Relevant bibliographies by topics / Zero-day malware attacks

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Zero-day malware attacks'

Author: Grafiati
Published: 19 February 2023

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Journal articles on the topic "Zero-day malware attacks"

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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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Emmah, Victor T., Chidiebere Ugwu, and Laeticia N. Onyejegbu. "An Enhanced Classification Model for Likelihood of Zero-Day Attack Detection and Estimation." European Journal of Electrical Engineering and Computer Science 5, no. 4 (August 19, 2021): 69–75. http://dx.doi.org/10.24018/ejece.2021.5.4.350.

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The growing threat to sensitive information stored in computer systems and devices is becoming alarming. This is as a result of the proliferation of different malware created on a daily basis to cause zero-day attacks. Most of the malware whose signatures are known can easily be detected and blocked, however, the unknown malwares are the most dangerous. In this paper a zero-day vulnerability model based on deep-reinforcement learning is presented. The technique employs a Monte Carlo Based Pareto Rule (Deep-RL-MCB-PR) approach that exploits a reward learning and training feature with sparse feature generation and adaptive multi-layered recurrent prediction for the detection and subsequent mitigation of zero-day threats. The new model has been applied to the Kyoto benchmark datasets for intrusion detection systems, and compared to an existing system, that uses a multi-layer protection and a rule-based ranking (RBK) approach to detect a zero-day attack likelihood. Experiments were performed using the dataset, and simulation results show that the Deep-RL-MCB-PR technique when measured with the classification accuracy metrics, produced about 67.77%. The dataset was further magnified, and the result of classification accuracy showed about 75.84%. These results account for a better error response when compared to the RBK technique.
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Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.

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Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.
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Tran, Hiep, Enrique Campos-Nanez, Pavel Fomin, and James Wasek. "Cyber resilience recovery model to combat zero-day malware attacks." Computers & Security 61 (August 2016): 19–31. http://dx.doi.org/10.1016/j.cose.2016.05.001.

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Tayyab, Umm-e.-Hani, Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, and Yeon Soo Lee. "A Survey of the Recent Trends in Deep Learning Based Malware Detection." Journal of Cybersecurity and Privacy 2, no. 4 (September 28, 2022): 800–829. http://dx.doi.org/10.3390/jcp2040041.

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Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying malicious activity. Malicious activities potentially lead to a system breach or data compromise. Various tools and anti-malware products exist for the detection of malware and cyberattacks utilizing IOCs, but all have several shortcomings. For instance, anti-malware systems make use of malware signatures, requiring a database containing such signatures to be constantly updated. Additionally, this technique does not work for zero-day attacks or variants of existing malware. In the quest to fight zero-day attacks, the research paradigm shifted from primitive methods to classical machine learning-based methods. Primitive methods are limited in catering to anti-analysis techniques against zero-day attacks. Hence, the direction of research moved towards methods utilizing classic machine learning, however, machine learning methods also come with certain limitations. They may include but not limited to the latency/lag introduced by feature-engineering phase on the entire training dataset as opposed to the real-time analysis requirement. Likewise, additional layers of data engineering to cater to the increasing volume of data introduces further delays. It led to the use of deep learning-based methods for malware detection. With the speedy occurrence of zero-day malware, researchers chose to experiment with few shot learning so that reliable solutions can be produced for malware detection with even a small amount of data at hand for training. In this paper, we surveyed several possible strategies to support the real-time detection of malware and propose a hierarchical model to discover security events or threats in real-time. A key focus in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning. We compare Deep Learning-based approaches with conventional machine learning based approaches and primitive (statistical analysis based) methods commonly reported in the literature.
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Cheng, Binlin, Jinjun Liu, Jiejie Chen, Shudong Shi, Xufu Peng, Xingwen Zhang, and Haiqing Hai. "MoG: Behavior-Obfuscation Resistance Malware Detection." Computer Journal 62, no. 12 (June 4, 2019): 1734–47. http://dx.doi.org/10.1093/comjnl/bxz033.

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Abstract Malware brings a big security threat on the Internet today. With the great increasing malware attacks. Behavior-based detection approaches are one of the major method to detect zero-day malware. Such approaches often use API calls to represent the behavior of malware. Unfortunately, behavior-based approaches suffer from behavior obfuscation attacks. In this paper, we propose a novel malware detection approach that is both effective and efficient. First, we abstract the API call to object operation. And then we generate the object operation dependency graph based on these object operations. Finally, we construct the family dependency graph for a malware family. Our approach use family dependency graph to represent the behavior of malware family. The evaluation results show that our approach can provide a complete resistance to all types of behavior obfuscation attacks, and outperforms existing behavior-based approaches in terms of better effectiveness and efficiency.
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Priya, P. Mohana, and Abhijit Ranganathan. "Cyber Awareness Learning Imitation Environment (CALIE): A Card Game to provide Cyber Security Awareness for Various Group of Practitioners." International Journal of Advanced Networking and Applications 14, no. 02 (2022): 5334–41. http://dx.doi.org/10.35444/ijana.2022.14203.

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Cyber attacks produced a massive impact for all online users, interrupted intended user’s internet services, financial losses, business interruptions for a large-scale industry. A proper cyber security education is must for the employees of an organization. The management prefers active based learning environment to train all non-IT and non-professionals working in an organization. This research work concentrates on development of gaming platform in both local host and in an online mode as a videogame for cyber security education. With this regard, Cyber Awareness Learning Imitation Environment – a card deck gaming environment is proposed where attackers can choose the attack cards to learn various cyber-attacks, defense cards are used for providing the suitable defense mechanism, Instruction card- to be used for learning about how to generate cyber-attacks and recent incident card used to train the players with recent incidents of various cyber-attacks discussed such as malware attack, phishing attack, password attack, Man-in-the-Middle attack, Structured Query Language injection attack, denial of service attack, insider threats, crypto jacking, zero-day exploit and watering hole attack. Questionnaire based feedback report is collected from the players to analyze their understanding about various cyber-attacks.
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Balaji K. M. and Subbulakshmi T. "Malware Analysis Using Classification and Clustering Algorithms." International Journal of e-Collaboration 18, no. 1 (January 2022): 1–26. http://dx.doi.org/10.4018/ijec.290290.

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Malware analysis and detection are important tasks to be accomplished as malware is getting more and more arduous at every instance. The threats and problems posed by the public around the globe are also rapidly increasing. Detection of zero-day attacks and polymorphic viruses is also a challenging task to be done. The increasing threats and problems lead to the need for detection techniques which lead to the well-known and the most common approach called as machine learning. The purpose of this survey is to formulate the most effective feature extraction and classification ways that sums up the most effective methods (which includes algorithms) with maximum accuracy and also to effectively understand the clustering properties of the malware datasets by considering appropriate algorithms. This work also provides an overview on information about malwares used. The experimental results of the proposed model clearly showed that the KNN classifier as the most accurate with 0.962355 accuracy.
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OPRIȘ, Cristian. "Cybercrime Evolution and Current Threats." International Journal of Information Security and Cybercrime 11, no. 1 (June 28, 2022): 41–48. http://dx.doi.org/10.19107/ijisc.2022.01.05.

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Cybercrime may be the biggest global threat in our time. This article reviews the current evolution of cybercrime and highlights some types of cybersecurity attacks: phishing, web-based attacks, malware, Denial of Service, Zero Day Manipulations, Cross-Site Scripting and IoT threats. We take a detailed study on ransomware phenomenon and present security measures that can protect companies and individuals regarding the current threats in cybersecurity field.
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Kim, Dohoon, Donghee Choi, and Jonghyun Jin. "Method for Detecting Core Malware Sites Related to Biomedical Information Systems." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/756842.

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Most advanced persistent threat attacks target web users through malicious code within landing (exploit) or distribution sites. There is an urgent need to block the affected websites. Attacks on biomedical information systems are no exception to this issue. In this paper, we present a method for locating malicious websites that attempt to attack biomedical information systems. Our approach uses malicious code crawling to rearrange websites in the order of their risk index by analyzing the centrality between malware sites and proactively eliminates the root of these sites by finding the core-hub node, thereby reducing unnecessary security policies. In particular, we dynamically estimate the risk index of the affected websites by analyzing various centrality measures and converting them into a single quantified vector. On average, the proactive elimination of core malicious websites results in an average improvement in zero-day attack detection of more than 20%.
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Dissertations / Theses on the topic "Zero-day malware attacks"

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Khraisat, Ansam. "Intelligent zero-day intrusion detection framework for internet of things." Thesis, Federation University Australia, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179729.

