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Search Results (16)
  • Open Access

    ARTICLE

    Detecting Android Botnet Applications Using Convolution Neural Network

    Mamona Arshad1, Ahmad Karim1, Salman Naseer2, Shafiq Ahmad3, Mejdal Alqahtani3, Akber Abid Gardezi4, Muhammad Shafiq5,*, Jin-Ghoo Choi5

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680

    Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A… More >

  • Open Access

    ARTICLE

    Design the IoT Botnet Defense Process for Cybersecurity in Smart City

    Donghyun Kim1, Seungho Jeon2, Jiho Shin3, Jung Taek Seo4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2979-2997, 2023, DOI:10.32604/iasc.2023.040019

    Abstract The smart city comprises various infrastructures, including healthcare, transportation, manufacturing, and energy. A smart city’s Internet of Things (IoT) environment constitutes a massive IoT environment encompassing numerous devices. As many devices are installed, managing security for the entire IoT device ecosystem becomes challenging, and attack vectors accessible to attackers increase. However, these devices often have low power and specifications, lacking the same security features as general Information Technology (IT) systems, making them susceptible to cyberattacks. This vulnerability is particularly concerning in smart cities, where IoT devices are connected to essential support systems such as healthcare and transportation. Disruptions can lead… More >

  • Open Access

    ARTICLE

    MBB-IoT: Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

    Yi Qing1, Xiangyu Liu2, Yanhui Du2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2095-2119, 2023, DOI:10.32604/cmc.2023.039980

    Abstract Distributed Denial of Service (DDoS) attacks have always been a major concern in the security field. With the release of malware source codes such as BASHLITE and Mirai, Internet of Things (IoT) devices have become the new source of DDoS attacks against many Internet applications. Although there are many datasets in the field of IoT intrusion detection, such as Bot-IoT, Constrained Application Protocol–Denial of Service (CoAP-DoS), and LATAM-DDoS-IoT (some of the names of DDoS datasets), which mainly focus on DDoS attacks, the datasets describing new IoT DDoS attack scenarios are extremely rare, and only N-BaIoT and IoT-23 datasets used IoT… More >

  • Open Access

    ARTICLE

    Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

    Muhammad Waqas Nadeem1,*, Hock Guan Goh1, Yichiet Aun1, Vasaki Ponnusamy2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2201-2217, 2023, DOI:10.32604/csse.2023.039893

    Abstract Over the past few years, rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems. As a result, greater intelligence is necessary to effectively manage, optimize, and maintain these systems. Due to their distributed nature, machine learning models are challenging to deploy in traditional networks. However, Software-Defined Networking (SDN) presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes. SDN provides a centralized network view and allows for dynamic updates of flow rules and software-based traffic analysis. While the programmable nature of SDN makes… More >

  • Open Access

    ARTICLE

    Mirai Botnet Attack Detection in Low-Scale Network Traffic

    Ebu Yusuf GÜVEN, Zeynep GÜRKAŞ-AYDIN*

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

    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 Mirai attack on IoT devices.… More >

  • Open Access

    ARTICLE

    Monitoring Peer-to-Peer Botnets: Requirements, Challenges, and Future Works

    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

    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 monitoring is an exacting mission… More >

  • Open Access

    ARTICLE

    IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

    Saeed Masoud Alshahrani1, Fatma S. Alrayes2, Hamed Alqahtani3, Jaber S. Alzahrani4, Mohammed Maray5, Sana Alazwari6, Mohamed A. Shamseldin7, Mesfer Al Duhayyim8,*

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

    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 (ML) and Deep Learning (DL)… More >

  • Open Access

    ARTICLE

    Preventing Cloud Network from Spamming Attacks Using Cloudflare and KNN

    Muhammad Nadeem1, Ali Arshad2, Saman Riaz2, SyedaWajiha Zahra1, Muhammad Rashid2, Shahab S. Band3,*, Amir Mosavi4,5,6

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

    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 intercepted through two effective techniques:… More >

  • Open Access

    ARTICLE

    Improved Ant Colony Optimization and Machine Learning Based Ensemble Intrusion Detection Model

    S. Vanitha1,*, P. Balasubramanie2

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

    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 and detection of attacks. New… More >

  • Open Access

    ARTICLE

    BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

    Khlood Shinan1,2, Khalid Alsubhi2, M. Usman Ashraf3,*

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

    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 is to build a botnet… More >

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