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  • Open Access

    ARTICLE

    An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

    A. Arivazhagi1,*, S. Raja Kumar2

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 141-157, 2022, DOI:10.32604/csse.2022.021851 - 23 March 2022

    Abstract Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear… More >

  • Open Access

    ARTICLE

    Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

    S. Navaneetha Krishnan1, P. Sundara Vadivel2,*, D. Yuvaraj3, T. Satyanarayana Murthy4, Sree Jagadeesh Malla5, S. Nachiyappan6, S. Shanmuga Priya7

    Computer Systems Science and Engineering, Vol.43, No.1, pp. 17-26, 2022, DOI:10.32604/csse.2022.021590 - 23 March 2022

    Abstract Wireless Sensor Networks (WSN) are commonly used to observe and monitor precise environments. WSNs consist of a large number of inexpensive sensor nodes that have been separated and distributed in different environments. The base station received the amount of data collected by the numerous sensors. The current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large scale. The Trust-Based Adaptive Acknowledgement (TRAACK) Intrusion-Detection System for Wireless Sensor Networks (WSN) is described based on the number of active positive deliveries and The Kalman filter used in Modified More >

  • Open Access

    ARTICLE

    Cyber Security Analysis and Evaluation for Intrusion Detection Systems

    Yoosef B. Abushark1, Asif Irshad Khan1,*, Fawaz Alsolami1, Abdulmohsen Almalawi1, Md Mottahir Alam2, Alka Agrawal3, Rajeev Kumar4, Raees Ahmad Khan3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1765-1783, 2022, DOI:10.32604/cmc.2022.025604 - 24 February 2022

    Abstract Machine learning is a technique that is widely employed in both the academic and industrial sectors all over the world. Machine learning algorithms that are intuitive can analyse risks and respond swiftly to breaches and security issues. It is crucial in offering a proactive security system in the field of cybersecurity. In real time, cybersecurity protects information, information systems, and networks from intruders. In the recent decade, several assessments on security and privacy estimates have noted a rapid growth in both the incidence and quantity of cybersecurity breaches. At an increasing rate, intruders are breaching… More >

  • Open Access

    ARTICLE

    Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

    Anwer Mustafa Hilal1,*, Jaber S. Alzahrani2, Ibrahim Abunadi3, Nadhem Nemri4, Fahd N. Al-Wesabi5,6, Abdelwahed Motwakel1, Ishfaq Yaseen1, Abu Sarwar Zamani1

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 333-348, 2022, DOI:10.32604/cmc.2022.024794 - 24 February 2022

    Abstract In recent times, Industrial Internet of Things (IIoT) experiences a high risk of cyber attacks which needs to be resolved. Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks. Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network, the performance arrived at, in existing studies still needs improvement. In this scenario, the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT (PPBDL-IIoT) on 6G environment. The proposed PPBDL-IIoT technique aims… More >

  • Open Access

    ARTICLE

    Anomaly Detection for Internet of Things Cyberattacks

    Manal Alanazi*, Ahamed Aljuhani

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 261-279, 2022, DOI:10.32604/cmc.2022.024496 - 24 February 2022

    Abstract The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT… More >

  • Open Access

    ARTICLE

    Deep Learning Based Intrusion Detection in Cloud Services for Resilience Management

    S. Sreenivasa Chakravarthi1,*, R. Jagadeesh Kannan2, V. Anantha Natarajan3, Xiao-Zhi Gao4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5117-5133, 2022, DOI:10.32604/cmc.2022.022351 - 14 January 2022

    Abstract In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology, and invest in building infrastructure. All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation. At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries. As part of the innovations happening in the industrial sector, a new concept termed as ‘smart manufacturing’ has emerged, which employs the benefits of emerging technologies like internet… More >

  • Open Access

    ARTICLE

    Robust Node Localization with Intrusion Detection for Wireless Sensor Networks

    R. Punithavathi1, R. Thanga Selvi2, R. Latha3, G. Kadiravan4,*, V. Srikanth5, Neeraj Kumar Shukla6

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 143-156, 2022, DOI:10.32604/iasc.2022.023344 - 05 January 2022

    Abstract Wireless sensor networks comprise a set of autonomous sensor nodes, commonly used for data gathering and tracking applications. Node localization and intrusion detection are considered as the major design issue in WSN. Therefore, this paper presents a new multi-objective manta ray foraging optimization (MRFO) based node localization with intrusion detection (MOMRFO-NLID) technique for WSN. The goal of the MOMRFO-NLID technique is to optimally localize the unknown nodes and determine the existence of intrusions in the network. The MOMRFO-NLID technique encompasses two major stages namely MRFO based localization of nodes and optimal Siamese Neural Network (OSNN) More >

  • Open Access

    ARTICLE

    A Novel Hybrid Deep Learning Framework for Intrusion Detection Systems in WSN-IoT Networks

    M. Maheswari1,2,*, R. A. Karthika1

    Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 365-382, 2022, DOI:10.32604/iasc.2022.022259 - 05 January 2022

    Abstract With the advent of wireless communication and digital technology, low power, Internet-enabled, and reconfigurable wireless devices have been developed, which revolutionized day-to-day human life and the economy across the globe. These devices are realized by leveraging the features of sensing, processing the data and nodes communications. The scale of Internet-enabled wireless devices has increased daily, and these devices are exposed to various cyber-attacks. Since the complexity and dynamics of the attacks on the devices are computationally high, intelligent, scalable and high-speed intrusion detection systems (IDS) are required. Moreover, the wireless devices are battery-driven; implementing them… More >

  • Open Access

    ARTICLE

    Optimized Fuzzy Enabled Semi-Supervised Intrusion Detection System for Attack Prediction

    Gautham Praveen Ramalingam1, R. Arockia Xavier Annie1, Shobana Gopalakrishnan2,*

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1479-1492, 2022, DOI:10.32604/iasc.2022.022211 - 09 December 2021

    Abstract Detection of intrusion plays an important part in data protection. Intruders will carry out attacks from a compromised user account without being identified. The key technology is the effective detection of sundry threats inside the network. However, process automation is experiencing expanded use of information communication systems, due to high versatility of interoperability and ease off 34 administration. Traditional knowledge technology intrusion detection systems are not completely tailored to process automation. The combined use of fuzziness-based and RNN-IDS is therefore highly suited to high-precision classification, and its efficiency is better compared to that of conventional More >

  • Open Access

    ARTICLE

    An IoT-Based Intrusion Detection System Approach for TCP SYN Attacks

    Abdelwahed Berguiga*, Ahlem Harchay

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3839-3851, 2022, DOI:10.32604/cmc.2022.023399 - 07 December 2021

    Abstract The success of Internet of Things (IoT) deployment has emerged important smart applications. These applications are running independently on different platforms, almost everywhere in the world. Internet of Medical Things (IoMT), also referred as the healthcare Internet of Things, is the most widely deployed application against COVID-19 and offering extensive healthcare services that are connected to the healthcare information technologies systems. Indeed, with the impact of the COVID-19 pandemic, a large number of interconnected devices designed to create smart networks. These networks monitor patients from remote locations as well as tracking medication orders. However, IoT… More >

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