Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (191)
  • Open Access

    ARTICLE

    An Intelligent Approach for Intrusion Detection in Industrial Control System

    Adel Alkhalil1,*, Abdulaziz Aljaloud1, Diaa Uliyan1, Mohammed Altameemi1, Magdy Abdelrhman2,3, Yaser Altameemi4, Aakash Ahmad5, Romany Fouad Mansour6

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2049-2078, 2023, DOI:10.32604/cmc.2023.044506

    Abstract Supervisory control and data acquisition (SCADA) systems are computer systems that gather and analyze real-time data, distributed control systems are specially designed automated control system that consists of geographically distributed control elements, and other smaller control systems such as programmable logic controllers are industrial solid-state computers that monitor inputs and outputs and make logic-based decisions. In recent years, there has been a lot of focus on the security of industrial control systems. Due to the advancement in information technologies, the risk of cyberattacks on industrial control system has been drastically increased. Because they are so inextricably tied to human life,… More >

  • Open Access

    ARTICLE

    GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture

    Abdelwahed Berguiga1,2,*, Ahlem Harchay1,2, Ayman Massaoudi1,2, Mossaad Ben Ayed3, Hafedh Belmabrouk4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 379-402, 2023, DOI:10.32604/cmc.2023.041667

    Abstract Smart Agriculture, also known as Agricultural 5.0, is expected to be an integral part of our human lives to reduce the cost of agricultural inputs, increasing productivity and improving the quality of the final product. Indeed, the safety and ongoing maintenance of Smart Agriculture from cyber-attacks are vitally important. To provide more comprehensive protection against potential cyber-attacks, this paper proposes a new deep learning-based intrusion detection system for securing Smart Agriculture. The proposed Intrusion Detection System IDS, namely GMLP-IDS, combines the feedforward neural network Multilayer Perceptron (MLP) and the Gaussian Mixture Model (GMM) that can better protect the Smart Agriculture… More >

  • Open Access

    ARTICLE

    Modified MMS: Minimization Approach for Model Subset Selection

    C. Rajathi, P. Rukmani*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 733-756, 2023, DOI:10.32604/cmc.2023.041507

    Abstract Considering the recent developments in the digital environment, ensuring a higher level of security for networking systems is imperative. Many security approaches are being constantly developed to protect against evolving threats. An ensemble model for the intrusion classification system yielded promising results based on the knowledge of many prior studies. This research work aimed to create a more diverse and effective ensemble model. To this end, selected six classification models, Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) from existing study to run as independent models. Once the… More >

  • Open Access

    ARTICLE

    Malicious Traffic Compression and Classification Technique for Secure Internet of Things

    Yu-Rim Lee1, Na-Eun Park1, Seo-Yi Kim2, Il-Gu Lee1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3465-3482, 2023, DOI:10.32604/cmc.2023.041196

    Abstract With the introduction of 5G technology, the application of Internet of Things (IoT) devices is expanding to various industrial fields. However, introducing a robust, lightweight, low-cost, and low-power security solution to the IoT environment is challenging. Therefore, this study proposes two methods using a data compression technique to detect malicious traffic efficiently and accurately for a secure IoT environment. The first method, compressed sensing and learning (CSL), compresses an event log in a bitmap format to quickly detect attacks. Then, the attack log is detected using a machine-learning classification model. The second method, precise re-learning after CSL (Ra-CSL), comprises a… More >

  • Open Access

    ARTICLE

    A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems

    Seyoung Lee1, Wonsuk Choi1, Insup Kim2, Ganggyu Lee2, Dong Hoon Lee1,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583

    Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These metrics take into consideration various… More >

  • Open Access

    ARTICLE

    FIDS: Filtering-Based Intrusion Detection System for In-Vehicle CAN

    Seungmin Lee, Hyunghoon Kim, Haehyun Cho, Hyo Jin Jo*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2941-2954, 2023, DOI:10.32604/iasc.2023.039992

    Abstract Modern vehicles are equipped with multiple Electronic Control Units (ECUs) that support various convenient driving functions, such as the Advanced Driver Assistance System (ADAS). To enable communication between these ECUs, the Controller Area Network (CAN) protocol is widely used. However, since CAN lacks any security technologies, it is vulnerable to cyber attacks. To address this, researchers have conducted studies on machine learning-based intrusion detection systems (IDSs) for CAN. However, most existing IDSs still have non-negligible detection errors. In this paper, we propose a new filtering-based intrusion detection system (FIDS) to minimize the detection errors of machine learning-based IDSs. FIDS uses… More >

  • Open Access

    ARTICLE

    Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

    Seshu Bhavani Mallampati1, Seetha Hari2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567

    Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are utilized. The Adaptive Synthetic oversampling… More >

  • Open Access

    ARTICLE

    AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

    Anıl Sezgin1,2,*, Aytuğ Boyacı3

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2121-2143, 2023, DOI:10.32604/cmc.2023.040287

    Abstract By identifying and responding to any malicious behavior that could endanger the system, the Intrusion Detection System (IDS) is crucial for preserving the security of the Industrial Internet of Things (IIoT) network. The benefit of anomaly-based IDS is that they are able to recognize zero-day attacks due to the fact that they do not rely on a signature database to identify abnormal activity. In order to improve control over datasets and the process, this study proposes using an automated machine learning (AutoML) technique to automate the machine learning processes for IDS. Our ground-breaking architecture, known as AID4I, makes use of… 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

    An Efficient Cyber Security and Intrusion Detection System Using CRSR with PXORP-ECC and LTH-CNN

    Nouf Saeed Alotaibi*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446

    Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS… More >

Displaying 11-20 on page 2 of 191. Per Page