Statistical analysis driven optimized deep learning system for intrusion detection

Attackers have developed ever more sophisticated and intelligent ways to hack information and communication technology systems. The extent of damage an individual hacker can carry out upon infiltrating a system is well understood. A potentially catastrophic scenario can be envisaged where a nation-state intercepting encrypted financial data gets hacked. Thus, intelligent cybersecurity systems have become inevitably important for improved protection against malicious threats. However, as malware attacks continue to dramatically increase in volume and complexity, it has become ever more challenging for traditional analytic tools to detect and mitigate threat. Furthermore, a huge amount of data produced by large networks has made the recognition task even more complicated and challenging. In this work, we propose an innovative statistical analysis driven optimized deep learning system for intrusion detection. The proposed intrusion detection system (IDS) extracts optimized and more correlated features using big data visualization and statistical analysis methods (human-in-the-loop), followed by a deep autoencoder for potential threat detection. Specifically, a pre-processing module eliminates the outliers and converts categorical variables into one-hot-encoded vectors. The feature extraction module discard features with null values and selects the most significant features as input to the deep autoencoder model (trained in a greedy-wise manner). The NSL-KDD dataset from the Canadian Institute for Cybersecurity is used as a benchmark to evaluate the feasibility and effectiveness of the proposed architecture. Simulation results demonstrate the potential of our proposed system and its outperformance as compared to existing state-of-the-art methods and recently published novel approaches. Ongoing work includes further optimization and real-time evaluation of our proposed IDS.Comment: To appear in the 9th International Conference on Brain Inspired Cognitive Systems (BICS 2018

NLSP Gluino Search at the Tevatron and early LHC

We investigate the collider phenomenology of gluino-bino co-annihilation scenario both at the Tevatron and 7 TeV LHC. This scenario can be realized, for example, in a class of realistic supersymmetric models with non-universal gaugino masses and t-b-\tau Yukawa unification. The NLSP gluino and LSP bino should be nearly degenerate in mass, so that the typical gluino search channels involving leptons or hard jets are not available. Consequently, the gluino can be lighter than various bounds on its mass from direct searches. We propose a new search for NLSP gluino involving multi-b final states, arising from the three-body decay \tilde{g}-> b\bar{b}\tilde{\chi}_1^0. We identify two realistic models with gluino mass of around 300 GeV for which the three-body decay is dominant, and show that a 4.5 \sigma observation sensitivity can be achieved at the Tevatron with an integrated luminosity of 10 fb^{-1}. For the 7 TeV LHC with 50 pb^{-1} of integrated luminosity, the number of signal events for the two models is O(10), to be compared with negligible SM background event.Comment: 14 pages, 4 figures and 3 tables, minor modifications made and accepted for publication in JHE

Gendered immobility: influence of social roles and local context on mobility decisions in Pakistan

This paper examines the instances of one-day immobility in Pakistan and reports its socio-demographic determinants using the nationally representative dataset of the 2007 Pakistan Time Use Survey. Of 37,830 time diary respondents, nearly 30% did not report travel during the diary day. Homemakers and those out of the workforce were more likely to be immobile than employed or student respondents. Immobility rates were very high among women (55%) as compared to men (4%). Among women, those between 20 and 34 years of age, married, with children, having better education, dependent on other household members and those living in higher income households were more likely to be immobile. The excessive gender nature of immobility seems to be triggered by a gender-based sociocultural environment, which restricts female mobility due to family honor concerns. Other than this, those living in the provinces of Sindh and Khyber Pakhtunkhwa or in urban areas were more likely to be immobile than those living in Punjab and Sindh provinces or in rural areas. The significant geographic effect at broader spatial scale is caused by the demographic structure as well as due to differences in the social and cultural context of these areas. Finally, questions regarding the measurement of immobility and the potential implications of increased female immobility are discussed

Enhancing Managed Lanes Equity Analysis

(c)1036342 (wo) 15Planning and environmental studies involving managed lanes still have difficulty determining how to effectively evaluate project alternatives from an equity perspective. To most people, \u201cequity\u201d is ubiquitous with income, but this is a narrow focus that limits the scope of what can be considered equity, and indeed this can be true when it comes to managed lanes. As the Minnesota Department of Transportation analyzes the expansion of E-ZPass corridors, it is imperative it evaluates project alternatives from an equity perspective. The results of this study suggest that E-ZPass lane users are more racially diverse than users in the travelsheds. In two out of the four E-ZPass lane corridors, a higher proportion of E-ZPass lane users have household incomes below $100,000 compared to the travelsheds. Overall, there is a lower percentage of people with disabilities among E-ZPass lane users than those in the travelsheds. These results are driven by the makeup of E-ZPass lane users. In addition, this research project demonstrates the feasibility of incorporating quantitative and qualitative equity measures into the alternatives analysis process. The demonstration shows that the quantitative measures are all feasible with existing tools, provide meaningful information to the alternatives analysis process, and can be put into practice immediately

Aberrant Mitochondrial Homeostasis in the Skeletal Muscle of Sedentary Older Adults

The role of mitochondrial dysfunction and oxidative stress has been extensively characterized in the aetiology of sarcopenia (aging-associated loss of muscle mass) and muscle wasting as a result of muscle disuse. What remains less clear is whether the decline in skeletal muscle mitochondrial oxidative capacity is purely a function of the aging process or if the sedentary lifestyle of older adult subjects has confounded previous reports. The objective of the present study was to investigate if a recreationally active lifestyle in older adults can conserve skeletal muscle strength and functionality, chronic systemic inflammation, mitochondrial biogenesis and oxidative capacity, and cellular antioxidant capacity. To that end, muscle biopsies were taken from the vastus lateralis of young and age-matched recreationally active older and sedentary older men and women (N = 10/group; ♀  =  ♂). We show that a physically active lifestyle is associated with the partial compensatory preservation of mitochondrial biogenesis, and cellular oxidative and antioxidant capacity in skeletal muscle of older adults. Conversely a sedentary lifestyle, associated with osteoarthritis-mediated physical inactivity, is associated with reduced mitochondrial function, dysregulation of cellular redox status and chronic systemic inflammation that renders the skeletal muscle intracellular environment prone to reactive oxygen species-mediated toxicity. We propose that an active lifestyle is an important determinant of quality of life and molecular progression of aging in skeletal muscle of the elderly, and is a viable therapy for attenuating and/or reversing skeletal muscle strength declines and mitochondrial abnormalities associated with aging

Linking Symptom Inventories using Semantic Textual Similarity

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes