A Structured Approach to Insider Threat Monitoring for Offensive Security Teams

In many countries, government agencies resort to third parties to acquire security services of many kinds, including Red Team operations to test the effectiveness of own defenses mechanisms. Absolute trust is a key requirement, lest a potentially devastating finding be exploited by a treacherous Red Team against the same entity which commissioned the operation, or sold to its adversaries. In our endeavour as a joint private-academic initiative to address this peculiar market, we observed that a structured approach to this issue is much less common than we would have expected. In this work, we outline the process we are devising to offer customers a verified environment, but integrating it with an evidence-based proof of their correct behavior during the operation, striving to solve the “Quis custodiet ipsos custodes” struggle in an offensive setting

Towards the Creation of Interdisciplinary Consumer-Oriented Security Metrics

Information systems are evolving: IoT devices and Cyber-physical systems (CPS) impact on the security of assets and people in the real world. Old cybersecurity approaches, which focused on seeing humans 'as a problem', could be substitute by new paradigms of seeing humans 'as a solution'. Therefore, consumers awareness will be one of the building blocks, as well as initiative that aim to create a set of standardized security metrics that can evaluate the security of systems. In order to do that, researchers need to study which are the essential factors that our future metrics should focus on. In this paper we analyzed this problem over CPS while assuming the consumer perspective. We summarize the state of the art in security metrics and advocate the need for a research effort aimed at taking the field to a new level of formal soundness and practical usability by considering interdisciplinary implications on cybersecurity

Complete Chloroplast Genome Sequence of Omani Lime (Citrus aurantiifolia) and Comparative Analysis within the Rosids

The genus Citrus contains many economically important fruits that are grown worldwide for their high nutritional and medicinal value. Due to frequent hybridizations among species and cultivars, the exact number of natural species and the taxonomic relationships within this genus are unclear. To compare the differences between the Citrus chloroplast genomes and to develop useful genetic markers, we used a reference-assisted approach to assemble the complete chloroplast genome of Omani lime (C. aurantiifolia). The complete C. aurantiifolia chloroplast genome is 159,893 bp in length; the organization and gene content are similar to most of the rosids lineages characterized to date. Through comparison with the sweet orange (C. sinensis) chloroplast genome, we identified three intergenic regions and 94 simple sequence repeats (SSRs) that are potentially informative markers with resolution for interspecific relationships. These markers can be utilized to better understand the origin of cultivated Citrus. A comparison among 72 species belonging to 10 families of representative rosids lineages also provides new insights into their chloroplast genome evolution

Poster: Continual Network Learning

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions

Biotic and Abiotic Stresses of Major Fruit Crops in Oman: A Review

Oman is located in an arid region of the world that is characterized by adverse climatic conditions, including heat and drought. In recent years, it has also been affected by climate turbulence and the occurrence of severe weather, such as cyclones and heat/cold waves affecting large agricultural areas of the country. Fruit cultivation area represents 31% of the total cultivated area (97,239.58 ha) in the country. However, the production share is only 17% of the total crop production in the country (2.6 million tons). About 90% of the fruit cultivation area is dominated by date palm, banana, lime, and mango. In addition to the abiotic stresses, such as drought, heat, and salinity, major fruit crops have declined in recent years due to various biotic stressors, primarily insect pests, and diseases. For several decades, the date palm has suffered from the Dubas bug and in recent years from Red Palm Weevil. Lime has been infected with Witch’s Broom Disease of Lime (WBDL) caused by ‘Candidatus Phytoplasma aurantifolia’ that has led to the decline of production to 25% from its peak in the nineties. Banana is Oman`s second-largest fruit crop in production and export. It has also been the subject of studies due to losses incurred by farmers during pre-and post-harvest stages, in addition to several pests and diseases that affect bananas in Oman. Mango is another major fruit crop that is primarily cultivated in northern Oman. Severe infection with mango decline has led to the eradication of mango orchards from many regions of Oman, particularly in Batinah Coast, where increased salinity has led to a decline in mango yield. Research conducted in Oman has investigated several aspects of these challenges. This review paper summarizes the outcome from studies conducted in the country and proposes directions towards resolving current and future challenges to the fruit industry

Probabilistic seismic hazard maps for the sultanate of Oman

This study presents the results of the first probabilistic seismic hazard assessment (PSHA) in the framework of logic tree for Oman. The earthquake catalogue was homogenized, declustered, and used to define seismotectonic source model that characterizes the seismicity of Oman. Two seismic source models were used in the current study; the first consists of 26 seismic source zones, while the second is expressing the alternative view that seismicity is uniform along the entire Makran and Zagros zones. The recurrence parameters for all the seismogenic zones were determined using the doubly bounded exponential distribution except the zones of Makran, which were modelled using the characteristic distribution. Maximum earthquakes were determined and the horizontal ground accelerations in terms of geometric mean were calculated using ground-motion prediction relationships developed based upon seismic data obtained from active tectonic environments similar to those surrounding Oman. The alternative seismotectonic source models, maximum magnitude, and ground-motion prediction relationships were weighted and used to account for the epistemic uncertainty. Hazard maps at rock sites were produced for 5 % damped spectral acceleration (SA) values at 0.1, 0.2, 0.3, 1.0 and 2.0 s spectral periods as well as peak ground acceleration (PGA) for return periods of 475 and 2,475 years. The highest hazard is found in Khasab City with maximum SA at 0.2 s spectral period reaching 243 and 397 cm/s[superscript 2] for return periods 475 and 2,475 years, respectively. The sensitivity analysis reveals that the choice of seismic source model and the ground-motion prediction equation influences the results most.Oman Ministerial Cabinet (project number 22409017

