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    Item type:Publication,
    Port cyberattacks from 2011 to 2023: a literature review and discussion of selected cases
    (2024-03-01)
    Senarak, Chalermpong
    Cyberattacks pose a significant threat to seaports worldwide. While numerous scholars have endeavored to address these threats, their findings are often limited in practicality, applying to only a few ports. This paper aims to contribute to the extant literature by presenting and discussing 15 case studies of port cyberattacks that occurred from 2011 to 2023. These cases encompass six instances in Europe, six in America, two in Asia, and one in Africa. The study employs a twofold methodology, consisting of a comprehensive literature review and a personal assessment. Several key insights emerge. First, two primary motivations drive port cyberattacks: (a) the demand for ransom payments and (b) the desire to cause severe disruptions. Second, ransomware was the most common threat used to extort payments, followed by malware and DDoS attacks, which aimed to inflict operational disruption. Third, weaknesses in cybersecurity procedures and a lack of awareness among staff members emerged as significant contributors to cyber vulnerabilities. Forth, ports that quickly detected threats and implemented response measures were able to minimize operational impacts, ensuring the swift resumption of services and the maintenance of service continuity. Fifth, collaborative efforts with external partners and officials expedited response and recovery processes by facilitating threat investigation and solution identification. In light of these findings, it becomes imperative for all ports to develop comprehensive cyber disaster management plans, covering four key phases: preparedness, response, recovery, and mitigation. These plans should encompass three critical aspects of cybersecurity hygiene: human factors, procedural improvements, and infrastructure enhancements. Such practices are instrumental in bolstering a port’s resilience and mitigating the risks associated with cyberattacks.
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    A new incremental decision tree learning for cyber security based on ILDA and mahalanobis distance
    (2019-01-01)
    Jaiyen, Saichon
    ;
    Sornsuwit, Ployphan
    A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.