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  • Open Access

    ARTICLE

    A Secure and Efficient Cluster-Based Authentication Scheme for Internet of Things (IoTs)

    Kanwal Imran1,*, Nasreen Anjum2, Abdullah Alghamdi3, Asadullah Shaikh3, Mohammed Hamdi3, Saeed Mahfooz1

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1033-1052, 2022, DOI:10.32604/cmc.2022.018589

    Abstract IPv6 over Low Power Wireless Personal Area Network (6LoWPAN) provides IP connectivity to the highly constrained nodes in the Internet of Things (IoTs). 6LoWPAN allows nodes with limited battery power and storage capacity to carry IPv6 datagrams over the lossy and error-prone radio links offered by the IEEE 802.15.4 standard, thus acting as an adoption layer between the IPv6 protocol and IEEE 802.15.4 network. The data link layer of IEEE 802.15.4 in 6LoWPAN is based on AES (Advanced Encryption Standard), but the 6LoWPAN standard lacks and has omitted the security and privacy requirements at higher layers. The sensor nodes in… More >

  • Open Access

    ARTICLE

    Vibration-Based Pattern Password Approach for Visually Impaired People

    Suliman A. Alsuhibany*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 341-356, 2022, DOI:10.32604/csse.2022.018563

    Abstract The pattern password method is amongst the most attractive authentication methods and involves drawing a pattern; this is seen as easier than typing a password. However, since people with visual impairments have been increasing their usage of smart devices, this method is inaccessible for them as it requires them to select points on the touch screen. Therefore, this paper exploits the haptic technology by introducing a vibration-based pattern password approach in which the vibration feedback plays an important role. This approach allows visually impaired people to use a pattern password through two developed vibration feedback: pulses, which are counted by… More >

  • Open Access

    REVIEW

    Intrusion Detection Systems Using Blockchain Technology: A Review, Issues and Challenges

    Salam Al-E’mari1, Mohammed Anbar1,*, Yousef Sanjalawe1,2, Selvakumar Manickam1, Iznan Hasbullah1

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 87-112, 2022, DOI:10.32604/csse.2022.017941

    Abstract Intrusion detection systems that have emerged in recent decades can identify a variety of malicious attacks that target networks by employing several detection approaches. However, the current approaches have challenges in detecting intrusions, which may affect the performance of the overall detection system as well as network performance. For the time being, one of the most important creative technological advancements that plays a significant role in the professional world today is blockchain technology. Blockchain technology moves in the direction of persistent revolution and change. It is a chain of blocks that covers information and maintains trust between individuals no matter… More >

  • Open Access

    ARTICLE

    DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection

    Ziadoon K. Maseer1, Robiah Yusof1, Salama A. Mostafa2,*, 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 >

  • Open Access

    ARTICLE

    Computational Intelligent Techniques To Detect DDOS Attacks : A Survey

    Isha Sood*, Varsha Sharma

    Journal of Cyber Security, Vol.3, No.2, pp. 89-106, 2021, DOI:10.32604/jcs.2021.018623

    Abstract The Internet is often targeted by the Distributed Denial of Service (DDOS) Attacks that deliberately utilize resources and bandwidth to prohibit access to potential users. The attack possibility is that the packets are filled massively. A DOS attack is launched by a single source, while a DDOS attack is originated from numerous resources. DDoS attacks are not capable of stealing website user’s information. The prime motive of the DDoS attacks is to devastate the website resources. Distributed Denial of Service (DDoS) attacks are disruptive to internet access on the Network. The attitude of the customer to get fast and reliable… More >

  • Open Access

    ARTICLE

    Entropy-Based Approach to Detect DDoS Attacks on Software Defined Networking Controller

    Mohammad Aladaileh1, Mohammed Anbar1,*, Iznan H. Hasbullah1, Yousef K. Sanjalawe1,2, Yung-Wey Chong1

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 373-391, 2021, DOI:10.32604/cmc.2021.017972

    Abstract The Software-Defined Networking (SDN) technology improves network management over existing technology via centralized network control. The SDN provides a perfect platform for researchers to solve traditional network’s outstanding issues. However, despite the advantages of centralized control, concern about its security is rising. The more traditional network switched to SDN technology, the more attractive it becomes to malicious actors, especially the controller, because it is the network’s brain. A Distributed Denial of Service (DDoS) attack on the controller could cripple the entire network. For that reason, researchers are always looking for ways to detect DDoS attacks against the controller with higher… More >

  • Open Access

    ARTICLE

    Cryptographic Based Secure Model on Dataset for Deep Learning Algorithms

    Muhammad Tayyab1,*, Mohsen Marjani1, N. Z. Jhanjhi1, Ibrahim Abaker Targio Hashim2, Abdulwahab Ali Almazroi3, Abdulaleem Ali Almazroi4

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1183-1200, 2021, DOI:10.32604/cmc.2021.017199

    Abstract Deep learning (DL) algorithms have been widely used in various security applications to enhance the performances of decision-based models. Malicious data added by an attacker can cause several security and privacy problems in the operation of DL models. The two most common active attacks are poisoning and evasion attacks, which can cause various problems, including wrong prediction and misclassification of decision-based models. Therefore, to design an efficient DL model, it is crucial to mitigate these attacks. In this regard, this study proposes a secure neural network (NN) model that provides data security during model training and testing phases. The main… More >

  • Open Access

    ARTICLE

    Analysis of Security Testing Techniques

    Omer Bin Tauqeer1, Sadeeq Jan1,*, Alaa Omar Khadidos2, Adil Omar Khadidos3, Fazal Qudus Khan3, Sana Khattak1

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 291-306, 2021, DOI:10.32604/iasc.2021.017260

    Abstract In the past decades, a significant increase has been observed in cyber-attacks on the web-based systems used for financial purposes. Such individual systems often contain security weaknesses, called vulnerabilities that can be exploited for malicious purposes. The exploitation of such vulnerabilities can result in disclosure and manipulation of sensitive data as well as have destructive effects. To protect such systems, security testing is required on a periodic basis. Various detection and assessment techniques have been suggested by developers and researchers to address these security issues. In this paper, we survey the contributions of academia in the field of security testing… More >

  • Open Access

    ARTICLE

    Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

    Muhammad Reazul Haque1, Saw Chin Tan1, Zulfadzli Yusoff2,*, Kashif Nisar3,5,6, Lee Ching Kwang2,7, Rizaludin Kaspin4, Bhawani Shankar Chowdhry5, Rajkumar Buyya8, Satya Prasad Majumder9, Manoj Gupta10, Shuaib Memon11

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3147-3165, 2021, DOI:10.32604/cmc.2021.016591

    Abstract In software-defined networks (SDNs), controller placement is a critical factor in the design and planning for the future Internet of Things (IoT), telecommunication, and satellite communication systems. Existing research has concentrated largely on factors such as reliability, latency, controller capacity, propagation delay, and energy consumption. However, SDNs are vulnerable to distributed denial of service (DDoS) attacks that interfere with legitimate use of the network. The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design, especially in critical applications such as military, health care, and financial services networks requiring high availability. We propose a mathematical… More >

  • Open Access

    ARTICLE

    Secure Multifactor Remote Access User Authentication Framework for IoT Networks

    Mohammed Mujib Alshahrani*

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3235-3254, 2021, DOI:10.32604/cmc.2021.015310

    Abstract The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require… More >

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