Quantifying the need for supervised machine learning in conducting live forensic analysis of emergent configurations (ECO) in IoT environments

© 2020 The Author(s) Machine learning has been shown as a promising approach to mine larger datasets, such as those that comprise data from a broad range of Internet of Things devices, across complex environment(s) to solve different problems. This paper surveys existing literature on the potential of using supervised classical machine learning techniques, such as K-Nearest Neigbour, Support Vector Machines, Naive Bayes and Random Forest algorithms, in performing live digital forensics for different IoT configurations. There are also a number of challenges associated with the use of machine learning techniques, as discussed in this paper

A critical look at studies applying over-sampling on the TPEHGDB dataset

Preterm birth is the leading cause of death among young children and has a large prevalence globally. Machine learning models, based on features extracted from clinical sources such as electronic patient files, yield promising results. In this study, we review similar studies that constructed predictive models based on a publicly available dataset, called the Term-Preterm EHG Database (TPEHGDB), which contains electrohysterogram signals on top of clinical data. These studies often report near-perfect prediction results, by applying over-sampling as a means of data augmentation. We reconstruct these results to show that they can only be achieved when data augmentation is applied on the entire dataset prior to partitioning into training and testing set. This results in (i) samples that are highly correlated to data points from the test set are introduced and added to the training set, and (ii) artificial samples that are highly correlated to points from the training set being added to the test set. Many previously reported results therefore carry little meaning in terms of the actual effectiveness of the model in making predictions on unseen data in a real-world setting. After focusing on the danger of applying over-sampling strategies before data partitioning, we present a realistic baseline for the TPEHGDB dataset and show how the predictive performance and clinical use can be improved by incorporating features from electrohysterogram sensors and by applying over-sampling on the training set

Clinical relevance of DNA microarray analyses using archival formalin-fixed paraffin-embedded breast cancer specimens

Abstract Background The ability of gene profiling to predict treatment response and prognosis in breast cancers has been demonstrated in many studies using DNA microarray analyses on RNA from fresh frozen tumor specimens. In certain clinical and research situations, performing such analyses on archival formalin fixed paraffin-embedded (FFPE) surgical specimens would be advantageous as large libraries of such specimens with long-term follow-up data are widely available. However, FFPE tissue processing can cause fragmentation and chemical modifications of the RNA. A number of recent technical advances have been reported to overcome these issues. Our current study evaluates whether or not the technology is ready for clinical applications. Methods A modified RNA extraction method and a recent DNA microarray technique, cDNA-mediated annealing, selection, extension and ligation (DASL, Illumina Inc) were evaluated. The gene profiles generated from FFPE specimens were compared to those obtained from paired fresh fine needle aspiration biopsies (FNAB) of 25 breast cancers of different clinical subtypes (based on ER and Her2/neu status). Selected RNA levels were validated using RT-qPCR, and two public databases were used to demonstrate the prognostic significance of the gene profiles generated from FFPE specimens. Results Compared to FNAB, RNA isolated from FFPE samples was relatively more degraded, nonetheless, over 80% of the RNA samples were deemed suitable for subsequent DASL assay. Despite a higher noise level, a set of genes from FFPE specimens correlated very well with the gene profiles obtained from FNAB, and could differentiate breast cancer subtypes. Expression levels of these genes were validated using RT-qPCR. Finally, for the first time we correlated gene expression profiles from FFPE samples to survival using two independent microarray databases. Specifically, over-expression of ANLN and KIF2C, and under-expression of MAPT strongly correlated with poor outcomes in breast cancer patients. Conclusion We demonstrated that FFPE specimens retained important prognostic information that could be identified using a recent gene profiling technology. Our study supports the use of FFPE specimens for the development and refinement of prognostic gene signatures for breast cancer. Clinical applications of such prognostic gene profiles await future large-scale validation studies

Encouraging female entrepreneurship in Jordan: environmental factors, obstacles and challenges

The number of female entrepreneurs and their contribution to the economy is steadily rising. Yet research suggests that female entrepreneurs face more challenges and barriers than their male counterparts. This is expected to be even more prevalent in Islamic contexts, which are characterised by conservative and patriarchal societies. In this research, 254 female business students from a private and a public university responded to a questionnaire that gauges their perceptions about potential barriers to entrepreneurship in Jordan and whether the business education they are receiving helps to prepare them for future entrepreneurial activity. Our results help to form a basis on which a deeper understanding of the phenomena can be achieved through more in depth future research. Among the main environmental factors that worry potential female entrepreneurs are the weakness of Jordanian economy, lack of finance, fear of risk, gender inequality and inability to maintain a work and private life balance. Our results also show that students are really not aware of the opportunities available to them and are unable to make a proper assessment. We call on both universities and the Jordanian government to put more emphasis on practical entrepreneurial education and encouraging women to play a much more active role within the workforce