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  • Open Access

    ARTICLE

    Intrusion Detection System Using FKNN and Improved PSO

    Raniyah Wazirali*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1429-1445, 2021, DOI:10.32604/cmc.2021.014172 - 05 February 2021

    Abstract Intrusion detection system (IDS) techniques are used in cybersecurity to protect and safeguard sensitive assets. The increasing network security risks can be mitigated by implementing effective IDS methods as a defense mechanism. The proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor (FKNN) algorithm. Using this method, two parameters, i.e., the neighborhood size (k) and fuzzy strength parameter (m) were characterized by implementing the particle swarm optimization (PSO). In addition to being used for FKNN parametric optimization, PSO is also used for selecting the conditional feature subsets for… More >

  • Open Access

    ARTICLE

    Enhancing Network Intrusion Detection Model Using Machine Learning Algorithms

    Nancy Awadallah Awad*

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 979-990, 2021, DOI:10.32604/cmc.2021.014307 - 12 January 2021

    Abstract After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99… More >

  • Open Access

    ARTICLE

    Robust Attack Detection Approach for IIoT Using Ensemble Classifier

    V. Priya1, I. Sumaiya Thaseen1, Thippa Reddy Gadekallu1, Mohamed K. Aboudaif2,*, Emad Abouel Nasr3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2457-2470, 2021, DOI:10.32604/cmc.2021.013852 - 28 December 2020

    Abstract Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition,… More >

  • Open Access

    ARTICLE

    Deep Learning Based Optimal Multimodal Fusion Framework for Intrusion Detection Systems for Healthcare Data

    Phong Thanh Nguyen1, Vy Dang Bich Huynh2, Khoa Dang Vo1, Phuong Thanh Phan1, Mohamed Elhoseny3, Dac-Nhuong Le4,5,*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2555-2571, 2021, DOI:10.32604/cmc.2021.012941 - 28 December 2020

    Abstract Data fusion is a multidisciplinary research area that involves different domains. It is used to attain minimum detection error probability and maximum reliability with the help of data retrieved from multiple healthcare sources. The generation of huge quantity of data from medical devices resulted in the formation of big data during which data fusion techniques become essential. Securing medical data is a crucial issue of exponentially-pacing computing world and can be achieved by Intrusion Detection Systems (IDS). In this regard, since singular-modality is not adequate to attain high detection rate, there is a need exists… More >

  • Open Access

    ARTICLE

    A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

    Amir Haider1, Muhammad Adnan Khan2, Abdur Rehman3, Muhib Ur Rahman4, Hyung Seok Kim1,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1785-1798, 2021, DOI:10.32604/cmc.2020.013910 - 26 November 2020

    Abstract In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in More >

  • Open Access

    ARTICLE

    A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

    Youseef Alotaibi*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1879-1895, 2021, DOI:10.32604/cmc.2020.013739 - 26 November 2020

    Abstract A new secured database management system architecture using intrusion detection systems (IDS) is proposed in this paper for organizations with no previous role mapping for users. A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm. A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’ profiles. Then, queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious. The IDS… More >

  • Open Access

    ARTICLE

    Improving the Detection Rate of Rarely Appearing Intrusions in Network-Based Intrusion Detection Systems

    Eunmok Yang1, Gyanendra Prasad Joshi2, Changho Seo3,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 1647-1663, 2021, DOI:10.32604/cmc.2020.013210 - 26 November 2020

    Abstract In network-based intrusion detection practices, there are more regular instances than intrusion instances. Because there is always a statistical imbalance in the instances, it is difficult to train the intrusion detection system effectively. In this work, we compare intrusion detection performance by increasing the rarely appearing instances rather than by eliminating the frequently appearing duplicate instances. Our technique mitigates the statistical imbalance in these instances. We also carried out an experiment on the training model by increasing the instances, thereby increasing the attack instances step by step up to 13 levels. The experiments included not… More >

  • Open Access

    ARTICLE

    A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

    Lewis Nkenyereye1, Bayu Adhi Tama2, Sunghoon Lim3,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432 - 26 November 2020

    Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study More >

  • Open Access

    ARTICLE

    Anomaly Classification Using Genetic Algorithm-Based Random Forest Model for Network Attack Detection

    Adel Assiri*

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 767-778, 2021, DOI:10.32604/cmc.2020.013813 - 30 October 2020

    Abstract Anomaly classification based on network traffic features is an important task to monitor and detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using machine learning (ML) methods are effective tools for protecting network infrastructures and services from unpredictable and unseen attacks. Among several ML methods, random forest (RF) is a robust method that can be used in ML-based network intrusion detection solutions. However, the minimum number of instances for each split and the number of trees in the forest are two key parameters of RF that can affect classification accuracy. Therefore, optimal parameter selection… More >

  • Open Access

    ARTICLE

    Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine

    Muhammad Adnan Khan1,*, Abdur Rehman2, Khalid Masood Khan1, Mohammed A. Al Ghamdi3, Sultan H. Almotiri3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 467-480, 2021, DOI:10.32604/cmc.2020.013121 - 30 October 2020

    Abstract Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system (IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding advancements of growth, current intrusion detection systems also experience dif- ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several researchers concentrated on designing intrusion detection systems that rely on machine learning approaches. Machine learning models will accurately identify the underlying variations… More >

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