Anomaly Detection for Cyber-Security Based on Convolution Neural Network : A survey

Alabadi, Montdher; Çelik, Yüksel

doi:10.1109/hora49412.2020.9152899

Cited by 25 publications

(13 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The authors focused on fraud detection, intrusion detection, spam detection, and virus detection during their investigation. Alabadi and Celik in [53] presented a comprehensive survey about using CNN as a key solution for anomaly detection. Kim and Park [54] focus the attention on ML in Cyber-Physical Systems (CPS), which is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network.…”

Section: B Surveys On Artificial Intelligence Applications In Cyberse...mentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

View full text Add to dashboard Cite

Nowadays, Artificial Intelligence (AI) is widely applied in every area of human being's daily life. Despite the AI benefits, its application suffer from the opacity of complex internal mechanisms and doesn't satisfy by design the principles of Explainable Artificial Intelligence (XAI). The lack of transparency further exacerbates the problem in the field of Cybersecurity because entrusting crucial decisions to a system that cannot explain itself presents obvious dangers. There are several methods in the literature capable of providing explainability of AI results. Anyway, the application of XAI in Cybersecurity can be a doubleedged sword. It substantially improves the Cybersecurity practices but simultaneously leaves the system vulnerable to adversary attacks. Therefore, there is a need to analyze the state-of-the-art of XAI methods in Cybersecurity to provide a clear vision for future research. This study presents an in-depth examination of the application of XAI in Cybersecurity. It considers more than 300 papers to comprehensively analyze the main Cybersecurity application fields, like Intrusion Detection Systems,Malware detection, Phishing and Spam detection, BotNets detection, Fraud detection, Zero-Day vulnerabilities, Digital Forensics and Crypto-Jacking . Specifically, this study focuses on the explainability methods adopted or proposed in these fields, pointing out promising works and new challenges.

show abstract

Section: B Surveys On Artificial Intelligence Applications In Cyberse...mentioning

confidence: 99%

Explainable Artificial Intelligence in CyberSecurity: A Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Program interpreter converts the data to a useful representation, data matchers compare the interpreted data with the program behavior (Al-Janabi and Altamimi, 2020). Most of the literature classifies anomaly detection as shown below (Alabadi and Celik, 2020):…”

Section: Intrusion Detection Systems (Ids)mentioning

confidence: 99%

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula¹,

Venkatesh²,

Venugopal³

2023

Journal of Computer Science

View full text Add to dashboard Cite

Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation's defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study's key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

show abstract

“…Аналіз роботи цих засобів показує, що більшість із них виконують лише одну або кілька специфічних функцій, які не можуть забезпечити тією чи іншою мірою складності захисту інформації, необхідної для майбутніх інфокомунікаційних мереж. Також, згідно із [4][5][6][7], встановлено, що наявні DPI системи потрубують нових інтелектуальних алгоритмів виявлення аномалій та атак для забезпечення необхідного рівня якості обслуговування та безпеки в перспективних програмно-конфігурованих мережах. Для ефективнішого виявлення нових атак у роботі [8] запропоновано модель виявлення аномалії із використанням вектора показника Херста та мультифрактального спектра.…”

Section: вступunclassified

Інтелектуальна Система Моніторингу Та Аналізу Трафіку Для Виявлення Атак В Програмно-Конфігурованих Мережах

Бешлей¹,

Прислупський²,

Медвецький³

et al. 2022

ICTEE

View full text Add to dashboard Cite

Today, the task of monitoring, security, management, traffic prioritization and optimization of the overall load on the network infrastructure is becoming extremely important not only for the corporate sector, but also for mobile and fixed-line operators. Current trends in Internet content services indicate that the requirements for traffic management are unpredictable. Therefore, service providers must implement new algorithms to monitor and analyze network traffic to detect attacks in today's networks, where software can be updated to support changing traffic management and control requirements as needed. That is why this paper developed a unique algorithm for monitoring and analyzing network traffic, which improved the efficiency of standard intrusion detection and prevention systems by using intelligent multifractal processes for analyzing incoming traffic. It has been experimentally proved that the use of the developed algorithms in the communication infrastructure allowed to reduce data losses up to 5 % if malicious traffic was present in the communication channels.

show abstract

Anomaly Detection for Cyber-Security Based on Convolution Neural Network : A survey

Cited by 25 publications

References 41 publications

Explainable Artificial Intelligence in CyberSecurity: A Survey

Explainable Artificial Intelligence in CyberSecurity: A Survey

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Інтелектуальна Система Моніторингу Та Аналізу Трафіку Для Виявлення Атак В Програмно-Конфігурованих Мережах

Contact Info

Product

Resources

About