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Zero-day intrusion detection system faces serious challenges as hundreds of thousands of new instances of malware are being created every day to cause harm or damage to the computer system. Cyber-attacks are becoming more sophisticated, leading to challenges in intrusion detection. There are many Intrusion Detection Systems (IDSs), which are proposed to identify abnormal activities, but most of these IDSs produce a large number of false positives and low detection accuracy. Hence, a significant quantity of false positives could generate a high-level of alerts in a short period of time as the normal activities are classified as intrusion activities. This thesis proposes a novel framework of hybrid intrusion detection system that integrates the Signature Intrusion Detection System (SIDS) with the Anomaly Intrusion Detection System (AIDS) to detect zero-day attacks with high accuracy. SIDS has been used to identify previously known intrusions, and AIDS has been applied to detect unknown zero-day intrusions. The goal of this research is to combine the strengths of each technique toward the development of a hybrid framework for the efficient intrusion detection system. A number of performance measures including accuracy, F-measure and area under ROC curve have been used to evaluate the efficacy of our proposed models and to compare and contrast with existing approaches. Extensive simulation results conducted in this thesis show that the proposed framework is capable of yielding excellent detection performance when tested with a number of widely used benchmark datasets in the intrusion detection system domain. Experiments show that the proposed hybrid IDS provides higher detection rate and lower false-positive rate in detecting intrusions as compared to the SIDS and AIDS techniques individually.
Doctor of Philosophy
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Homoliak, Ivan. "Metriky pro detekci útoků v síťovém provozu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236525.

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Publication aims to propose and apply new metrics for intrusion detection in network traffic according to analysis of existing metrics, analysis of network traffic and behavioral characteristics of known attacks. The main goal of the thesis is to propose and implement new collection of metrics which will be capable to detect zero day attacks.
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Bláha, Lukáš. "Analýza automatizovaného generování signatur s využitím Honeypotu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236430.

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In this paper, system of automatic processing of attacks using honeypots is discussed. The first goal of the thesis is to become familiar with the issue of signatures to detect malware on the network, especially the analysis and description of existing methods for automatic generation of signatures using honeypots. The main goal is to use the acquired knowledge to the design and implementation of tool which will perform the detection of new malicious software on the network or end user's workstation.
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Book chapters on the topic "Zero-day malware attacks"

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Sharukh, Shaik Moin. "A Hybrid Deep Learning Approach for Detecting Zero-Day Malware Attacks." In Machine Learning Technologies and Applications, 203–10. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4046-6_20.

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Ngo, Quoc-Dung, and Quoc-Huu Nguyen. "A Reinforcement Learning-Based Approach for Detection Zero-Day Malware Attacks on IoT System." In Artificial Intelligence Trends in Systems, 381–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09076-9_34.

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Roseline, S. Abijah, and S. Geetha. "Intelligent Malware Detection Using Deep Dilated Residual Networks for Cyber Security." In Countering Cyber Attacks and Preserving the Integrity and Availability of Critical Systems, 211–29. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8241-0.ch011.

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Malware is the most serious security threat, which possibly targets billions of devices like personal computers, smartphones, etc. across the world. Malware classification and detection is a challenging task due to the targeted, zero-day, and stealthy nature of advanced and new malwares. The traditional signature detection methods like antivirus software were effective for detecting known malwares. At present, there are various solutions for detection of such unknown malwares employing feature-based machine learning algorithms. Machine learning techniques detect known malwares effectively but are not optimal and show a low accuracy rate for unknown malwares. This chapter explores a novel deep learning model called deep dilated residual network model for malware image classification. The proposed model showed a higher accuracy of 98.50% and 99.14% on Kaggle Malimg and BIG 2015 datasets, respectively. The new malwares can be handled in real-time with minimal human interaction using the proposed deep residual model.
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Ambika N. "Minimum Prediction Error at an Early Stage in Darknet Analysis." In Advances in Digital Crime, Forensics, and Cyber Terrorism, 18–30. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3942-5.ch002.

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The previous work adopts an evolving methodology in neural system. The chapter is a new darknet transactions summary. It is a system administration structure for real-time automating of the wicked intention discovery method. It uses a weight agnostic fuzzy interface construction. It is an efficient and reliable computational rational forensics device for web exchange examination, the exposure of malware transactions, and decoded business testimony in real-time. The suggestion is an automatic searching neural-net structure that can execute different duties, such as recognizing zero-day crimes. By automating the spiteful purpose disclosure means from the darknet, the answer reduces the abilities and training wall. It stops many institutions from adequately preserving their most hazardous asset. The system uses two types of datasets – training and prediction sets. The errors are detected using back propagation. The recommendation detects the attacks earlies by 6.85% and 13% of resources compared to the previous work.
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Conference papers on the topic "Zero-day malware attacks"

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Radhakrishnan, Kiran, Rajeev R. Menon, and Hiran V. Nath. "A survey of zero-day malware attacks and its detection methodology." In TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON). IEEE, 2019. http://dx.doi.org/10.1109/tencon.2019.8929620.

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