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  • Open Access

    ARTICLE

    Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

    U. Sakthivelu, C. N. S. Vinoth Kumar*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3691-3707, 2023, DOI:10.32604/iasc.2023.036946 - 15 March 2023

    Abstract The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s livelihood. Several cyber-attacks lead to the compromise of data security. The proposed system offers complete data protection from Advanced Persistent Threat (APT) attacks with attack detection and defence mechanisms. The modified lateral movement detection algorithm detects the APT attacks, while the defence is achieved by the Dynamic Deception system that makes use of the belief update algorithm. Before termination, every cyber-attack undergoes multiple stages, with the most prominent stage being Lateral Movement (LM). The LM uses… More >

  • Open Access

    ARTICLE

    Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

    Abdelwahed Motwakel1,*, Fadwa Alrowais2, Khaled Tarmissi3, Radwa Marzouk4, Abdullah Mohamed5, Abu Sarwar Zamani1, Ishfaq Yaseen1, Mohamed I. Eldesouki6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3157-3173, 2023, DOI:10.32604/iasc.2023.034908 - 15 March 2023

    Abstract The paradigm shift towards the Internet of Things (IoT) phenomenon and the rise of edge-computing models provide massive potential for several upcoming IoT applications like smart grid, smart energy, smart home, smart health and smart transportation services. However, it also provides a sequence of novel cyber-security issues. Although IoT networks provide several advantages, the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for cyber-attackers. Cyberattacks result in financial loss and data breaches for organizations and individuals. So, it becomes crucial to secure the IoT environment from such… More >

  • Open Access

    ARTICLE

    A Cyber-Attack Detection System Using Late Fusion Aggregation Enabled Cyber-Net

    P. Shanmuga Prabha*, S. Magesh Kumar

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3101-3119, 2023, DOI:10.32604/iasc.2023.034885 - 15 March 2023

    Abstract Today, securing devices connected to the internet is challenging as security threats are generated through various sources. The protection of cyber-physical systems from external attacks is a primary task. The presented method is planned on the prime motive of detecting cybersecurity attacks and their impacted parameters. The proposed architecture employs the LYSIS dataset and formulates Multi Variant Exploratory Data Analysis (MEDA) through Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) for the extraction of unique parameters. The feature mappings are analyzed with Recurrent 2 Convolutional Neural Network (R2CNN) and Gradient Boost Regression (GBR) to More >

  • Open Access

    ARTICLE

    An Improved LSTM-PCA Ensemble Classifier for SQL Injection and XSS Attack Detection

    Deris Stiawan1, Ali Bardadi1, Nurul Afifah1, Lisa Melinda1, Ahmad Heryanto1, Tri Wanda Septian1, Mohd Yazid Idris2, Imam Much Ibnu Subroto3, Lukman4, Rahmat Budiarto5,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1759-1774, 2023, DOI:10.32604/csse.2023.034047 - 09 February 2023

    Abstract The Repository Mahasiswa (RAMA) is a national repository of research reports in the form of final assignments, student projects, theses, dissertations, and research reports of lecturers or researchers that have not yet been published in journals, conferences, or integrated books from the scientific repository of universities and research institutes in Indonesia. The increasing popularity of the RAMA Repository leads to security issues, including the two most widespread, vulnerable attacks i.e., Structured Query Language (SQL) injection and cross-site scripting (XSS) attacks. An attacker gaining access to data and performing unauthorized data modifications is extremely dangerous. This… More >

  • Open Access

    ARTICLE

    A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack

    Hasan Dalmaz*, Erdal Erdal, Halil Murat Ünver

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1277-1314, 2023, DOI:10.32604/cmes.2023.025212 - 06 February 2023

    Abstract This paper addresses the urgent need to detect network security attacks, which have increased significantly in recent years, with high accuracy and avoid the adverse effects of these attacks. The intrusion detection system should respond seamlessly to attack patterns and approaches. The use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational costs. To achieve better performance of these algorithms and further improve the results, hybridization of algorithms can be used, which leads to more successful results. Nowadays, many studies are conducted on this topic. In this study, a new hybrid… More >

  • Open Access

    ARTICLE

    Optimal Hybrid Deep Learning Enabled Attack Detection and Classification in IoT Environment

    Fahad F. Alruwaili*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 99-115, 2023, DOI:10.32604/cmc.2023.034752 - 06 February 2023

    Abstract The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so… More >

  • Open Access

    ARTICLE

    A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

    Maha Sabir1, Jawad Ahmad2,*, Daniyal Alghazzawi1

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277 - 20 January 2023

    Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based… More >

  • Open Access

    ARTICLE

    Modified Adhoc On-Demand Distance Vector for Trust Evaluation And Attack Detection

    S. Soundararajan1,*, B. R. Tapas Bapu2, C. Kotteeswaran1, S. Venkatasubramanian3, P. J. Sathish Kumar4, Ahmed Mudassar Ali2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1227-1240, 2023, DOI:10.32604/iasc.2023.025752 - 05 January 2023

    Abstract Recently, Wireless Sensor Network (WSN) becomes most potential technologies for providing improved services to several data gathering and tracking applications. Because of the wireless medium, multi-hop communication, absence of physical protectivity, and accumulated traffic, WSN is highly vulnerable to security concerns. Therefore, this study explores a specific type of DoS attack identified as a selective forwarding attack where the misbehaving node in the network drops packet on a selective basis. It is challenging to determine if packet loss is caused by a collision in the medium access path, poor channel quality, or a selective forwarding More >

  • Open Access

    ARTICLE

    Probe Attack Detection Using an Improved Intrusion Detection System

    Abdulaziz Almazyad, Laila Halman, Alaa Alsaeed*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4769-4784, 2023, DOI:10.32604/cmc.2023.033382 - 28 December 2022

    Abstract The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems… More >

  • Open Access

    ARTICLE

    EsECC_SDN: Attack Detection and Classification Model for MANET

    Veera Ankalu Vuyyuru1, Youseef Alotaibi2, Neenavath Veeraiah3,*, Saleh Alghamdi4, Korimilli Sirisha5

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6665-6688, 2023, DOI:10.32604/cmc.2023.032140 - 28 December 2022

    Abstract Mobile Ad Hoc Networks (MANET) is the framework for social networking with a realistic framework. In the MANET environment, based on the query, information is transmitted between the sender and receiver. In the MANET network, the nodes within the communication range are involved in data transmission. Even the nodes that lie outside of the communication range are involved in the transmission of relay messages. However, due to the openness and frequent mobility of nodes, they are subjected to the vast range of security threats in MANET. Hence, it is necessary to develop an appropriate security… More >

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