Thematische Bibliographien / Identify malware

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Auswahl der wissenschaftlichen Literatur zum Thema „Identify malware“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 10. Februar 2022

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Zeitschriftenartikel zum Thema "Identify malware"

Suryati, One Tika, und Avon Budiono. „Impact Analysis of Malware Based on Call Network API With Heuristic Detection Method“. International Journal of Advances in Data and Information Systems 1, Nr. 1 (01.04.2020): 1–8. http://dx.doi.org/10.25008/ijadis.v1i1.176.

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Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user's knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites.

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Yuswanto, Andrie, und Budi Wibowo. „A SYSTEMATIC REVIEW METHOD FOR SECURITY ANALYSIS OF INTERNET OF THINGS ON HONEYPOT DETECTION“. TEKNOKOM 4, Nr. 1 (24.05.2021): 16–20. http://dx.doi.org/10.31943/teknokom.v4i1.54.

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A very significant increase in the spread of malware has resulted in malware analysis. A recent approach to using the internet of things has been put forward by many researchers. Iot tool learning approaches as a more effective and efficient approach to dealing with malware compared to conventional approaches. At the same time, the researchers transformed the honeypot as a device capable of gathering malware information. The honeypot is designed as a malware trap and is stored on the provided system. Then log the managed events and gather information about the activity and identity of the attacker. This paper aims to use a honeypot in machine learning to deal with malware The Systematic Literature Review (SLR) method was used to identify 207. Then 10 papers were selected to be investigated based on inclusion and exclusion criteria. . The technique used by most researchers is to utilize the available honeypot dataset. Meanwhile, based on the type of malware being analyzed, honeypot in machine learning is mostly used to collect IoT-based malware.

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Bai, Jinrong, Qibin Shi und Shiguang Mu. „A Malware and Variant Detection Method Using Function Call Graph Isomorphism“. Security and Communication Networks 2019 (22.09.2019): 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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Bai, Jin Rong, Shi Guang Mu und Guo Zhong Zou. „The Application of Machine Learning to Study Malware Evolution“. Applied Mechanics and Materials 530-531 (Februar 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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Et. al., Balal Sohail. „Macro Based Malware Detection System“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, Nr. 3 (10.04.2021): 5776–87. http://dx.doi.org/10.17762/turcomat.v12i3.2254.

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Macro based Malware has taken a great rise is these recent years, Attackers are now using this malware for hacking purposes. This virus is embedded inside the macro of a word document and can be used to infect the victim’s machine. These infected files are usually sent through emails and all antivirus software are unable to detect the virus due to the format of the file. Due to the format being a rich text file and not an executable file, the infected file is able to bypass all security. Hence it is necessary to develop a detection system for such attacks to help reduce the threat. Technical research is carried out to identify the tools and techniques essential in the completion of this system. Research on methodology is done to finalise which development cycle will be used and how functions will be carried out at each phase of the development cycle. This paper outlines the problems that people face once they are attacked through macro malwares and the way it can be mitigated. Lastly, all information necessary to start the implementation has been gathered and analysed

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Susanto, Susanto, M. Agus Syamsul Arifin, Deris Stiawan, Mohd Yazid Idris und Rahmat Budiarto. „The trend malware source of IoT network“. Indonesian Journal of Electrical Engineering and Computer Science 22, Nr. 1 (01.04.2021): 450. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp450-459.

