Detection and Analysis of Drive-by Downloads and Malicious Websites

Ibrahim, Saeed; Herami, Nawwaf Al; Naqbi, Ebrahim Al; Aldwairi, Monther

doi:10.1007/978-981-15-4825-3_6

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Hackers are constantly searching for vulnerable websites and considering the creation of malicious HTTP or PHP code scripts that could be used in these websites to capture data. These kinds of states could be installed from any website by a machine, and it could take the form of an IFRAME that redirects the victim to download a particular site that is managed by criminals [42]. The event driver download arrangement is attacked; Figure 9 could reflect this situation.…”

Section: Attack Modelsmentioning

confidence: 99%

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

et al. 2021

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With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

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Section: Attack Modelsmentioning

confidence: 99%

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

et al. 2021

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Detecting Malicious Domains Using Statistical Internationalized Domain Name Features in Top Level Domains

Alshaima

Mahmoud

Bayena

et al. 2022

2022 14th Annual Undergraduate Research Conference on Applied Computing (URC)

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Malicious Web Links Detection - A Comparative Analysis of Machine Learning Algorithms

Coste

2023

Studia UBB Informatica

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"One of the most challenging categories of threats circulating into the online world is social engineering, with malicious web links, fake news, clickbait, and other tactics. Malware URLs are extremely dangerous because they represent the main propagating vector for web malware. Malicious web links detection is a challenging task because the detection mechanism should not influence the consumers’ online experience. The proposed solutions must be sensitive enough, and fast enough to perform the detection mechanism before the user accesses the link and downloads its content. Our paper proposes three goals. The main purpose of this paper is to refine a methodology for malicious web links detection that may be used to experiment with machine learning algorithms. Moreover, we propose to use this methodology for training and comparing several machine learning algorithms such as Random Forest, Decision Tree, K-Nearest Neighbor. The results are compared, justified, and placed in the malicious web links literature. In addition, we propose to identify the most relevant features and draw some observations about them. 2010 Mathematics Subject Classification. 68T99, 68U99. 1998 CR Categories and Descriptors. 68T99 [Artificial Intelligence]: Applications and Expert Systems – Experiments for Malicious Web Links Detection; 68U99 [Management of Computing and Information Systems]: Security and Protection – Malicious Web Links. Key words and phrases. malicious web links detection, web-malware, artificial intelligence."

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Detection and Analysis of Drive-by Downloads and Malicious Websites

Cited by 3 publications

References 24 publications

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

Detecting Malicious Domains Using Statistical Internationalized Domain Name Features in Top Level Domains

Malicious Web Links Detection - A Comparative Analysis of Machine Learning Algorithms

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