Compromised user credentials detection using temporal features: A prudent based approach

© 2017 ACM. This study exposes a serious and rapidly growing cyber threat of compromised legitimate user credentials which is very effective for cyber-criminals to gain trusted relationships with the account owners. Such a compromised user\u27s credentials ultimately result in damage incurred by the attacker at large-scale. Moreover, the detection of compromised legitimate user activities is crucial in competitive and sensitive organizations because wrong data is more difficult to clean from the database. The proposed study presents a novel approach to detect compromised users\u27 activity in a live database. Our approach uses a composition of prudence analysis, ripple down rules (RDR) and simulated experts (SE) to detect and identify accounts that experience a sudden change in behavior. We collected data from a sensitive running database for a period of Six months and evaluate the proposed technique. The results show that this combined model can fully detect outlier user\u27s activity and can provide useful information for the concerned decision maker

Response of Tuberose (Polianthes tuberosa) to Potassium and Planting Depth

A research work was carried to find out the effect of planting depths, potassium levels and their interaction during the year 2012. The corms of tuberose were planted at a depth of 5, 10, and 15 cm and were fertilized with four levels of potassium 0, 50, 100 and 150 Kg of K2O per hectare using K2SO4 as a source of K2O. Result of the study revealed that planting depth of 15 cm significantly increased length of spike (56.9 cm), number of florets spike-1 (54.84), and plant height (103.13) cm. Planting depth of 5cm cause decreased number of days to last floret opening (180.08). Potassium level of 150 kg of K2O ha-1 length of spike (55.24 cm), number of florets spike-1 (49.2) and plant height (100.29 cm). Planting depth of 15 cm and fertilizer application of 150 kg of K2O ha-1proved to be superior regarding length of spike opening (64.4cm), number of florets spike-1 (62.2) and plant height (106.20 cm). Hence planting tuberose at a depth of 15cm and fertilizer application of 150 kg of K2O ha-1 is recommended for commercial cultivation of tuberose. Keywords: Tuberose; Potassium; Planting Depth; Number of Florets Spike-1; Spike Length

Situational Analysis of Public Sector Schools in Rural Areas of Southern Punjab, Pakistan

Training assumes an imperative part in the improvement of the nation and people. Pakistan is among the most thickly populated countries of the world. Pakistan has a standout amongst the most insignificant capability rates on the planet and as showed by the United Nations Educational, Scientific and Cultural Organization (UNESCO), it is 55 for each penny and stays at 160th in total countries of the world. The target of this examination is to distinguish the reason of low proficiency rate in rustic territories and to recognize physical structure we likewise checkout essential security courses of action in government schools and break down effect of missing offices on youngsters. We likewise discover the reason of dropout youngsters from school and recognize Staff nearness at school. A Situational logical examination was led in region Punjab and in multi arranges testing system was utilized in local Muzaffargarh. Two tehsils from locale were chosen purposively for information accumulation. Tehsil jatoi and Alipur were chosen from locale Muzaffargarh. 150 respondents were selected as a specimen size of the examination. Rate, chi square, gamma, examinations were utilized to investigate the connection between various factors. The outcomes with respect to sexual orientation, age, territory, instruction level, wage level, among various factors are tried by utilizing SPSS for discoveries of the examination. Government should actualize the instructive arrangements from the gross root level. Guys and females ought to incorporate equivalent level for instructive basic leadership process. Conventional esteems, financial obstructions and some other social imperatives ought to be debilitated at all levels in instruction segment particularly in provincial southern Punjab

Estudio experimental de la acumulación de partículas atmosféricas de cristales en la transmisión de luz diurna

Daylight is one of the most significant light source which could illuminate interior spaces by passing through windows and light collectors. Dust and aerosol accumulation on windowpanes reduce the light amount passing through it. The main objective of this research is to determine the impact of airborne particulate matters deposition on past light quantity. In this experiment the most prevalent particulate matters such as dust, carbon, and a mixture of both examined with 3 mm common commercial glasses at single and double glaze windowpanes and several interesting observations have been obtained. The result of this experiment will help building owners to adjust a window-cleaning schedule to reduce their lighting electricity consumption and expenses.La luz del día es una de las fuentes de luz más importantes que podría iluminar los espacios interiores al pasar a través de ventanas y colectores de luz. La acumulación de polvo y aerosoles en los cristales de las ventanas reduce la cantidad de luz que pasa a través de ellos. El objetivo principal de esta investigación es determinar el impacto de la deposición de partículas en el aire sobre la cantidad de luz pasada. En este experimento, se obtuvieron las partículas más prevalentes, como el polvo, el carbono y una mezcla de ambos examinados con vidrios comerciales comunes de 3 mm en cristales de vidrios simples y dobles y se obtuvieron varias observaciones interesantes. El resultado de este experimento ayudará a los propietarios de edificios a ajustar un programa de limpieza de ventanas para reducir el consumo y los gastos de electricidad de iluminación

