Internet of Things botnets: A survey on Artificial Intelligence based detection techniques

被引:1
作者
Lefoane, Moemedi [1 ]
Ghafir, Ibrahim [1 ]
Kabir, Sohag [1 ]
Awan, Irfan-Ullah [1 ]
机构
[1] Univ Bradford, Fac Engn & Digital Technol, Bradford BD7 1DP, England
关键词
Botnet attack; Internet Of Things; Network security; Intrusion Detection System; Machine learning; Artificial Intelligence; IOT-BASED BOTNET; INTRUSION DETECTION; DETECTION SYSTEM; ATTACKS; CHALLENGES; SELECTION; EDGE;
D O I
10.1016/j.jnca.2025.104110
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The Internet of Things (IoT) is a game changer when it comes to digitisation across industries. The Fourth Industrial Revolution (4IR), brought about a paradigm shift indeed, unlocking possibilities and taking industries to greater heights never reached before in terms of cost saving and improved performance leading to increased productivity and profits, just to mention a few. While there are more benefits provided by IoT, there are challenges arising from the complexities, limitations and requirements of IoT and key enabling technologies. Distributed Denial of Service (DDoS) attacks are among the most prevalent and dominant cyber-attacks that have been making headlines repeatedly in recent years. IoT technology has increasingly become the preferred technology for delivering these cyber-attacks. It does not come as a surprise that IoT devices are an attractive target for adversaries, as they are easy to compromise due to inherent limitations and given that they are deployed in large numbers. This paper reviews IoT botnet detection approaches proposed in recent years. Furthermore, IoT ecosystem components are outlined, revealing their challenges, limitations and key requirements that are vital to securing the whole ecosystem. These include cloud computing, Machine Learning (ML) and emerging wireless technologies: 5G and 6G.
引用
收藏
页数:17
相关论文
共 104 条
[1]   Cloud of Things (CoT): Cloud-Fog-IoT Task Offloading for Sustainable Internet of Things [J].
Aazam, Mohammad ;
ul Islam, Saif ;
Lone, Salman Tariq ;
Abbas, Assad .
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING, 2022, 7 (01) :87-98
[2]   Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset [J].
Abdalgawad, N. ;
Sajun, A. ;
Kaddoura, Y. ;
Zualkernan, I. A. ;
Aloul, F. .
IEEE ACCESS, 2022, 10 :6430-6441
[3]  
Abdallah Abdallah, 2022, 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), P955, DOI 10.1109/CCNC49033.2022.9700704
[4]  
Abdulhamid A., 2022, 2022 INT C EL COMP C, P1
[5]   LOAD BALANCING IN HYBRID WIFI/ LIFI NETWORKS BASED ON THE RSSI OF THE LOAD USING OPTIMIZED KNN CLUSTERING [J].
Ahmed, Mohammed ;
Alkahrsan, Ali ;
Ilyas, Muhammad .
2ND INTERNATIONAL CONGRESS ON HUMAN-COMPUTER INTERACTION, OPTIMIZATION AND ROBOTIC APPLICATIONS (HORA 2020), 2020, :652-655
[6]   Systematic Literature Review on IoT-Based Botnet Attack [J].
Ali, Ihsan ;
Ahmed, Abdelmuttlib Ibrahim Abdalla ;
Almogren, Ahmad ;
Raza, Muhammad Ahsan ;
Shah, Syed Attique ;
Khan, Anwar ;
Gani, Abdullah .
IEEE ACCESS, 2020, 8 :212220-212232
[7]   A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things [J].
Alwarafy, Abdulmalik ;
Al-Thelaya, Khaled A. ;
Abdallah, Mohamed ;
Schneider, Jens ;
Hamdi, Mounir .
IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (06) :4004-4022
[8]  
[Anonymous], 2021, BBC News
[9]  
[Anonymous], 2020, Machine learning
[10]  
[Anonymous], 2016, The Guardian