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 >

Jae Dong Lee^1,2, Hyo Soung Cha¹, Shailendra Rathore², Jong Hyuk Park^2,*

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1537-1553, 2021, DOI:10.32604/cmc.2021.014774

Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More >

Yussuf Ahmed^1,*, A.Taufiq Asyhari¹, Md Arafatur Rahman²

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2497-2513, 2021, DOI:10.32604/cmc.2021.014223

Abstract The number of cybersecurity incidents is on the rise despite significant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availability of powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings processing power closer to the… 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 >

Nancy Awadallah Awad^*

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

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 intrusion dataset. Our study focuses… More >

V. Priya¹, I. Sumaiya Thaseen¹, Thippa Reddy Gadekallu¹, Mohamed K. Aboudaif^2,*, Emad Abouel Nasr³

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

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, outlier analysis, and speech recognition.… 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 >

Amir Haider¹, Muhammad Adnan Khan², Abdur Rehman³, Muhib Ur Rahman⁴, Hyung Seok Kim^1,*

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

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 this article an intrusion detection… More >

Youseef Alotaibi^*

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

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 will stop query execution or… More >

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 >