Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports

Open-Source Projects and Libraries are being used in software development while also bearing multiple security vulnerabilities. This use of third party ecosystem creates a new kind of attack surface for a product in development. An intelligent attacker can attack a product by exploiting one of the vulnerabilities present in linked projects and libraries. In this paper, we mine threat intelligence about open source projects and libraries from bugs and issues reported on public code repositories. We also track library and project dependencies for installed software on a client machine. We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph. Security analysts and developers can then query and receive alerts from the knowledge graph if any threat intelligence is found about linked libraries and projects, utilized in their products

SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.Comment: 12 page

Intermittent Theta Burst Stimulation: Application to Spinal Cord Injury Rehabilitation and Computational Modeling

Loss of motor function from spinal cord injuries (SCI) results in loss of independence. Rehabilitation efforts are targeted to enhance the ability to perform activities of daily living (ADLs), but outcomes from physical therapy alone are often insufficient. Neuromodulation techniques that induce neuroplasticity may push the limits on recovery. Neuromodulation by intermittent theta burst transcranial magnetic stimulation (iTBS) induces neuroplasticity by increasing corticomotor excitability, though this has most frequently been studied with motor targets and on individuals not in need of rehabilitation. Increased corticomotor excitability is associated with motor learning. The response to iTBS, however, is highly variable and unpredictable, while the mechanisms are not well understood. Studies have proposed brain anatomy and individual subject differences as a source of variability but have not quantified the effects. Existing models have not incorporated known neurotransmitter changes at the synaptic level to pair mechanisms to cell output in a neural circuit. To use iTBS in practical rehabilitative efforts, the technique must either be consistent, have a predictable responsiveness, or present with enough mechanistic understanding to improve its efficacy. To that effect, this study has two primary objectives for the improvement of rehabilitation techniques. The first is to establish how iTBS affects both a motor target and population that typically undergoes physical rehabilitation often with unsatisfactory outcomes, in this case the biceps brachii in individuals with SCI and relate the empirical effects of iTBS to individual anatomy. This will establish the consistency of the technique and predictability of its effects, relevant to rehabilitative efforts. The secondary objective is to create the foundation of a model that exhibits circuit organization, which would start the development of a motor neuroplasticity functional unit with simulation of the synaptic long-term potentiation (LTP) like effects of iTBS. Summary of Methods: iTBS was performed targeting the biceps, on multiple cohorts, with changes in motor evoked potential amplitude (MEP) tracked after sham and active intervention. This was compared between nonimpaired individuals and those with SCI. Furthermore, iTBS of both biceps and first dorsal interosseus (FDI) was compared to simulation of TMS on MRI derived head models to establish the impact of individualized neuroanatomy. Finally, a motor canonical neural circuit was programmed to display fundamental physiological spiking behavior of membrane potentials. Summary of Results: iTBS did facilitate corticomotor excitability in the biceps of nonimpaired individuals and in those with SCI. iTBS had no group-wide effect on the FDI, highlighting the variability in response to the protocol. TMS response (motor thresholds) and iTBS response (change in MEPs) both were related to parameters extracted from MRI-derived head models representing variations in individual neuroanatomy. The neural circuit model represents a canonical networked unit. In the future, this can be further tuned to exhibit biological variability and generate population-based values being run in parallel, while matching the understood mechanisms of neuroplasticity: disinhibition and LTP. Conclusion: These studies provide missing information of iTBS responsivity by (1) determining group-wide responsiveness in a clinically relevant target; (2) establishing individual level influences that affect responsivity which can be measured prior to iTBS; and (3) beginning design of a tool to test a single neural circuit and its mechanistic responses

Model Driven Optimization of Drug Delivery for Spinal Cord Injury

Spinal cord injuries have an annual new case incidence in the United States of up to 40 cases per million population, with a 36.2% 10 year survival rate in patients over 50 years old. Barely half of survivors from 40 to 60 can perform activities of daily living, regardless of the severity of the initial injury. [1] Spinal cord injury is progressive, mediated by secondary injury, or a subacute inflammatory process that is poorly understood. [2-4] Secondary injury can involve or lead to apoptosis, scarring, cavitation, ischemia, and demyelination, among other sequelae, and many of these are not conducive to neuronal recovery. [4] Therefore there is a need for improved ways of addressing secondary injury mechanisms in these patients. [2] Minocycline can target multiple secondary injury mechanisms through its anti-inflammatory, anti-oxidative, and anti-apoptotic effects. [5-7] However, it is only fully effective at high concentrations. At the systemic levels required for this, minocycline can cause liver toxicity and even death. [8] Minocycline can form self-assembling, water insoluble particles with dextran sulfate and divalent metal cations, with high entrapment efficiency and a stable, slow release. There is potential for a controllable vehicle for drug delivery to optimize dosing with minocycline. [8] Predicting minocycline release behavior of a complex system can be performed by the creation of a model to simplify the system to an equation. This simplification can be guided by a combination of theoretical frameworks and previous empirical data that suggests idealized ratios between the components; previous work suggests the highest entrapment efficiency given a 1.2:1 mass concentration ratio of dextran sulfate:minocycline, in the presence of 7.2 mM Mg2+, suggesting saturation of binding sites at these values. [8] With a high degree of accuracy, this model can help direct the optimization of the drug delivery system. In the case of minocycline:dextran sulfate:Mg2+, the model proposed in this study was able to predict the trend of behavior but not with enough sensitivity for small dosing changes that characterize this delivery method. Biomedical engineering is an interdisciplinary field that combines understanding of various scientific and design expertise. In order to tailor a drug delivery system, as well as help to characterize the mechanism of release, generating a simple model can provide insight to the behavior of the system while reducing time, labor, and cost.M.S., Biomedical Engineering -- Drexel University, 201

