Ziadoon K. Maseer¹, Robiah Yusof¹, Salama A. Mostafa^2,*, Nazrulazhar Bahaman¹, Omar Musa³, Bander Ali Saleh Al-rimy⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3945-3966, 2021, DOI:10.32604/cmc.2021.016074

Abstract With an increasing number of services connected to the internet, including cloud computing and Internet of Things (IoT) systems, the prevention of cyberattacks has become more challenging due to the high dimensionality of the network traffic data and access points. Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. However, due to the high dynamics and imbalanced nature of the data, the existing DL classifiers are not completely effective at distinguishing between abnormal and normal behavior line connections for modern networks. Therefore, it is important to design… More >

Yanyan Zhang¹, Xiangjin Ran^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.3, pp. 1231-1245, 2021, DOI:10.32604/cmes.2021.016866

Abstract In the network security field, the network intrusion detection system (NIDS) is considered one of the critical issues in the detection accuracy and missed detection rate. In this paper, a method of two-step network intrusion detection on the basis of GoogLeNet Inception and deep convolutional neural networks (CNNs) models is proposed. The proposed method used the GoogLeNet Inception model to identify the network packets’ binary problem. Subsequently, the characteristics of the packets’ raw data and the traffic features are extracted. The CNNs model is also used to identify the multiclass intrusions by the network packets’ features. In the experimental results,… More >

Farouq Aliyu^1,*, Tarek Sheltami¹, Ashraf Mahmoud¹, Louai Al-Awami¹, Ansar Yasar²

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1159-1181, 2021, DOI:10.32604/cmc.2021.016938

Abstract Fog computing (FC) is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network (close to the Internet of Things (IoT) devices). Fog nodes provide services in lieu of the cloud. Thus, improving the performance of the network and making it attractive to social media-based systems. Security issues are one of the most challenges encountered in FC. In this paper, we propose an anomaly-based Intrusion Detection and Prevention System (IDPS) against Man-in-the-Middle (MITM) attack in the fog layer. The system uses special nodes known as Intrusion Detection System (IDS) nodes to detect… More >

Nojood O. Aljehane^*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3915-3929, 2021, DOI:10.32604/cmc.2021.017905

Abstract Cyber physical systems (CPSs) are a networked system of cyber (computation, communication) and physical (sensors, actuators) elements that interact in a feedback loop with the assistance of human interference. Generally, CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices. Increased utilization of CPSs, however, poses many threats, which may be of major significance for users. Such security issues in CPSs represent a global issue; therefore, developing a robust, secure, and effective CPS is currently a hot research topic. To resolve this issue, an intrusion… More >

Thavavel Vaiyapuri^*, Adel Binbusayyis

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3271-3288, 2021, DOI:10.32604/cmc.2021.017665

Abstract In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the… More >

Omar Almomani^*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 409-429, 2021, DOI:10.32604/cmc.2021.016113

Abstract Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one… More >

Muhammad Ashfaq Khan^1,2, Yangwoo Kim^1,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 671-687, 2021, DOI:10.32604/cmc.2021.015647

Abstract Machine learning (ML) algorithms are often used to design effective intrusion detection (ID) systems for appropriate mitigation and effective detection of malicious cyber threats at the host and network levels. However, cybersecurity attacks are still increasing. An ID system can play a vital role in detecting such threats. Existing ID systems are unable to detect malicious threats, primarily because they adopt approaches that are based on traditional ML techniques, which are less concerned with the accurate classification and feature selection. Thus, developing an accurate and intelligent ID system is a priority. The main objective of this study was to develop… More >

Sinil Mubarak¹, Mohamed Hadi Habaebi^1,*, Md Rafiqul Islam¹, Farah Diyana Abdul Rahman, Mohammad Tahir²

Computer Systems Science and Engineering, Vol.37, No.1, pp. 33-46, 2021, DOI:10.32604/csse.2021.014384

Abstract An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of cyber-attacks in SCADA systems, as… More >

Raniyah Wazirali^*

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

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 detection. To proficiently regulate the… More >

Phong Thanh Nguyen¹, Vy Dang Bich Huynh², Khoa Dang Vo¹, Phuong Thanh Phan¹, Mohamed Elhoseny³, Dac-Nhuong Le^4,5,*

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

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 to merge diverse techniques using… More >