Journal of Computing and Security
Volume:10 Issue: 2, Summer and Autumn 2023

In recent years, Online Social Network (OSN) has been rapidly evolving and attracted many users. In OSN, users share sensitive information; therefore, effective access control models are needed to protect information from unauthorized users. Currently, Relational Based Access Control (ReBAC) is used to protect user’s private information. The authorization policy in ReBAC is based on the relationship type and depth among users; however, it is not sufficient to protect private information such as location, time, and age. In this paper, attributes are added to the social graph to establish an efficient access control in OSN, then a policy model is proposed for the new Attribute Relation Based Access Control model (A-ReBAC), and unambiguous Hybrid Logic (HL) policy language is used to formulate the access control policy model. To evaluate the proposed policy model two path-checking algorithms (depth-first search (DFS) and breadth-first search (BFS)) are applied to real datasets, and the time spent on access requests is calculated in the social graph of these datasets. The results showed DFS takes less time than BFS to do the task defined.

Textual analysis in the realm of business depends on text-processing techniques borrowed mainly from information retrieval. Yet, these text-processing techniques are not viable in text-based financial forecasting. In this paper, we suggest developing financial home-grown techniques for processing textual data, specifically in the course of scoring words where standard techniques are not appropriate in financial analysis. On that matter, we pursue two issues. First, we examine major information retrieval heuristics, where we find TF-IDF too facile not only in predicting trends but also in generating accurate results (in terms of errors) on large numbers in text-based financial analysis. Second, we work on a new heuristic satisfying financial concerns. We consider the relationship between the publication rate of information and its importance. The proposed heuristic provides results of unmatchable performance in both predicting trends and precision measures. In an additional analysis, we optimize our scheme using a genetic algorithm as an optimization technique and get greater precision. In comparison with TF-IDF, our proposed heuristic conduces to a 38.5 percent lower error in closeness measures which is again reduced by 16.46 percent with the help of a genetic algorithm. Our findings suggest that researchers in the field of financial textual analysis should not rely on standard information retrieval heuristics.

In this paper, a new audio watermarking scheme is proposed that addresses the synchronization problem using an adaptive filter. To spread the watermark energy across the spectrum of the host audio signal, the scheme uses Hamming coding, convolutional encoding, and generalized PN sequence generation and for watermark recovery, the scheme utilizes Viterbi decoding. Also, the watermark cannot be detected either statistically or perceptually. To increase the watermark embedding capacity, the watermark is embedded in certain areas of the host signal, which increases the masking threshold. By using a high masking threshold, the adaptive watermark insertion, blind detection, robustness, and capacity of watermarking are enhanced in comparison to similar methods. Finally, experimental results show that in addition to guaranteeing an error bit rate (BER) of almost 0 for non- and minor-attack conditions, the BER is substantially improved in the case of low-pass filtering at a cutoff frequency as low as 3.2 KHz.

Uplift Modeling aims to detect subgroups in a population with a specific response or reaction to an action taken on the targeted group. In these models, the Treatment set contains objects that have been exposed to some action, such as a marketing campaign or clinical treatment, while in the Control set, they have not. In this study, a novel artificial immune system-based model was designed using an AIRS classifier to solve uplift modeling problems with improved efficiency. In this approach, a predictive model was built for estimating the conditional probability of receiving the desired response from the subpopulation that has taken the action over the relevant probability of the sub-population that has not taken the action. The proposed model was tested on the Hillstorm-visit-w dataset. Experimental results showed a 138 percent improvement in the area under the uplift curve which is a measure to assess an uplift model's performance.

With the rise of cloud infrastructures, micro-services, frameworks, and reference architectures for every conceivable domain and quality attribute, someone might think that architectural knowledge is hardly needed anymore. But all the architect of today needs to select from the rich array of tools and infrastructure alternatives out there, instantiate, configure them, and create an architecture. Software architecture tools mean any software that helps automation and create architecture, according to requirements. The purpose of these tools is to reduce human effort, speed up software development, and increase reliability. This paper aims to perform a literature review of software architecture tools and to propose architectures for the requirements of the Organization of Small Industries and Industrial Towns of Iran (OSIITI). We surveyed more than 50 software architecture tools for use in practical situations and large-scale projects such as OSIITI’s needs. The results of this survey identified five classes, namely (a) Modeling Tools to model architectures; (b) Code-Based Tools (Diagrams-As-Code) to perform syntactic and semantic consistency checking of the models; (c) Automated Tools to generate executable source code automatically that implements the models; (d) Diagramming Tools and (e) Icons-Based Tools to support for trace links between models and requirements or models and tests interfaces. For each class, several software tools are provided with their major features. These classes and tools are very helpful for organizations such as OSIITI that want to develop software, in both small and large-scale projects. A couple of architectures, based on layered and service-oriented patterns are proposed for OSIITI.

Cloud computing is a demand computing model that requires a large number of physical resources and provides services based on the request of each user. A large number of physical servers in data centers have high electrical energy consumption, which causes high operating costs, increases carbon dioxide (CO2) emission. The focus of this paper is on virtual machine consolidation to minimize power consumption, the number of VM migrations and, reducing service level agreement violation. In contrast to the existing works that use CPU utilization for the detection of host overload, a recent study has proposed Multiple Regression Host Overload Detection (MRHOD) and Hybrid Local Regression Host Overload Detection (HLRHOD) algorithms which take multiple factors (CPU, memory, and network bandwidth utilization) into consideration. This paper provides a framework that takes into account multiple factors: CPU, memory, and bandwidth utilization in three terms: host overload detection, VM placement, and service level agreement violation. First, in the host overload detection term, we provide a Separately Local Regression Host Overload Detection (SLRHOD) algorithm that considers CPU, memory, and bandwidth utilization, separately. Second, in terms of VM placement which is an NP-hard problem, the Power Aware Best Fit Decreasing (PABFD) algorithm with consideration of Dot-product (DP) heuristics was proposed. Third CPU and memory take into account the calculation of SLA violation in terms of SLA violation. To evaluate our framework in contrast to existing works, many experiments were performed. For each experiment, we evaluated and compared three objectives, namely energy consumption, service level agreement violation, and the number of VM migrations. Our experiment results show that the Separately Local Regression Host Overload Detection (SLRHOD) algorithm in terms of SLA violations reveals a significant improvement of 80\%. On the other hand, the Separately Local Regression Host Overload Detection (SLRHOD) algorithm saves energy, up to 3\%, compared to the HLRHOD and MRHOD algorithms. Our simulation results show that our proposed algorithm outperforms the existing algorithm and achieves improvements in energy consumption, service level agreement violation, and the number of VM migrations.