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<span>Malware may disrupt the internet of thing (IoT) system/network when it resides in the network, or even harm the network operation. Therefore, malware detection in the IoT system/network becomes an important issue. Research works related to the development of IoT malware detection have been carried out with various methods and algorithms to increase detection accuracy. The majority of papers on malware literature studies discuss mobile networks, and very few consider malware on IoT networks. This paper attempts to identify problems and issues in IoT malware detection presents an analysis of each step in the malware detection as well as provides alternative taxonomy of literature related to IoT malware detection. The focuses of the discussions include malware repository dataset, feature extraction methods, the detection method itself, and the output of each conducted research. Furthermore, a comparison of malware classification approaches accuracy used by researchers in detecting malware in IoT is presented.</span>

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Muhtadi, Adib Fakhri, und Ahmad Almaarif. „Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique“. International Journal of Advances in Data and Information Systems 1, Nr. 1 (01.04.2020): 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, und Ahmad Almaarif. „Analysis of Malware Impact on Network Traffic using Behavior-based Detection Technique“. International Journal of Advances in Data and Information Systems 1, Nr. 1 (09.03.2020): 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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Martín, Ignacio, José Alberto Hernández, Alfonso Muñoz und Antonio Guzmán. „Android Malware Characterization Using Metadata and Machine Learning Techniques“. Security and Communication Networks 2018 (08.07.2018): 1–11. http://dx.doi.org/10.1155/2018/5749481.

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Android malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and metadata to identify patterns in malware applications. Our experiments show the following: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android markets are more relevant in detecting malware, such as the application developer and certificate issuer; and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.

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Kalash, Mahmoud, Mrigank Rochan, Noman Mohammed, Neil Bruce, Yang Wang und Farkhund Iqbal. „A Deep Learning Framework for Malware Classification“. International Journal of Digital Crime and Forensics 12, Nr. 1 (Januar 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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Mehr Quellen

Dissertationen zum Thema "Identify malware"

Varga, Adam. „Identifikace a charakterizace škodlivého chování v grafech chování“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442388.

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Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.

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Nguyen, Sao Linh. „Bezpečnostní rizika sociálních sítí a jejich prevence“. Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2018. http://www.nusl.cz/ntk/nusl-378363.

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This diploma thesis deals with the issue of security risks and threats of social networks. The work includes basic information about the most popular online social networks such as Facebook, Twitter and Instagram. The work analyzes the development and use of the above mentioned networks. In addition, there are the risks and dangers of communicating on social networks and recommendations for safe use.

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Bücher zum Thema "Identify malware"

Phishing Exposed. Syngress, 2005.

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Buchteile zum Thema "Identify malware"

Bellizzi, Jennifer, Mark Vella, Christian Colombo und Julio Hernandez-Castro. „Real-Time Triggering of Android Memory Dumps for Stealthy Attack Investigation“. In Secure IT Systems, 20–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70852-8_2.

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AbstractAttackers regularly target Android phones and come up with new ways to bypass detection mechanisms to achieve long-term stealth on a victim’s phone. One way attackers do this is by leveraging critical benign app functionality to carry out specific attacks.In this paper, we present a novel generalised framework, JIT-MF (Just-in-time Memory Forensics), which aims to address the problem of timely collection of short-lived evidence in volatile memory to solve the stealthiest of Android attacks. The main components of this framework are i) Identification of critical data objects in memory linked with critical benign application steps that may be misused by an attacker; and ii) Careful selection of trigger points, which identify when memory dumps should be taken during benign app execution.The effectiveness and cost of trigger point selection, a cornerstone of this framework, are evaluated in a preliminary qualitative study using Telegram and Pushbullet as the victim apps targeted by stealthy malware. Our study identifies that JIT-MF is successful in dumping critical data objects on time, providing evidence that eludes all other forensic sources. Experimentation offers insight into identifying categories of trigger points that can strike a balance between the effort required for selection and the resulting effectiveness and storage costs. Several optimisation measures for the JIT-MF tools are presented, considering the typical resource constraints of Android devices.

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Russel, Md Omar Faruque Khan, Sheikh Shah Mohammad Motiur Rahman und Takia Islam. „A Large-Scale Investigation to Identify the Pattern of Permissions in Obfuscated Android Malwares“. In Cyber Security and Computer Science, 85–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52856-0_7.

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Russel, Md Omar Faruque Khan, Sheikh Shah Mohammad Motiur Rahman und Takia Islam. „A Large-Scale Investigation to Identify the Pattern of App Component in Obfuscated Android Malwares“. In Communications in Computer and Information Science, 513–26. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6318-8_42.

