyzed so that the impact of malware can be determined on network traffic.

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Al-Marghilani, A. "Comprehensive Analysis of IoT Malware Evasion Techniques." Engineering, Technology & Applied Science Research 11, no. 4 (August 21, 2021): 7495–500. http://dx.doi.org/10.48084/etasr.4296.

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Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.

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Marcello, Mario. "Collecting Malware in Swiss German University with Low Energy and Cost Computer." Journal of Applied Information, Communication and Technology 5, no. 2 (October 28, 2018): 85–91. http://dx.doi.org/10.33555/ejaict.v5i2.57.

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The malware spreads massively in Indonesia. The security in Information Technology doesn’t seem to become a top priority for Indonesian. The use of pirated software is still high, although it is the biggest threat and entrance for the malwares to attacks. This paper shows how to collect a spreading malware in a system to know the malware trends that exist. So, the owner may know the malware trends inside his system and he can countermeasure the attacks. To collect the malwares, I use the Dionaea, the honeypot to collect malware and implement it to Raspberry Pi. Raspberry Pi is a small, low cost and low energy consumption computer. By using Raspberry Pi to collect malware, we can minimize budget, save the energy and space.

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Ashik, Mathew, A. Jyothish, S. Anandaram, P. Vinod, Francesco Mercaldo, Fabio Martinelli, and Antonella Santone. "Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms." Electronics 10, no. 14 (July 15, 2021): 1694. http://dx.doi.org/10.3390/electronics10141694.

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Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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Bai, Jinrong, Qibin Shi, and Shiguang Mu. "A Malware and Variant Detection Method Using Function Call Graph Isomorphism." Security and Communication Networks 2019 (September 22, 2019): 1–12. http://dx.doi.org/10.1155/2019/1043794.

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The huge influx of malware variants are generated using packing and obfuscating techniques. Current antivirus software use byte signature to identify known malware, and this method is easy to be deceived and generally ineffective for identifying malware variants. Antivirus experts use hash signature to verify if captured sample is one of the malware databases, and this method cannot recognize malware variants whose hash signatures have changed completely. Function call graph is a high-level abstraction representation of a program and more stable and resilient than byte or hash signature. In this paper, function call graph is used as signature of a program, and two kinds of graph isomorphism algorithms are employed to identify known malware and its variants. Four experiments are designed to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is effective and efficient for identifying known malware and a portion of their variants. The proposed method can also be used to index and locate a large-scale malware database and group malware to the corresponding family.

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Rossinskaya, Elena, and Igor Ryadovskiy. "The Concept of Malware as a Means of Committing Computer Crimes: Classification and Methods of Illegal Use." Russian Journal of Criminology 14, no. 5 (November 20, 2020): 699–709. http://dx.doi.org/10.17150/2500-4255.2020.14(5).699-709.

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The authors analyze problems connected with malware from the standpoint of the doctrine of the methods of computer crimes/offenses as one of the components of the theory of information-computer support of criminalistic work. Most methods of computer crimes are based on the unauthorized access to computer facilities and systems gained through malware that, in fact, acts as a weapon of crime. The authors present a classification of malware based on different parameters: from the standpoint of criminal law and criminology; the standpoint of information technology; the standpoint of the doctrine of computer crimes/offenses. Various grounds for the classification of malware are examined. A general classification, widely used by the developers of antiviral software, includes virus-programs, worm-programs and trojan-programs. In the modern situation of massive digitization, it is not practical to regard masquerading as a legitimate file as a dominant feature of trojan software. On the contrary, criminals try hard to hide from the user the downloading, installation and activity of malware that cannot self-propagate. The key method of propagating trojan programs is sending mass emails with attachments masquerading as useful content. The classification of malware by the way and method of propagation - viruses, worms and trojan programs - is only currently used due to traditions and does not reflect the essence of the process. A different classification of malware into autonomous, semi-autonomous and non-autonomous programs is based on the possibility of their autonomous functioning. At present there is practically no malware whose functions include only one specific type of actions, most of it contains a combination of various types of actions implemented through module architecture, which offers criminals wide opportunities for manipulating information. The key mechanisms of malwares work are described and illustrated through examples. Special attention is paid to harmful encryption software working through stable cryptographic algorithms - ransomware, when criminals demand ransom for restoring data. There is no criminal liability for such theft. The authors outline the problems connected with the possibility of the appearance of new malware that would affect cloud resources.

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Ming, Jiang, Zhi Xin, Pengwei Lan, Dinghao Wu, Peng Liu, and Bing Mao. "Impeding behavior-based malware analysis via replacement attacks to malware specifications." Journal of Computer Virology and Hacking Techniques 13, no. 3 (May 31, 2016): 193–207. http://dx.doi.org/10.1007/s11416-016-0281-3.

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Olawale Surajudeen, Adebayo. "Malware Detection, Supportive Software Agents and Its Classification Schemes." International Journal of Network Security & Its Applications 4, no. 6 (November 30, 2012): 33–49. http://dx.doi.org/10.5121/ijnsa.2012.4603.

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Brody, Richard G., Harold U. Chang, and Erich S. Schoenberg. "Malware at its worst: death and destruction." International Journal of Accounting & Information Management 26, no. 4 (October 1, 2018): 527–40. http://dx.doi.org/10.1108/ijaim-04-2018-0046.

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Purpose Most people are probably aware of malware, but they may not be aware of malware in what may be its most dangerous form, i.e. causing physical harm, even death, to individuals. This paper aims to document how software can cause malicious harm to individuals by attacking modern systems that appear to be neglected and under-researched. Design/methodology/approach This paper will review some of the most significant areas of concern with respect to end of days malware, i.e. malware that has a dangerous intent. The areas included are automobiles, medical devices and air traffic control systems. Findings The potential harmful effects of malware are often not well known by consumers and businesses around the world. These issues are not limited to just financial harm. Lives can actually be in danger. Underestimating the importance of cybersecurity and understanding the dangers that are associated with advancing technology are global issues that will continue unless there is enough awareness to force businesses and governments to address these issues. It is critical that safeguards are established. Originality/value While many papers have been written about malware and the implications of having malicious software infect a computer or a network, little attention has been paid to “end of days” malware. With advancing technology, malware now has the ability to cause serious injury or death to individuals who have minimal or no knowledge of the potential consequences of, for example, driving in an automobile, wearing or having an internal medical device or flying on an airplane. It is up to businesses and governments to address these issues.

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