Eunmok Yang¹, Gyanendra Prasad Joshi², Changho Seo^3,*

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

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 only known attacks, but also… More >

Lewis Nkenyereye¹, Bayu Adhi Tama², Sunghoon Lim^3,*

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

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 is to emphasize the effectiveness… More >

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 >

Muhammad Adnan Khan^1,*, 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

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 among regular information and irregular… More >

Xinda Hao¹, Jianmin Zhou^2,*, Xueqi Shen¹, Yu Yang¹

Journal of Quantum Computing, Vol.2, No.2, pp. 97-104, 2020, DOI:10.32604/jqc.2020.010819

Abstract In recent years, machine learning technology has been widely used for timely network attack detection and classification. However, due to the large number of network traffic and the complex and variable nature of malicious attacks, many challenges have arisen in the field of network intrusion detection. Aiming at the problem that massive and high-dimensional data in cloud computing networks will have a negative impact on anomaly detection, this paper proposes a Bi-LSTM method based on attention mechanism, which learns by transmitting IDS data to multiple hidden layers. Abstract information and high-dimensional feature representation in network data messages are used to… More >

Rashid Amin^{1, *}, Mudassar Hussain², Mohammed Alhameed³, Syed Mohsan Raza⁴, Fathe Jeribi³, Ali Tahir³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 1869-1890, 2020, DOI:10.32604/cmc.2020.011758

Abstract Software Defined Networking (SDN) being an emerging network control model is widely recognized as a control and management platform. This model provides efficient techniques to control and manage the enterprise network. Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller. This data processing at the edge nodes reduces the latency and bandwidth requirements. In SDN, the controller is a single point of failure. Several security issues related to the traditional network can be solved by using SDN central management and control. Address Spoofing and Network Intrusion… More >

Iftikhar Ahmad^{1, *}, Rayan Atteah Alsemmeari¹

CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1097-1111, 2020, DOI:10.32604/cmc.2020.011732

Abstract An IDS (intrusion detection system) provides a foremost front line mechanism to guard networks, systems, data, and information. That’s why intrusion detection has grown as an active study area and provides significant contribution to cyber-security techniques. Multiple techniques have been in use but major concern in their implementation is variation in their detection performance. The performance of IDS lies in the accurate detection of attacks, and this accuracy can be raised by improving the recognition rate and significant reduction in the false alarms rate. To overcome this problem many researchers have used different machine learning techniques. These techniques have limitations… More >

Usman Tariq

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 231-242, 2019, DOI:10.31209/2018.100000040

Abstract Wireless Sensor Networks (WSNs) empower the reflection of the environment with an extraordinary resolve. These systems are combination of several minuscule squat-cost, and stumpy-power on-chip transceiver sensing motes. Characteristically, a sensing device comprises of four key gears: an identifying element for data attainment, a microcontroller for native data dispensation, a message component to permit the broadcast/response of data to/from additional associated hardware, and lastly, a trivial energy source. Near field frequency series and inadequate bandwidth of transceiver device drags to multi-stage data transactions at minimum achievable requirements. State of art, and prevailing operating systems, such as TinyOS (Levis, et.al. 2005),… More >

Fei Li^{1, *}, Jiayan Zhang¹, Edward Szczerbicki², Jiaqi Song¹, Ruxiang Li ¹, Renhong Diao¹

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 653-681, 2020, DOI:10.32604/cmc.2020.011264

Abstract The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency… More >

Ziyong Ran¹, Desheng Zheng^{1, *}, Yanling Lai¹, Lulu Tian²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >