Towards AI-powered Cybersecurity Attack Modeling with Simulation Tools: Review of Attack Simulators

Jaber, Aws Naser; Fritsch, Lothar

doi:10.1007/978-3-031-19945-5_25

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…Artificial intelligence algorithms can be taught to recognize common forms of assault and to spot novel ones that have certain traits. Patching security holes, keeping tabs on network traffic, and handling incidents are just some of the mundane responsibilities that may be automated with the help of AI [53,54]. Cybersecurity experts can then devote their time and energy to solving problems that call for their unique set of skills.…”

Section: Ai For Cybersecurity In Generalmentioning

confidence: 99%

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Krichen

2023

Computers

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Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security.

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Section: Ai For Cybersecurity In Generalmentioning

confidence: 99%

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Krichen

2023

Computers

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“…Irshad and Siddiqui [Irshad and Siddiqui 2022] developed a mechanism to at-tribute or profile cyber threat actors (CTA) by extracting features from cyber threat intelligence (CTI) reports. They defined a methodology to extract features from unstructured CTI reports by using natural language processing (NLP) techniques and then attributing cyber threat actor by using machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Regardless of the type of assessment carried out, such as war games [Moumouh et al 2023], penetration testing [Mayukha and Vadivel 2023] or breach attack simulations [Jaber and Fritsch 2023], the results of the execution of your campaigns are presented in reports that will serve as a basis for preparing the action plan to mitigate the weaknesses found. As assessment campaigns perform various procedures that simulate the behavior of known hackers or malware groups in a generalized way.…”

Section: Introductionmentioning

confidence: 99%

“…Most research found in the state of the art has the objective of detecting and recognizing patterns in combating malicious adversaries [Ahn et al 2022, Lin et al 2022, Noor et al 2019]. However, these works have addressed this problem through proposals for a cyber threat attribution using unstructured reports in cyber threat intelligence [Irshad and Siddiqui 2022] and an automated reclassification for threat actors [Shin et al 2021]. Also, the study in [Park et al 2023] compares the TTPs of different Advanced Persistent Threat (APT) groups through vectorizing the ATT&CK matrix and calculating the cosine similarity.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Potential Threats in the Critical Path for Defense Operations by Cross Reference Features Clustering

Horta,

Marinho,

Holanda

2023

Anais Do XXIII Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2023)

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Cyber attacks are a threat to the security of the most diverse types of organizations. To mitigate the risk of suffering successful attacks, organizations use different types of assessments. The research problem addressed in this study is to present, among the behaviors of known threats, those that are similar to the assessment campaign carried out and consequently represent greater risk when attempting attacks using the more exploitable critical path. The purpose of this research is to present a method for identifying the critical path of the threat and the similarity factor by Cross Reference Features (CRF) method to identify the opponents most similar to the procedures used in the assessment campaign carried out. The CRF was used as a baseline for comparison between 2 unsupervised learning algorithms in the threat clustering task. For essays that considered only groups as threats, K-means outperformed the Hierarchical Agglomerative Clustering by 2.4 percentage points, while in the essay with all threats, Hierarchical Agglomerative Clustering surpassed K-means by 2.3%.

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“…This is also an era where cyber risks, cyber-attacks and the spread of disinformation are also inevitable. Threat actors are already increasing their capabilities, using artificial intelligence-powered cyber-attacks (Guembea, et al, 2022;Jaber & Fritsch, 2023). There has been a 38% increase in cyber-attacks worldwide in 2022 compared to 2021, with education/research, government/military and healthcare being the most attacked sectors (Check Point Research, 2023).…”

Section: Introductionmentioning

confidence: 99%

A Cyber Counterintelligence Competence Framework: Developing the Job Roles

Sithole

Toit²,

Solms³

2023

eccws

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In recent years, there have been intensifying cyber risks and volumes of cyber incidents prompting a significant shift in the cyber threat landscape. Both nation-state and non-state actors are increasingly resolute and innovative in their techniques and operations globally. These intensifying cyber risks and incidents suggest that cyber capability is inversely proportional to cyber risks, threats and attacks. Therefore, this confirms an emergent and critical need to adopt and invest in intelligence strategies, predominantly cyber counterintelligence (CCI), which is a multi-disciplinary and proactive measure to mitigate risks and counter cyber threats and cyber-attacks. Concurrent with the adoption of CCI is an appreciation that requisite job roles must be defined and developed. Notwithstanding the traction that CCI is gaining, we found no work on a clear categorisation for the CCI job roles in the academic or industry literature surveyed. Furthermore, from a cybersecurity perspective, it is unclear which job roles constitute the CCI field. This paper stems from and expands on the authors’ prior research on developing a CCI Competence Framework. The proposed CCI Competence Framework consists of four critical elements deemed essential for CCI workforce development. In order of progression, the Framework’s elements are: CCI Dimensions (passive-defensive, active-defensive, passive-offensive, active-offensive), CCI Functional Areas (detection, deterrence, deception, neutralisation), CCI Job Roles (associated with each respective Functional Area), and Tasks and Competences (allocated to each job role). Pivoting on prior research on CCI Dimensions and CCI Functional Areas, this paper advances a proposition on associated Job Roles in a manner that is both intelligible and categorised. To this end, the paper advances a five-step process that evaluates and examines Counterintelligence and Cybersecurity Job Roles and functions to derive a combination of new or existing Job Roles required for the CCI workforce/professionals. Although there are several cybersecurity frameworks for workforce development, establishing the CCI Job Roles is specifically based on the expression of the Job Roles defined in the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework.

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Towards AI-powered Cybersecurity Attack Modeling with Simulation Tools: Review of Attack Simulators

Cited by 13 publications

References 20 publications

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence

Identification of Potential Threats in the Critical Path for Defense Operations by Cross Reference Features Clustering

A Cyber Counterintelligence Competence Framework: Developing the Job Roles

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