Estudio experimental de la acumulación de partículas atmosféricas de cristales en la transmisión de luz diurna

La luz del día es una de las fuentes de luz más importantes que podría iluminar los espacios interiores al pasar a través de ventanas y colectores de luz. La acumulación de polvo y aerosoles en los cristales de las ventanas reduce la cantidad de luz que pasa a través de ellos. El objetivo principal de esta investigación es determinar el impacto de la deposición de partículas en el aire sobre la cantidad de luz pasada. En este experimento, se obtuvieron las partículas más prevalentes, como el polvo, el carbono y una mezcla de ambos examinados con vidrios comerciales comunes de 3 mm en cristales de vidrios simples y dobles y se obtuvieron varias observaciones interesantes. El resultado de este experimento ayudará a los propietarios de edificios a ajustar un programa de limpieza de ventanas para reducir el consumo y los gastos de electricidad de iluminació

A prudent based approach for compromised user credentials detection

© Springer Science+Business Media New York 2018. Compromised user credential (CUC) is an activity in which someone, such as a thief, cyber-criminal or attacker gains access to your login credentials for the purpose of theft, fraud, or business disruption. It has become an alarming issue for various organizations. It is not only crucial for information technology (IT) oriented institutions using database management systems (DBMSs) but is also critical for competitive and sensitive organization where faulty data is more difficult to clean up. Various well-known risk mitigation techniques have been developed, such as authentication, authorization, and fraud detection. However, none of these methods are capable of efficiently detecting compromised legitimate users’ credentials. This is because cyber-criminals can gain access to legitimate users’ accounts based on trusted relationships with the account owner. This study focuses on handling CUC on time to avoid larger-scale damage incurred by the cyber-criminals. The proposed approach can efficiently detect CUC in a live database by analyzing and comparing the user’s current and past operational behavior. This novel approach is built by a combination of prudent analysis, ripple down rules and simulated experts. The experiments are carried out on collected data over 6 months from sensitive live DBMS. The results explore the performance of the proposed approach that it can efficiently detect CUC with 97% overall accuracy and 2.013% overall error rate. Moreover, it also provides useful information about compromised users’ activities for decision or policy makers as to which user is more critical and requires more consideration as compared to less crucial user based prevalence value

Configurable data acquisition for cloud-centric IoT

© 2018 ACM. In the era of smart devices, the reliance of intelligence is the abundance of data. This data is heterogeneous in nature and generated by many small-scale sensory devices. With their taskspecific nature, these sensory devices are energy efficient resources with limited computing and storage power. The effective utilization of these devices requires collaborative execution with remote storage and computing power. Internet of Things defines one such cluster formation of these devices which are single-task specific in themselves but achieves higher goals when executed in a collective environment. Data acquisition from these devices is accumulated at scalable resources like cloud where intelligent processes constitute the foundation of smart environments. However, the current implementations of data acquisition for cloud-centric IoT are driven by the APIs of the device manufacturers, resulting in platforms which are less dynamic and configurable for data acquisition. In this paper, we propose our cloud-centric methodology with the focus on configurable data acquisition from IoT. Our methodology decouples communication of data from the strategical usage of the sensory device. Thus, making it more dynamic and applicable for evolutionary smart environments

Morphological Classification of Radio Galaxies using Semi-Supervised Group Equivariant CNNs

Out of the estimated few trillion galaxies, only around a million have been detected through radio frequencies, and only a tiny fraction, approximately a thousand, have been manually classified. We have addressed this disparity between labeled and unlabeled images of radio galaxies by employing a semi-supervised learning approach to classify them into the known Fanaroff-Riley Type I (FRI) and Type II (FRII) categories. A Group Equivariant Convolutional Neural Network (G-CNN) was used as an encoder of the state-of-the-art self-supervised methods SimCLR (A Simple Framework for Contrastive Learning of Visual Representations) and BYOL (Bootstrap Your Own Latent). The G-CNN preserves the equivariance for the Euclidean Group E(2), enabling it to effectively learn the representation of globally oriented feature maps. After representation learning, we trained a fully-connected classifier and fine-tuned the trained encoder with labeled data. Our findings demonstrate that our semi-supervised approach outperforms existing state-of-the-art methods across several metrics, including cluster quality, convergence rate, accuracy, precision, recall, and the F1-score. Moreover, statistical significance testing via a t-test revealed that our method surpasses the performance of a fully supervised G-CNN. This study emphasizes the importance of semi-supervised learning in radio galaxy classification, where labeled data are still scarce, but the prospects for discovery are immense.Comment: 9 pages, 6 figures, accepted in INNS Deep Learning Innovations and Applications (INNS DLIA 2023) workshop, IJCNN 2023, to be published in Procedia Computer Scienc

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

© 2018 Elsevier Ltd Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean)