Journal Article10.9734/ajrcos/2023/v16i3357
Cyber Kill Chain Analysis Using Artificial Intelligence
A. Shehu,Mahmood Hamid Umar,A. Aliyu +2 more
4
TL;DR: This study develops a conceptual framework for applying Artificial Intelligence (AI) to the Cyber Kill Chain phases, highlighting AI's potential in enhancing cybersecurity through machine learning, anomaly detection, and behavioral analysis, while addressing challenges and limitations.
read more
Abstract: Artificial Intelligence (AI) tools are promising multifaceted techniques for addressing the most mundane tasks for greater efficiency and high productivity. Cyber security space is one of the areas that AI is promising to revolutionize. This study will develop a conceptual and theoretical framework to support a research design that can simulate research in understanding how AI can be applied to Cyber Kill Chain phases. This study has reviewed 21 journal and conference articles mostly from IEEE Xplore database. An overview of the application of artificial intelligence (AI) in cybersecurity, particularly within the framework of the Cyber Kill Chain was provided in this study. It also emphasizes the limitations of traditional security approaches and the necessity for innovative and intelligent defense methodologies. The results of reviewing the relevant literatures discovered that the key components of cybersecurity, includes identity, asset management, automated configuration management, security control validation, governance, risk assessment, and vulnerability identification. A theoretical framework was developed which introduces the Cyber Kill Chain model with a Unified Kill Chain model to address its shortcomings. Application of AI in cybersecurity offers an optimistic solutions to address the evolving threat landscape. AI techniques, such as machine learning, anomaly detection, and behavioural analysis, have shown great potential in enhancing various aspects of cybersecurity. However, challenges related to data quality, adversarial attacks, and privacy concerns need to be addressed for successful implementation. Further research and development are crucial to fully harness the power of AI in cybersecurity and stay ahead of evolving cyber threats.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention
Onuh Matthew Ijiga,Idoko Peter Idoko,Godslove Isenyo Ebiega,Frederick Itunu Olajide,Timilehin Isaiah Olatunde,Chukwunonso Ukaegbu +5 more
TL;DR: AI-driven strategies in cybersecurity risk assessment and fraud prevention leverage adversarial machine learning techniques to dynamically assess risks, detect fraudulent activities, and enhance security defenses.
25
Integration of Machine Learning and Human Expertise for the Improvement of Cybersecurity: Applications and Challenges
Sree Pradip Kumer Sarker,Rajib Das,Rabiul Hasan +2 more
TL;DR: Integration of machine learning and human expertise for cybersecurity improves threat detection, incident response, and vulnerability management.
Taksonomi pertahanan cyber security menggunakan model cyber kill chain
TL;DR: Model Cyber Kill Chain digunakan untuk mengungkap status dari pelanggaran data dan menghasilkan informasi perilaku pengguna jaringan berupa notifikasi ancaman pada setiap tahap kill chain.
The Role of AI In Enhancing Threat Detection and Response in Cybersecurity Infrastructures
Valentine A. Onih,Yufenyuy S. Sevidzem,Sulaimon Adeniji +2 more
TL;DR: AI significantly enhances threat detection and response in cybersecurity infrastructures, improving security systems and addressing urgent defence needs. However, challenges such as high implementation costs, lack of skilled employees, and data security concerns hinder widespread adoption.
References
Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics
Arunan Sivanathan,Hassan Habibi Gharakheili,Franco Loi,Adam Radford,Chamith Wijenayake,Arun Vishwanath,Vijay Sivaraman +6 more
TL;DR: This study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.
735
Artificial intelligence for cybersecurity: Literature review and future research directions
TL;DR: In this article , the authors present a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning, which resulted in 2395 studies, of which 236 were identified as primary.
200
Ensemble machine learning approach for classification of IoT devices in smart home
TL;DR: In this article, a logistic regression method enhanced by the concept of supervised machine learning (logitboost) was used for developing a classification model using 13 network traffic features generated by IoT devices.
•Proceedings Article
Cloudy with a chance of breach: forecasting cyber security incidents
Yang Liu,Armin Sarabi,Jing Zhang,Parinaz Naghizadeh,Manish Karir,Michael Bailey,Mingyan Liu +6 more
- 12 Aug 2015
TL;DR: The extent to which cyber security incidents, such as those referenced by Verizon in its annual Data Breach Investigations Reports (DBIR), can be predicted based on externally observable properties of an organization's network is characterized.
Automated IoT Device Identification using Network Traffic
Ahmet Aksoy,Mehmet Hadi Gunes +1 more
- 20 May 2019
TL;DR: A system for automated classification of device characteristics, called System IDentifier (SysID), based on their network traffic, which allows the ability to have a completely automated way of classifying IoT devices using their TCP/IP packets without expert input for classification.
166