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Sethuraman, Murugan Sethuraman. „Survey of Unknown Malware Attack Finding“. In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 260–76. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3129-6.ch011.

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Intrusion detection system(IDS) has played a vital role as a device to guard our networks from unknown malware attacks. However, since it still suffers from detecting an unknown attack, i.e., 0-day attack, the ultimate challenge in intrusion detection field is how we can precisely identify such an attack. This chapter will analyze the various unknown malware activities while networking, internet or remote connection. For identifying known malware various tools are available but that does not detect Unknown malware exactly. It will vary according to connectivity and using tools and finding strategies what they used. Anyhow like known Malware few of unknown malware listed according to their abnormal activities and changes in the system. In this chapter, we will see the various Unknown methods and avoiding preventions as birds eye view manner.

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Sethuraman, Murugan Sethuraman. „Survey of Unknown Malware Attack Finding“. In Intelligent Systems, 2227–43. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch099.

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Sharma, Kavita, und B. B. Gupta. „Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches“. In Research Anthology on Securing Mobile Technologies and Applications, 645–66. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8545-0.ch036.

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Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

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Luo, Xin, und Merrill Warkentin. „Developments and Defenses of Malicious Code“. In Encyclopedia of Multimedia Technology and Networking, Second Edition, 356–63. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch049.

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The continuous evolution of information security threats, coupled with increasing sophistication of malicious codes and the greater flexibility in working practices demanded by organizations and individual users, have imposed further burdens on the development of effective anti-malware defenses. Despite the fact that the IT community is endeavoring to prevent and thwart security threats, the Internet is perceived as the medium that transmits not only legitimate information but also malicious codes. In this cat-and-mouse predicament, it is widely acknowledged that, as new security countermeasures arise, malware authors are always able to learn how to manipulate the loopholes or vulnerabilities of these technologies, and can thereby weaponize new streams of malicious attacks. From e-mail attachments embedded with Trojan horses to recent advanced malware attacks such as Gozi programs, which compromise and transmit users’ highly sensitive information in a clandestine way, malware continues to evolve to be increasingly surreptitious and deadly. This trend of malware development seems foreseeable, yet making it increasingly arduous for organizations and/or individuals to detect and remove malicious codes and to defend against profit-driven perpetrators in the cyber world. This article introduces new malware threats such as ransomware, spyware, and rootkits, discusses the trends of malware development, and provides analysis for malware defenses. Keywords: Ransomware, Spyware, Anti-Virus, Malware, Malicious Code, Background Various forms of malware have been a part of the computing environment since before the implementation of the public Internet. However, the Internet’s ubiquity has ushered in an explosion in the severity and complexity of various forms of malicious applications delivered via increasingly ingenious methods. The original malware attacks were perpetrated via e-mail attachments, but new vulnerabilities have been identified and exploited by a variety of perpetrators who range from merely curious hackers to sophisticated organized criminals and identify thieves. In an earlier manuscript (Luo & Warkentin, 2005), the authors established the basic taxonomy of malware that included various types of computer viruses (boot sector viruses, macro viruses, etc.), worms, and Trojan horses. Since that time, numerous new forms of malicious code have been found “in the wild.”

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Rajkumar, Manokaran Newlin, Varadhan Venkatesa Kumar und Ramachandhiran Vijayabhasker. „A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning“. In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 176–94. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch009.

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This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

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Rajkumar, Manokaran Newlin, Varadhan Venkatesa Kumar und Ramachandhiran Vijayabhasker. „A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning“. In Research Anthology on Securing Mobile Technologies and Applications, 626–44. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8545-0.ch035.

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Narayan, Valliammal, und Barani Shaju. „Malware and Anomaly Detection Using Machine Learning and Deep Learning Methods“. In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 104–31. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch006.

