Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2023
Volume
Vol. 24
Number
No. 1
Description
Journal Volume
Computer Science
Vol. 24 (2023)
Projects
Pages
Articles
Securing centralized SDN control with distributed blockchain technology
(Wydawnictwa AGH, 2023) Ahmad, Suhail; Mir, Ajaz Hussain
Software-Defined Networks (SDN) advocate the segregation of network control logic, forwarding functions and management applications into different planes to achieve network programmability and automated and dynamic flow control in next-generation networks. It promotes the deployment of novel and augmented network-management functions in order to have flexible, robust, scalable, and cost-effective network deployments. All of these features introduce new research challenges and require secure communication protocols among segregated network planes. This manuscript focuses on the security issue of the southbound interface that operates between the SDN control and the data plane. We have highlighted the security threats that are associated with an unprotected southbound interface and those issues that are related to the existing TLS-based security solution. A lightweight blockchain-based decentralized security solution is proposed for the southbound interface to secure the resources of logically centralized SDN controllers and distributed forwarding devices from opponents. The proposed mechanism can operate in multi-domain SDN deployment and can be used with a wide range of network controllers and data plane devices. In addition to this, the proposed security solution has been analyzed in terms of its security features, communication, and re-authentication overhead.
Diacritic-aware Yorùbá spell checker
(Wydawnictwa AGH, 2023) Asahiah, Franklin Oládiípò; Onífádé, Mary Taiwo; Asahiah, Adekemisola Olufunmilayo; Adegunlehin, Abayomi Emmanuel; Amoo, Adekemi Olawunmi
Spell checking and correction is still in its infancy for the Yorùbá language, existing tools cannot be directly applied to address the problem, as Yorùbá uses diacritics extensively for distinguishing phonemes and for marking tone. A model was formulated as a parallel combination of a unigram language model and a diacritic model to form a dictionary sub-model that can be used by error-detection and candidate-generation modules. The candidate-generation module was implemented as a reverse Levensthein edit-distance algorithm. The system was evaluated by using detection accuracy (calculated from the precision and recall) and suggestion accuracy (SA) as metrics. Our experimental setups compared the performance of the component subsystems when used alone and with their combination into a unified model. The detection accuracies for the different models range from 93.23 to 95.01%, and the suggestion accuracies range from 26.94 to 72.10%. The results indicated that each of the sub-models in the dictionary played different roles.
A note on hardness of multiprocessor scheduling with scheduling solution space tree
(Wydawnictwa AGH, 2023) Dwibedy, Debasis; Mohanty, Rakesh
We study the hardness of the non-preemptive scheduling problem of a list of independent jobs on a set of identical parallel processors with a makespan minimization objective. We make a maiden attempt to explore the combinatorial structure of the problem by introducing a scheduling solution space tree (SSST) as a novel data structure. We formally define and characterize the properties of SSST through our analytical results. We show that the multiprocessor scheduling problem is $\cal {NP}$-complete with an alternative technique using SSST and weighted scheduling solution space tree (WSSST) data structures. We propose a non-deterministic polynomial-time algorithm called magic scheduling (MS) based on the reduction framework. We also define a new variant of multiprocessor scheduling by including the user as an additional input parameter, which we called the multiuser multiprocessor scheduling problem (MUMPSP). We also show that MUMPSP is $\cal {NP}$-complete. We conclude the article by exploring several non-trivial research challenges for future research investigations.
Privacy preservation for transaction initiators: stronger key image ring signature and smart contract-based framework
(Wydawnictwa AGH, 2023) Odoom, Justice; Huang, Xiaofang; Danso, Samuel; Nyarko, Benedicta Nana Esi
Recently, blockchain technology has garnered a great deal of suport, however, an attenuating factor to its global adoption in certain use cases is privacypreservation (owing to its inherent transparency). A widely explored cryptographic option to address this challenge has been a ring signature that, aside from its privacy guarantee, must be double-spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attacks in a lightweight ring signature scheme and proceed to construct a new fortified commitment scheme that uses a signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme that is solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secure, and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as a Kovan testnet along with a performance analysis that attests to its efficiency and usability – and, we make the code publicly available on GitHub.
Explainable deep neural network-based analysis on intrusion-detection systems
(Wydawnictwa AGH, 2023) Pande, Sagar Dhanraj; Khamparia, Aditya
The research on intrusion-detection systems (IDSs) has been increasing in recent years. Particularly, this research widely utilizes machine-learning concepts, and it has proven that these concepts are effective with IDSs - particularly, deep neural network-based models have enhanced the rates of the detection of IDSs. In the same instance, these models are turning out to be very complex, and users are unable to track down explanations for the decisions that are made, this indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, this article deals with a proposed model that can explain the obtained predictions. The proposed framework is a combination of a conventional IDS with the aid of a deep neural network and the interpretability of the model predictions. The proposed model utilizes Shapley additive explanations (SHAPs) that mixes the local explainability as well as the global explainability for the enhancement of interpretations in the case of IDS. The proposed model was implemented by using popular data sets (NSL-KDD and UNSW-NB15), and the performance of the framework was evaluated by using their accuracy. The framework achieved accuracy levels of 99.99 and 99.96%, respectively. The proposed framework can identify the top-4 features using local explainability and the top-20 features using global explainability.