TY - GEN
T1 - Utilizing Machine Learning and Deep Learning Techniques for the Detection of Distributed Denial of Service (DDoS) Attacks
AU - Al-Hajri, Salim Badar Salim Hamed
AU - Jenkins, Paul
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/12/20
Y1 - 2024/12/20
N2 - The growing number of cyber-attacks has heightened the need for robust security measures, as they escalate in frequency and impact, affecting both economic stability and personal safety. Traditional methods of detecting these cyber threats are often costly and slow, prompting the need for more efficient and accurate technologies. This study explores advanced artificial intelligence techniques, utilizing both Machine Learning (ML) and Deep Learning (DL), to enhance the detection of Distributed Denial of Service (DDoS) attacks, by integrating diverse AI methodologies, including deep neural networks, random forest, long short-term memory and extreme gradient boosting systems, moreover, the paper investigates their collective effectiveness on the CICIDS2017 dataset. The analysis confirms that these integrated AI approaches achieve significant accuracy, recall and low false positive rates in identifying DDoS incidents. The paper is constructed as follows, Section 1 – Introduction, Section 2 A review of AI methods, Section 3 - Evolution of proposed models, Section 4 Experimental results, and Sect. 5 Discusses possible areas for further research.
AB - The growing number of cyber-attacks has heightened the need for robust security measures, as they escalate in frequency and impact, affecting both economic stability and personal safety. Traditional methods of detecting these cyber threats are often costly and slow, prompting the need for more efficient and accurate technologies. This study explores advanced artificial intelligence techniques, utilizing both Machine Learning (ML) and Deep Learning (DL), to enhance the detection of Distributed Denial of Service (DDoS) attacks, by integrating diverse AI methodologies, including deep neural networks, random forest, long short-term memory and extreme gradient boosting systems, moreover, the paper investigates their collective effectiveness on the CICIDS2017 dataset. The analysis confirms that these integrated AI approaches achieve significant accuracy, recall and low false positive rates in identifying DDoS incidents. The paper is constructed as follows, Section 1 – Introduction, Section 2 A review of AI methods, Section 3 - Evolution of proposed models, Section 4 Experimental results, and Sect. 5 Discusses possible areas for further research.
KW - AI Artificial Intelligence
KW - Cybersecurity
KW - DDoS – Distributed Denial of Service
KW - DL – Deep Learning
KW - ML – Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85214203284&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-74443-3_13
DO - 10.1007/978-3-031-74443-3_13
M3 - Conference contribution
AN - SCOPUS:85214203284
SN - 9783031744426
T3 - Lecture Notes in Networks and Systems
SP - 223
EP - 235
BT - Contributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI - The C3AI 2024
A2 - Naik, Nitin
A2 - Grace, Paul
A2 - Jenkins, Paul
A2 - Prajapat, Shaligram
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Computing, Communication, Cybersecurity and AI, C3AI 2024
Y2 - 3 July 2024 through 4 July 2024
ER -