Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 419-437, 2023, DOI:10.32604/iasc.2023.038043 - 29 April 2023

Abstract The Internet of Things (IoT) has aided in the development of new products and services. Due to the heterogeneity of IoT items and networks, traditional techniques cannot identify network risks. Rule-based solutions make it challenging to secure and manage IoT devices and services due to their diversity. While the use of artificial intelligence eliminates the need to define rules, the training and retraining processes require additional processing power. This study proposes a methodology for analyzing constrained devices in IoT environments. We examined the relationship between different sized samples from the Kitsune dataset to simulate the… More >

Arkan Hammoodi Hasan Kabla, Mohammed Anbar, Selvakumar Manickam, Alwan Ahmed Abdulrahman Alwan, Shankar Karuppayah^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3375-3398, 2023, DOI:10.32604/cmc.2023.036587 - 31 March 2023

Abstract The cyber-criminal compromises end-hosts (bots) to configure a network of bots (botnet). The cyber-criminals are also looking for an evolved architecture that makes their techniques more resilient and stealthier such as Peer-to-Peer (P2P) networks. The P2P botnets leverage the privileges of the decentralized nature of P2P networks. Consequently, the P2P botnets exploit the resilience of this architecture to be arduous against take-down procedures. Some P2P botnets are smarter to be stealthy in their Command-and-Control mechanisms (C2) and elude the standard discovery mechanisms. Therefore, the other side of this cyberwar is the monitor. The P2P botnet… More >

Saeed Masoud Alshahrani¹, Fatma S. Alrayes², Hamed Alqahtani³, Jaber S. Alzahrani⁴, Mohammed Maray⁵, Sana Alazwari⁶, Mohamed A. Shamseldin⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3085-3100, 2023, DOI:10.32604/cmc.2023.032972 - 31 October 2022

Abstract Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning… More >

Muhammad Nadeem¹, Ali Arshad², Saman Riaz², SyedaWajiha Zahra¹, Muhammad Rashid², Shahab S. Band^3,*, Amir Mosavi^4,5,6

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2641-2659, 2023, DOI:10.32604/cmc.2023.028796 - 31 October 2022

Abstract Cloud computing is one of the most attractive and cost-saving models, which provides online services to end-users. Cloud computing allows the user to access data directly from any node. But nowadays, cloud security is one of the biggest issues that arise. Different types of malware are wreaking havoc on the clouds. Attacks on the cloud server are happening from both internal and external sides. This paper has developed a tool to prevent the cloud server from spamming attacks. When an attacker attempts to use different spamming techniques on a cloud server, the attacker will be More >

S. Vanitha^1,*, P. Balasubramanie²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 849-864, 2023, DOI:10.32604/iasc.2023.032324 - 29 September 2022

Abstract Internet of things (IOT) possess cultural, commercial and social effect in life in the future. The nodes which are participating in IOT network are basically attracted by the cyber-attack targets. Attack and identification of anomalies in IoT infrastructure is a growing problem in the IoT domain. Machine Learning Based Ensemble Intrusion Detection (MLEID) method is applied in order to resolve the drawback by minimizing malicious actions in related botnet attacks on Message Queue Telemetry Transport (MQTT) and Hyper-Text Transfer Protocol (HTTP) protocols. The proposed work has two significant contributions which are a selection of features… More >

Khlood Shinan^1,2, Khalid Alsubhi², M. Usman Ashraf^3,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 693-714, 2023, DOI:10.32604/cmc.2023.031641 - 22 September 2022

Abstract The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study… More >

Peng Hui Li¹, Jie Xu^1,*, Zhong Yi Xu¹, Su Chen¹, Bo Wei Niu², Jie Yin¹, Xiao Feng Sun¹, Hao Liang Lan¹, Lu Lu Chen³

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3847-3860, 2022, DOI:10.32604/cmc.2022.029969 - 16 June 2022

Abstract At present, the severe network security situation has put forward high requirements for network security defense technology. In order to automate botnet threat warning, this paper researches the types and characteristics of Botnet. Botnet has special characteristics in attributes such as packets, attack time interval, and packet size. In this paper, the attack data is annotated by means of string recognition and expert screening. The attack features are extracted from the labeled attack data, and then use K-means for cluster analysis. The clustering results show that the same attack data has its unique characteristics, and… More >

Tariq Ahamed Ahanger^1,*, Abdulaziz Aldaej¹, Mohammed Atiquzzaman², Imdad Ullah¹, Mohammed Yousuf Uddin¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3199-3217, 2022, DOI:10.32604/cmc.2022.027212 - 16 June 2022

Abstract DDoS attacks in the Internet of Things (IoT) technology have increased significantly due to its spread adoption in different industrial domains. The purpose of the current research is to propose a novel technique for detecting botnet attacks in user-oriented IoT environments. Conspicuously, an attack identification technique inspired by Recurrent Neural networks and Bidirectional Long Short Term Memory (BLRNN) is presented using a unique Deep Learning (DL) technique. For text identification and translation of attack data segments into tokenized form, word embedding is employed. The performance analysis of the presented technique is performed in comparison to More >

Attia Qammar¹, Ahmad Karim^1,*, Yasser Alharbi², Mohammad Alsaffar², Abdullah Alharbi²

Computer Systems Science and Engineering, Vol.43, No.3, pp. 915-930, 2022, DOI:10.32604/csse.2022.023652 - 09 May 2022

Abstract Smartphone devices particularly Android devices are in use by billions of people everywhere in the world. Similarly, this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control (C&C) method to expand malicious activities. At present, mobile botnet attacks launched the Distributed denial of services (DDoS) that causes to steal of sensitive data, remote access, and spam generation, etc. Consequently, various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic analysis. In this paper, a novel hybrid model, the combination More >

Ye-Eun Kim¹, Min-Gyu Kim², Hwankuk Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4467-4488, 2022, DOI:10.32604/cmc.2022.026581 - 21 April 2022

Abstract As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type More >