Multiagent and Grid Systems - Volume 18, issue 1

Authors: Douha, Djamel | Mokhtari, Aicha | Guessoum, Zahia | Berghout, Yasser Moussa

Article Type: Research Article

Abstract: An agent is an autonomous entity that can perform actions to achieve its goals. It acts in a dynamic environment that may engender failures regarding its behavior. Therefore, a formal testing/verification approach of the agent is required to ensure the correctness of its behavior. In this paper, we propose a Default Logic formalism to abstract an agent behavior as knowledge and reasoning rules, and to verify and test the consistency of the behavior. The considered agents are implemented with JADE framework. Also, agent abstraction is translated into Answer Set Programming and solved by Clingo to generate dynamic and …adaptive test cases of the agent behavior. The dynamic test cases allow predicting the agent behavior when a new information arises in the system. Show more

Keywords: Agent behavior, multi-agent systems, testing, verification, non-monotonic reasoning, default logic

DOI: 10.3233/MGS-220359

Citation: Multiagent and Grid Systems, vol. 18, no. 1, pp. 1-20, 2022

Authors: V., Murali Mohan | RM, Balajee | K Mewada, Hiren | BR, Rajakumar | D, Binu

Article Type: Research Article

Abstract: Cloud computing provides various cost-effective on-demand services to the user and so it is rising like a real trend in the IT service model. However, due to its open and distributed architecture, it is highly vulnerable to attackers. The security and privacy of cloud users has become a major hurdle. The most prevalent approach for detecting attacks on the cloud is the Intrusion Detection System (IDS). Scalability and autonomous self-adaptation weren’t features of contemporary IDS deployed in traditional Internet or Intranet contexts. Furthermore, they lack determinism, making them inappropriate for cloud-based settings. This necessitates the development of new cloud-based IDS …capable of fulfilling the firm’s security standards. Therefore, in this research work, we have introduced a new IDS model for the cloud environment. Our research work is made up of four major phases: “data pre-processing, optimal clustering, feature selection, and attack detection phase”. Initially, the collected raw data are pre-processed to enhance the quality of the data. Then, these pre-processed data are segmented with the newly introduced K-means clustering model, where we’ve optimally selected the centroids by introducing a new hybrid optimization model referred as Spider Monkey Updated with Sealion Optimization (SMSLO), which is the conceptual hybridization of standard SeaLion Optimization (SLnO) and Spider Monkey Optimization (SMO), respectively. At the end of segmentation, two clusters (attack data and non-attack data) will be formed. The data available in both clusters seems to be huge in dimensions, so we’ve lessened the dimensions of the data in the clusters by applying the “Principal Component Analysis (PCA)” algorithm. Subsequently, these dimensionality-reduced features pass into the attack detection phase. The attack detection phase is modeled with the optimized Deep Belief Network (DBN), which portrays the type of attack (Dos, Botnet, DDoS as well) that intruded into the network. Since the DBN makes the final detections; it is ought to be less prone to errors. We have lessened the detection errors such as the Mean Square Error (MSE) of DBN by fine-tuning its weight using a new hybrid optimization model (SMSLO). Finally, the result acquired from the proposed work (DBN+ SMSLO) is validated. Show more

Keywords: Cloud computing, attack type detection, improved K-means clustering, Deep Belief Network, SMSLO model

DOI: 10.3233/MGS-220360

Citation: Multiagent and Grid Systems, vol. 18, no. 1, pp. 21-43, 2022

Authors: Verma, Garima | Kanrar, Soumen

Article Type: Research Article

Abstract: Nowadays cloud computing has given a new paradigm of computing. Despite several benefits of cloud computing there is still a big challenge of ensuring confidentiality and integrity for sensitive information on the cloud. Therefore to address these challenges without loss of any sensitive information and privacy, we present a novel and robust model called ‘Enhanced Cloud Security using Hyper Elliptic Curve and Biometric’ (ECSHB). The model ECSHB ensures the preservation of data security, privacy, and authentication of data in a cloud environment. The proposed approach combines biometric and hyperelliptic curve cryptography (HECC) techniques to elevate the security of data accessing …and resource preservations in the cloud. ECSHB provides a high level of security using less processing power, which will automatically reduce the overall cost. The efficacy of the ECSHB has been evaluated in the form of recognition rate, biometric similarity score, False Matching Ratio (FMR), and False NonMatching Ratio (FNMR). ECSHB has been validated using security threat model analysis in terms of confidentiality. The measure of collision attack, replay attack and non-repudiation is also considered in this work. The evidence of results is compared with some existing work, and the results obtained exhibit better performance in terms of data security and privacy in the cloud environment. Show more

Keywords: Cloud computing, security, biometrics, hyperelliptic curve cryptography, replay attack

DOI: 10.3233/MGS-220361

Citation: Multiagent and Grid Systems, vol. 18, no. 1, pp. 45-63, 2022

Authors: Jawade, Prashant Balkrishna | Ramachandram, S.

Article Type: Research Article

Abstract: The appliances that are received at a cloud data centre are a compilation of jobs (task) that might be independent or dependent on one another. These tasks are then allocated to diverse virtual machine (VM) in a scheduled way. For this task allocation, various scheduling policies are deployed with the intention of reducing energy utilization and makespan, and increasing cloud resource exploitation as well. A variety of research and studies were done to attain an optimal solution in a single cloud setting, however the similar schemes might not operate on multi-cloud environments. Here, this paper aims to introduce a secured …task scheduling model in multi-cloud environment. The developed approach mainly concerns on optimal allocation of tasks via a hybrid optimization theory. Consequently, the developed optimal task allotment considers the objectives like makespan, execution time, security parameters (risk evaluation), utilization cost, maximal service level agreement (SLA) adherence and power usage effectiveness (PUE). For resolving this issue, a novel hybrid algorithm termed as rock hyraxes updated shark smell with logistic mapping (RHU-SLM) is introduced in this work. At last, the superiority of developed approach is proved on varied measures. Show more

Keywords: Task allocation, multi-cloud, makespan, power usage, RHU-SLM model

DOI: 10.3233/MGS-220362

Citation: Multiagent and Grid Systems, vol. 18, no. 1, pp. 65-85, 2022