STRIKE: A Structured Taxonomy of Cybercrime for Risk, Impact, Knowledge, and Evolution
- URL: http://arxiv.org/abs/2605.16589v1
- Date: Fri, 15 May 2026 19:48:16 GMT
- Title: STRIKE: A Structured Taxonomy of Cybercrime for Risk, Impact, Knowledge, and Evolution
- Abstract summary: STRIKE provides a unified, multi-dimensional framework for categorizing cybercrimes.<n>It spans both conventional and emerging domains, including ransomware, phishing, network intrusion, child sexual abuse material (CSAM), cryptojacking, deepfakes, and supply chain attacks.<n>It organizes threats using criteria such as attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies.
- Score: 4.47562439034112
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cybercrime has grown exponentially in both scale and sophistication, posing significant threats. As attack methods evolve rapidly, traditional classification schemes often fail to capture the complexity and diversity of modern threats. To address this gap, we introduce STRIKE,a Structured Taxonomy for Risk, Impact, Knowledge, and Emerging Threats, which provides a unified, multi-dimensional framework for categorizing cybercrimes. STRIKE spans both conventional and emerging domains, including ransomware, phishing, network intrusion, child sexual abuse material (CSAM), cryptojacking, deepfakes, and supply chain attacks. It organizes threats using criteria such as attack vectors, adversarial tactics, societal impact, detection techniques, and mitigation strategies. Alongside the taxonomy, we review recent advances in detection methodologies and present a response workflow to assist practitioners under active threat conditions. This work offers researchers, security professionals, and policymakers a practical foundation for threat analysis, comparative evaluation, and adaptive cyber defense.
Related papers
- Techniques of Modern Attacks [51.56484100374058]
Advanced Persistent Threats (APTs) represent a complex method of attack aimed at specific targets.<n>I will investigate both the attack life cycle and cutting-edge detection and defense strategies proposed in recent academic research.<n>I aim to highlight the strengths and limitations of each approach and propose more adaptive APT mitigation strategies.
arXiv Detail & Related papers (2026-01-19T22:15:25Z) - AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies [0.0]
This paper aims to analyze emerging risks, attack mechanisms, and defense shortcomings related to AI in cybersecurity.<n>We introduce a comparative taxonomy connecting AI capabilities with threat modalities and defenses.<n>Our findings emphasize the urgency for explainable, interdisciplinary, and regulatory-compliant AI defense systems.
arXiv Detail & Related papers (2026-01-06T05:09:40Z) - An Unsupervised Learning Approach For A Reliable Profiling Of Cyber Threat Actors Reported Globally Based On Complete Contextual Information Of Cyber Attacks [0.0]
It is critical to promptly recognize cyberattacks and establish strong defense mechanisms against them.<n>Creating a profile of cyber threat actors based on their traits or patterns of behavior can help to create effective defenses against cyberattacks in advance.<n>In this paper, an unsupervised efficient agglomerative hierarchal clustering technique is proposed for profiling cybercriminal groups.
arXiv Detail & Related papers (2025-09-15T08:32:59Z) - Cyber Threat Hunting: Non-Parametric Mining of Attack Patterns from Cyber Threat Intelligence for Precise Threats Attribution [0.0]
We propose a machine learning based approach featuring visually interactive analytics tool named the Cyber-Attack Pattern Explorer (CAPE)<n>In the proposed system, a non-parametric mining technique is proposed to create a dataset for identifying the attack patterns within cyber threat intelligence documents.<n>The extracted dataset is used for training of proposed machine learning algorithms that enables the attribution of cyber threats with respective to the actors.
arXiv Detail & Related papers (2025-09-15T06:15:22Z) - So, I climbed to the top of the pyramid of pain -- now what? [1.3249509346606658]
Humal Layer Kill Chain integrates human psychology and behaviour into the analysis of cyber threats.<n>By merging the Human Layer with the Cyber Kill Chain, we propose a Sociotechnical Kill Plane.<n>This framework not only aids cybersecurity professionals in understanding adversarial methods, but also empowers non-technical personnel to engage in threat identification and response.
arXiv Detail & Related papers (2025-05-30T15:09:03Z) - Taxonomy for Cybersecurity Threat Attributes and Countermeasures in Smart Manufacturing Systems [0.0]
Existing attack taxonomy only focus on a narrow range of attacks and limited threat attributes.
This paper proposes a comprehensive attack taxonomy for a holistic understanding and characterization of cybersecurity threats in manufacturing systems.
arXiv Detail & Related papers (2023-12-30T02:11:02Z) - On the Security Risks of Knowledge Graph Reasoning [71.64027889145261]
We systematize the security threats to KGR according to the adversary's objectives, knowledge, and attack vectors.
We present ROAR, a new class of attacks that instantiate a variety of such threats.
We explore potential countermeasures against ROAR, including filtering of potentially poisoning knowledge and training with adversarially augmented queries.
arXiv Detail & Related papers (2023-05-03T18:47:42Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Towards Automated Classification of Attackers' TTPs by combining NLP
with ML Techniques [77.34726150561087]
We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research.
Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
arXiv Detail & Related papers (2022-07-18T09:59:21Z) - Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligence [94.94833077653998]
ThreatRaptor is a system that facilitates threat hunting in computer systems using open-source Cyber Threat Intelligence (OSCTI)
It extracts structured threat behaviors from unstructured OSCTI text and uses a concise and expressive domain-specific query language, TBQL, to hunt for malicious system activities.
Evaluations on a broad set of attack cases demonstrate the accuracy and efficiency of ThreatRaptor in practical threat hunting.
arXiv Detail & Related papers (2020-10-26T14:54:01Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.