Profits at the Dawn of Cybercrime-as-a-Service

Vidal, Jorge Maestre; Monge, Marco Antonio Sotelo; Monterrubio, Sergio Mauricio Martinez; López, Lorena Isabel Barona; Caraguay, Ángel Leonardo Valdivieso

doi:10.1109/ici2st.2019.00017

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The increasing digitization of the world led to a rise in cybercrime, particularly through particularly through crimeware-as-a-service (CaaS), where criminal organizations provide illicit services and tools for online attacks like phishing, malware distribution, and data theft [1]. CaaS operates similarly to legitimate software-as-a-service (SaaS) providers but is illegal and used for criminal activities [2].…”

Section: Introductionmentioning

confidence: 99%

Enhanced website phishing detection based on the cyber kill chain and cloud computing

Rashid,

Abdullah

2023

IJEECS

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<span>Crimeware-as-a-service (CaaS) presents a growing cybersecurity threat by facilitating the acquisition of tools for website phishing attacks. Detecting these attacks requires effective techniques to obtain accurate results in real time. Cloud machine learning (CML) emerged as a promising solution with the powerful tools of amazon web services (AWS). This study proposes a novel approach combining cyber kill chain concept with AWS technologies to enhance website phishing detection, using AWS SageMaker to preprocess an 11,430 uniform resource locators (URL) dataset and train 3 algorithms, which are: decision tree (DT), random forest (RF), and support vector machine (SVM), evaluate their performance through batch transform, and deploy them as separate endpoints. Prediction functions are then conducted on each endpoint and compared to batch transform results. Our findings demonstrate that the combination of the cyber kill chain concept and AWS CML significantly enhances website phishing detection by achieving results of 97% for RF in 0.48 seconds, 94% for SVM in 0.94 seconds, and 93% for DT in 0.52 seconds. By leveraging CML algorithms and breaking down attacks into stages, our approach identifies and disrupts attacks earlier, preventing damage. This research highlights the value of our approach in improving cybersecurity and protecting against website phishing attacks.</span>

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Section: Introductionmentioning

confidence: 99%

Enhanced website phishing detection based on the cyber kill chain and cloud computing

Rashid,

Abdullah

2023

IJEECS

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“…In addition to the activities of criminal gangs, the cyber crime marketplace is evolving over time. Cyber crime as a Service (CaaS) operations involve distributed denial of service attacks, credit card fraud, compromised systems selling and many other types of cyber crime [645] [287]. In the particular case of Ransomware as a Service (RaaS), criminal groups rent from packers and algorithms to social engineering capabilities [323].…”

Section: Non-state Actorsmentioning

confidence: 99%

Modeling of Advanced Threat Actors: Characterization, Categorization and Detection

Huerta¹

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AISGA: Multi-objective parameters optimization for countermeasures selection through genetic algorithm

Nespoli

Mármol

Kambourakis

2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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Cyberattacks targeting modern network infrastructures are increasing in number and impact. This growing phenomenon emphasizes the central role of cybersecurity and, in particular, the reaction against ongoing threats targeting assets within the protected system. Such centrality is reflected in the literature, where several works have been presented to propose full-fledged reaction methodologies to tackle offensive incidents' consequences. In this direction, the work in [18] developed an immuno-based response approach based on the application of the Artificial Immune System (AIS) methodology. That is, the AIS-powered reaction is able to calculate the optimal set of atomic countermeasure to enforce on the asset within the monitored system, minimizing the risk to which those are exposed in a more than adequate time. To further contribute to this line, the paper at hand presents AISGA, a multi-objective approach that leverages the capabilities of a Genetic Algorithm (GA) to optimize the selection of the input parameters of the AIS methodology. Specifically, AISGA selects the optimal ranges of inputs that balance the tradeoff between minimizing the global risk and the execution time of the methodology. Additionally, by flooding the AIS-powered reaction with a wide range of possible inputs, AISGA intends to demonstrate the robustness of such a model. Exhaustive experiments are executed to precisely compute the optimal ranges of parameters, demonstrating that the proposed multi-objective optimization prefers a fast-but-effective reaction.

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Profits at the Dawn of Cybercrime-as-a-Service

Cited by 3 publications

References 17 publications

Enhanced website phishing detection based on the cyber kill chain and cloud computing

Enhanced website phishing detection based on the cyber kill chain and cloud computing

Modeling of Advanced Threat Actors: Characterization, Categorization and Detection

AISGA: Multi-objective parameters optimization for countermeasures selection through genetic algorithm

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