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  • Open Access


    Towards Generating a Practical SUNBURST Attack Dataset for Network Attack Detection

    Ehab AlMasri1, Mouhammd Alkasassbeh1, Amjad Aldweesh2,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2643-2669, 2023, DOI:10.32604/csse.2023.040626

    Abstract Supply chain attacks, exemplified by the SUNBURST attack utilizing SolarWinds Orion updates, pose a growing cybersecurity threat to entities worldwide. However, the need for suitable datasets for detecting and anticipating SUNBURST attacks is a significant challenge. We present a novel dataset collected using a unique network traffic data collection methodology to address this gap. Our study aims to enhance intrusion detection and prevention systems by understanding SUNBURST attack features. We construct realistic attack scenarios by combining relevant data and attack indicators. The dataset is validated with the J48 machine learning algorithm, achieving an average F-Measure of 87.7%. Our significant contribution… More >

  • Open Access


    A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack

    Hasan Dalmaz*, Erdal Erdal, Halil Murat Ünver

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1277-1314, 2023, DOI:10.32604/cmes.2023.025212

    Abstract This paper addresses the urgent need to detect network security attacks, which have increased significantly in recent years, with high accuracy and avoid the adverse effects of these attacks. The intrusion detection system should respond seamlessly to attack patterns and approaches. The use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational costs. To achieve better performance of these algorithms and further improve the results, hybridization of algorithms can be used, which leads to more successful results. Nowadays, many studies are conducted on this topic. In this study, a new hybrid approach using Gray Wolf Optimizer… More >

  • Open Access


    An Effective Classifier Model for Imbalanced Network Attack Data

    Gürcan Çetin*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4519-4539, 2022, DOI:10.32604/cmc.2022.031734

    Abstract Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these two concerns. The number of… More >

  • Open Access


    Automatic Botnet Attack Identification Based on Machine Learning

    Peng Hui Li1, Jie Xu1,*, 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

    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 the automatic identification of network… More >

  • Open Access


    A Network Security Risk Assessment Method Based on a B_NAG Model

    Hui Wang1, Chuanhan Zhu1, Zihao Shen1,*, Dengwei Lin2, Kun Liu1, MengYao Zhao3

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 103-117, 2021, DOI:10.32604/csse.2021.014680

    Abstract Computer networks face a variety of cyberattacks. Most network attacks are contagious and destructive, and these types of attacks can be harmful to society and computer network security. Security evaluation is an effective method to solve network security problems. For accurate assessment of the vulnerabilities of computer networks, this paper proposes a network security risk assessment method based on a Bayesian network attack graph (B_NAG) model. First, a new resource attack graph (RAG) and the algorithm E-Loop, which is applied to eliminate loops in the B_NAG, are proposed. Second, to distinguish the confusing relationships between nodes of the attack graph… More >

  • Open Access


    RP-NBSR: A Novel Network Attack Detection Model Based on Machine Learning

    Zihao Shen1,2, Hui Wang1,*, Kun Liu1, Peiqian Liu1, Menglong Ba1, MengYao Zhao3

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 121-133, 2021, DOI:10.32604/csse.2021.014988

    Abstract The rapid progress of the Internet has exposed networks to an increased number of threats. Intrusion detection technology can effectively protect network security against malicious attacks. In this paper, we propose a ReliefF-P-Naive Bayes and softmax regression (RP-NBSR) model based on machine learning for network attack detection to improve the false detection rate and F1 score of unknown intrusion behavior. In the proposed model, the Pearson correlation coefficient is introduced to compensate for deficiencies in correlation analysis between features by the ReliefF feature selection algorithm, and a ReliefF-Pearson correlation coefficient (ReliefF-P) algorithm is proposed. Then, the Relief-P algorithm is used… More >

  • Open Access


    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

    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 is a real problem in… More >

  • Open Access


    A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation

    Deyin Li1, 2, Mingzhi Cheng3, Yu Yang1, 2, *, Min Lei1, 2, Linfeng Shen4

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 623-635, 2020, DOI:10.32604/cmc.2020.09800

    Abstract Deep learning networks are widely used in various systems that require classification. However, deep learning networks are vulnerable to adversarial attacks. The study on adversarial attacks plays an important role in defense. Black-box attacks require less knowledge about target models than white-box attacks do, which means black-box attacks are easier to launch and more valuable. However, the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images. This paper proposes a kind of fast black-box attack based on the cross-correlation (FBACC) method. The attack is carried out in two stages.… More >

  • Open Access


    Defense Strategies Against Network Attacks in Cyber-Physical Systems with Analysis Cost Constraint Based on Honeypot Game Model

    Wen Tian1, Xiaopeng Ji1,*, Weiwei Liu1, Guangjie Liu1, Rong Lin1,2, Jiangtao Zhai3, Yuewei Dai3

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 193-211, 2019, DOI:10.32604/cmc.2019.05290

    Abstract Cyber-physical system (CPS) is an advanced system that integrats physical processes, computation and communication resources. The security of cyber-physical systems has become an active research area in recent years. In this paper, we focus on defensive strategies against network attacks in CPS. We introduce both low- and highinteraction honeypots into CPS as a security management tool deliberately designed to be probed, attacked and compromised. In addition, an analysis resource constraint is introduced for the purpose of optimizing defensive strategies against network attacks in CPS. We study the offensive and defensive interactions of CPS and model the offensive and defensive process… More >

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