Multiplex collaboration networks facilitate intricate connections among individuals, enabling multidimensional collaborations across various domains and fostering synergistic knowledge exchange. This study focuses on ...
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Instagram is a social media whose users are presently increasing. The Instagram Suggested Post feature is one of the features created by Instagram to increase interest in using the application. Focus on this feature;i...
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Universities serve as hubs for knowledge acquisition, making it crucial for them to implement effective information security policies to protect their assets. This study focuses on the IT risk control assessment of th...
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ISBN:
(数字)9798331533243
ISBN:
(纸本)9798331533250
Universities serve as hubs for knowledge acquisition, making it crucial for them to implement effective information security policies to protect their assets. This study focuses on the IT risk control assessment of the computer Laboratory at the faculty of computer Science, UPN Veteran Jakarta (UPNVJ). The research aims to identify and mitigate risks that could compromise the institution's operations by applying the ISO 27001:2022 framework using the PDCA (Plan, Do, Check, Act) methodology. Through this framework, we evaluated the lab's information security measures, identifying key vulnerabilities and implementing specific controls to minimize the risk of data breaches and system failures. The findings include a detailed risk assessment with improvements in security management, though some limitations were encountered in implementation. Future research could expand the study to other departments at UPNVJ to ensure comprehensive protection across the university's IT infrastructure.
Instagram is a social media whose users are presently increasing. The Instagram Suggested Post feature is one of the features created by Instagram to increase interest in using the application. Focus on this feature; ...
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ISBN:
(纸本)9781665453967
Instagram is a social media whose users are presently increasing. The Instagram Suggested Post feature is one of the features created by Instagram to increase interest in using the application. Focus on this feature; it is necessary to test and determine the influence of the Instagram Suggested Post Algorithm on the learning behavior of Klabat University students using the Theory of Reasoned Action (TRA) model concept. The objective of the present study is to investigate the influence of Instagram's suggested post algorithm on the learning behavior of students from the faculty of computer science at Klabat University. The results of this study show that the three hypotheses proposed, namely the first hypothesis of the Use Attitude (SP) of the Instagram Suggested Post Algorithm, have an influence on Learning Interest (MB) with a t-count value of $\boldsymbol{6.800 >}$ t-table 1.98580 with a Sig value. $\boldsymbol{< 0.05}$ . While the second hypothesis Subjective Norm (NS), influences MB with a t value of $\boldsymbol{7.302 >}$ t table 1.98580 with a value of Sig. $\boldsymbol{ < 0.05}$ and the third hypothesis MB affects Learning Behavior (PB) with a t-count value of $\boldsymbol{7.407 >}$ t-table 1.98580 with a Sig value. $\boldsymbol{ < 0.05}$ , then the three hypotheses have a significant relationship; thus, these three hypotheses are accepted.
Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity ...
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Let X be a connected G-locally primitive arc-transitive graph for some sub-group G of Aut(X).Suppose that X is a(G,s)-transitive *** this paper,we give a characterization of the vertex-stabilizer G,when X has valency 8.
Let X be a connected G-locally primitive arc-transitive graph for some sub-group G of Aut(X).Suppose that X is a(G,s)-transitive *** this paper,we give a characterization of the vertex-stabilizer G,when X has valency 8.
The ever-growing and increasing diversity of IoT ecosystems, the seamless interoperability, scale, and near real-time automation have remained grand challenges. This paper presents a solution enterprise semantic and o...
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Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely ...
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Software Defined Networks (SDN) face many security challenges today. A great deal of research has been done within the field of Intrusion Detection Systems (IDS) in these networks. Yet, numerous approaches still rely on deep learning algorithms, but these algorithms suffer from complexity in implementation, the need for high processing power, and high memory consumption. In addition to security issues, firstly, the number of datasets that are based on SDN protocols are very small. Secondly, the ones that are available encompass a variety of attacks in the network and do not focus on a single attack. For this reason, to introduce an SDN-based IDS with a focus on Distributed Denial of Service (DDoS) attacks, it is necessary to generate a DDoS-oriented dataset whose features can train a high-quality IDS. In this work, in order to address two important challenges in SDNs, in the first step, we generate three DDoS attack datasets based on three common and different network topologies. Then, in the second step, using the Convolutional Tsetlin Machine (CTM) algorithm, we introduce a lightweight IDS for DDoS attack dubbed "CTMBIDS," with which we implement an anomaly-based IDS. The lightweight nature of the CTMBIDS stems from its low memory consumption and also its interpretability compared to the existing complex deep learning models. The low usage of system resources for the CTMBIDS makes it an ideal choice for an optimal software that consumes the SDN controller’s least amount of memory. Also, in order to ascertain the quality of the generated datasets, we compare the empirical results of our work with the DDoS attacks of the KDDCup99 benchmark dataset as well. Since the main focus of this work is on a lightweight IDS, the results of this work show that the CTMBIDS performs much more efficiently than traditional and deep learning based machine learning algorithms. Furthermore, the results also show that in most datasets, the proposed method has relatively equal or better
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