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This chapter aims to discuss applications of machine learning in cyber security and explore how machine learning algorithms help to fight cyber-attacks. Cyber-attacks are wide and varied in multiple forms. The key benefit of machine learning algorithms is that it can deep dive and analyze system behavior and identify anomalies which do not correlate with expected behavior. Algorithms can be trained to observe multiple data sets and strategize payload beforehand in detection of malware analysis.

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Konferenzberichte zum Thema "Identify malware"

Tam, Geran, und Aaron Hunter. „Machine Learning to Identify Android Malware“. In 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2018. http://dx.doi.org/10.1109/uemcon.2018.8796795.

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Pang, Jianmin, Yichi Zhang, Zhen Shan und Chao You. „Program Behavior Fusion to Identify Malware“. In 2012 5th International Symposium on Computational Intelligence and Design (ISCID 2012). IEEE, 2012. http://dx.doi.org/10.1109/iscid.2012.30.

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Botacin, Marcus, André Grégio und Paulo De Geus. „Malware Variants Identiﬁcation in Practice“. In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação, 2019. http://dx.doi.org/10.5753/sbseg.2019.13960.

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Malware are persistent threats to computer systems and analysis procedures allow developing countermeasures to them. However, as samples are spreading on growing rates, malware clustering techniques are required to keep analysis procedures scalable. Current clustering approaches use Call Graphs (CGs) to identify polymorphic samples, but they consider only individual functions calls, thus failing to cluster malware variants created by replacing sample's original functions by semantically-equivalent ones. To solve this problem, we propose a behavior-based classiﬁcation procedure able to group functions on classes, thus reducing analysis procedures costs. We show that classifying samples according their behaviors (via function call semantics) instead by their pure API invocation is a more effective way to cluster malware variants. We also show that using a continence metric instead of a similarity metric helps to identify malware variants when a sample is embedded in another.

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Saxe, Joshua, David Mentis und Christopher Greamo. „Mining Web Technical Discussions to Identify Malware Capabilities“. In 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops (ICDCSW). IEEE, 2013. http://dx.doi.org/10.1109/icdcsw.2013.56.

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Qiao, Yanchen, Xiaochun Yun und Yongzheng Zhang. „How to Automatically Identify the Homology of Different Malware“. In 2016 IEEE Trustcom/BigDataSE/ISPA. IEEE, 2016. http://dx.doi.org/10.1109/trustcom.2016.0158.

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VanHoudnos, Nathan, William Casey, David French, Brian Lindauer, Eliezer Kanal, Evan Wright, Bronwyn Woods, Seungwhan Moon, Peter Jansen und Jamie Carbonell. „This Malware Looks Familiar: Laymen Identify Malware Run-time Similarity with Chernoff faces and Stick Figures“. In 10th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). EAI, 2017. http://dx.doi.org/10.4108/eai.22-3-2017.152417.

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Duan, Yiheng, Xiao Fu, Bin Luo, Ziqi Wang, Jin Shi und Xiaojiang Du. „Detective: Automatically identify and analyze malware processes in forensic scenarios via DLLs“. In 2015 IEEE International Conference on Signal Processing for Communications (ICC). IEEE, 2015. http://dx.doi.org/10.1109/icc.2015.7249229.

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Smutz, Charles, und Angelos Stavrou. „When a Tree Falls: Using Diversity in Ensemble Classifiers to Identify Evasion in Malware Detectors“. In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2016. http://dx.doi.org/10.14722/ndss.2016.23078.

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Pascariu, Cristian, und Ionut-Daniel Barbu. „Dynamic analysis of malware using artificial neural networks: Applying machine learning to identify malicious behavior based on parent process hirarchy“. In 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2017. http://dx.doi.org/10.1109/ecai.2017.8166505.

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Ramkumar, G., S. Vigneshwari und S. Roodyn. „An enhanced system to identify mischievous social malwares on Facebook applications“. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016. http://dx.doi.org/10.1109/iccpct.2016.7530271.

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