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  • Open Access

    ARTICLE

    Framework for Cybersecurity Centers to Mass Scan Networks

    Waiel M. Eid1,2, Samer Atawneh1, Mousa Al-Akhras1,3,*

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1319-1334, 2020, DOI:10.32604/iasc.2020.013678

    Abstract The huge number of devices available in cyberspace and the increasing number of security vulnerabilities discovered daily have added many difficulties in keeping track of security vulnerabilities, especially when not using special security tools and software. Mass scanning of the Internet has opened a broad range of possibilities for security tools that help cybersecurity centers detect weaknesses and vulnerabilities in cyberspace. However, one critical issue faced by national cybersecurity centers is the collection of information about IP addresses and subnet ranges. To develop a data collection mechanism for such information and maintain this information with continuous updates, a scanning system… More >

  • Open Access

    ARTICLE

    A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

    Amir Haider1, Muhammad Adnan Khan2, Abdur Rehman3, Muhib Ur Rahman4, Hyung Seok Kim1,*

    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 >

  • Open Access

    ARTICLE

    Adversarial Active Learning for Named Entity Recognition in Cybersecurity

    Tao Li1, Yongjin Hu1,*, Ankang Ju1, Zhuoran Hu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 407-420, 2021, DOI:10.32604/cmc.2020.012023

    Abstract Owing to the continuous barrage of cyber threats, there is a massive amount of cyber threat intelligence. However, a great deal of cyber threat intelligence come from textual sources. For analysis of cyber threat intelligence, many security analysts rely on cumbersome and time-consuming manual efforts. Cybersecurity knowledge graph plays a significant role in automatics analysis of cyber threat intelligence. As the foundation for constructing cybersecurity knowledge graph, named entity recognition (NER) is required for identifying critical threat-related elements from textual cyber threat intelligence. Recently, deep neural network-based models have attained very good results in NER. However, the performance of these… More >

  • Open Access

    ARTICLE

    Multilayer Self-Defense System to Protect Enterprise Cloud

    Shailendra Mishra, Sunil Kumar Sharma*, Majed A. Alowaidi

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 71-85, 2021, DOI:10.32604/cmc.2020.012475

    Abstract A data breach can seriously impact organizational intellectual property, resources, time, and product value. The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols. As TCP relies on IP addresses, an attacker may easily trace the IP address of the organization. Given that many organizations run the risk of data breach and cyber-attacks at a certain point, a repeatable and well-developed incident response framework is critical to shield them. Enterprise cloud possesses the challenges of security, lack of transparency, trust and loss of controls. Technology eases quickens the processing of information but… More >

  • Open Access

    ARTICLE

    Identifying and Verifying Vulnerabilities through PLC Network Protocol and Memory Structure Analysis

    Joo-Chan Lee1, Hyun-Pyo Choi1, Jang-Hoon Kim1, Jun-Won Kim1, Da-Un Jung1, Ji-Ho Shin1, Jung-Taek Seo1, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 53-67, 2020, DOI:10.32604/cmc.2020.011251

    Abstract Cyberattacks on the Industrial Control System (ICS) have recently been increasing, made more intelligent by advancing technologies. As such, cybersecurity for such systems is attracting attention. As a core element of control devices, the Programmable Logic Controller (PLC) in an ICS carries out on-site control over the ICS. A cyberattack on the PLC will cause damages on the overall ICS, with Stuxnet and Duqu as the most representative cases. Thus, cybersecurity for PLCs is considered essential, and many researchers carry out a variety of analyses on the vulnerabilities of PLCs as part of preemptive efforts against attacks. In this study,… More >

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