Optical quality ZnSe films and low loss waveguides on Si substrates for mid-infrared applications

Zinc Selenide (ZnSe) is a promising mid-infrared waveguide material with high refractive index and wide transparency. Optical quality ZnSe thin films were deposited on silicon substrates by RF sputtering and thermal evaporation, and characterized and compared for material and optical properties. Evaporated films were found to be denser and smoother than sputtered films. Rib waveguides were fabricated from these films and evaporated films exhibited losses as low as 0.6 dB/cm at wavelengths between 2.5 µm and 3.7 µm. The films were also used as isolation/lower cladding layers on Si with GeTe4 as the waveguide core and propagation losses were determined in this wavelength range

Effect of neuroanatomy on corticomotor excitability during and after transcranial magnetic stimulation and intermittent theta burst stimulation

Individual neuroanatomy can influence motor responses to transcranial magnetic stimulation (TMS) and corticomotor excitability after intermittent theta burst stimulation (iTBS). The purpose of this study was to examine the relationship between individual neuroanatomy and both TMS response measured using resting motor threshold (RMT) and iTBS measured using motor evoked potentials (MEPs) targeting the biceps brachii and first dorsal interosseus (FDI). Ten nonimpaired individuals completed sham-controlled iTBS sessions and underwent MRI, from which anatomically accurate head models were generated. Neuroanatomical parameters established through fiber tractography were fiber tract surface area (FTSA), tract fiber count (TFC), and brain scalp distance (BSD) at the point of stimulation. Cortical magnetic field induced electric field strength (EFS) was obtained using finite element simulations. A linear mixed effects model was used to assess effects of these parameters on RMT and iTBS (post-iTBS MEPs). FDI RMT was dependent on interactions between EFS and both FTSA and TFC. Biceps RMT was dependent on interactions between EFS and and both FTSA and BSD. There was no groupwide effect of iTBS on the FDI but individual changes in corticomotor excitability scaled with RMT, EFS, BSD, and FTSA. iTBS targeting the biceps was facilitatory, and dependent on FTSA and TFC. MRI-based measures of neuroanatomy highlight how individual anatomy affects motor system responses to different TMS paradigms and may be useful for selecting appropriate motor targets when designing TMS based therapies

Effect of neuroanatomy on corticomotor excitability during and after transcranial magnetic stimulation and intermittent theta burst stimulation

Individual neuroanatomy can influence motor responses to transcranial magnetic stimulation (TMS) and corticomotor excitability after intermittent theta burst stimulation (iTBS). The purpose of this study was to examine the relationship between individual neuroanatomy and both TMS response measured using resting motor threshold (RMT) and iTBS measured using motor evoked potentials (MEPs) targeting the biceps brachii and first dorsal interosseus (FDI). Ten nonimpaired individuals completed sham-controlled iTBS sessions and underwent MRI, from which anatomically accurate head models were generated. Neuroanatomical parameters established through fiber tractography were fiber tract surface area (FTSA), tract fiber count (TFC), and brain scalp distance (BSD) at the point of stimulation. Cortical magnetic field induced electric field strength (EFS) was obtained using finite element simulations. A linear mixed effects model was used to assess effects of these parameters on RMT and iTBS (post-iTBS MEPs). FDI RMT was dependent on interactions between EFS and both FTSA and TFC. Biceps RMT was dependent on interactions between EFS and and both FTSA and BSD. There was no groupwide effect of iTBS on the FDI but individual changes in corticomotor excitability scaled with RMT, EFS, BSD, and FTSA. iTBS targeting the biceps was facilitatory, and dependent on FTSA and TFC. MRI-based measures of neuroanatomy highlight how individual anatomy affects motor system responses to different TMS paradigms and may be useful for selecting appropriate motor targets when designing TMS based therapies