MORPH: Towards Automated Concept Drift Adaptation for Malware Detection

Concept drift is a significant challenge for malware detection, as the performance of trained machine learning models degrades over time, rendering them impractical. While prior research in malware concept drift adaptation has primarily focused on active learning, which involves selecting representative samples to update the model, self-training has emerged as a promising approach to mitigate concept drift. Self-training involves retraining the model using pseudo labels to adapt to shifting data distributions. In this research, we propose MORPH -- an effective pseudo-label-based concept drift adaptation method specifically designed for neural networks. Through extensive experimental analysis of Android and Windows malware datasets, we demonstrate the efficacy of our approach in mitigating the impact of concept drift. Our method offers the advantage of reducing annotation efforts when combined with active learning. Furthermore, our method significantly improves over existing works in automated concept drift adaptation for malware detection

Emergent (In)Security of Multi-Cloud Environments

As organizations increasingly use cloud services to host their IT infrastructure, there is a need to share data among these cloud hosted services and systems. A majority of IT organizations have workloads spread across different cloud service providers, growing their multi-cloud environments. When an organization grows their multi-cloud environment, the threat vectors and vulnerabilities for their cloud systems and services grow as well. The increase in the number of attack vectors creates a challenge of how to prioritize mitigations and countermeasures to best defend a multi-cloud environment against attacks. Utilizing multiple industry standard risk analysis tools, we conducted an analysis of multi-cloud threat vectors enabling calculation and prioritization for the identified mitigations and countermeasures. The prioritizations from the analysis showed that authentication and architecture are the highest risk areas of threat vectors. Armed with this data, IT managers are able to more appropriately budget cybersecurity expenditure to implement the most impactful mitigations and countermeasures

Systemic Risk and Vulnerability Analysis of Multi-cloud Environments

With the increasing use of multi-cloud environments, security professionals face challenges in configuration, management, and integration due to uneven security capabilities and features among providers. As a result, a fragmented approach toward security has been observed, leading to new attack vectors and potential vulnerabilities. Other research has focused on single-cloud platforms or specific applications of multi-cloud environments. Therefore, there is a need for a holistic security and vulnerability assessment and defense strategy that applies to multi-cloud platforms. We perform a risk and vulnerability analysis to identify attack vectors from software, hardware, and the network, as well as interoperability security issues in multi-cloud environments. Applying the STRIDE and DREAD threat modeling methods, we present an analysis of the ecosystem across six attack vectors: cloud architecture, APIs, authentication, automation, management differences, and cybersecurity legislation. We quantitatively determine and rank the threats in multi-cloud environments and suggest mitigation strategies.Comment: 27 pages, 9 figure

Ultrastructural Characterization of Serially Passaged Amastigote Like Forms of Leishmania (Leishmania) Donovani

The present study was done to establish an in vitro axenic culture of amastigote like forms of Leishmania (Leishmania) donovani (Dd-8 strain), the causative agent of Indian kala-azar. Transformation of promastigotes to amastigote like forms was induced by temperature shift from 26±1℃ to 34±1℃ at pH 7.0 in NNN medium. These forms were dividing as evidenced by flow cytometry. Scanning and transmission electron microscopic studies revealed a remarkable ultrastructural similarity of these in vitro cultured amastigotes with intracellular amastigotes. These forms have been successfully maintained for a period of more than one year, during which they have remained infective. On subjecting these forms to temperature of 26±1℃, they reverted back to the promastigote forms. Thus a simple NNN medium, free from foetal calf serum has been developed to generate large amounts of amastigote like forms which can be used for further biochemical, immunological and chemotherapeutic studies

ANALYSIS OF SAVITZKY-GOLAY FILTER FOR BASELINE WANDER CANCELLATION IN ECG USING WAVELETS

Electrocardiogram (ECG) has always been the most basic useful and low cost tool for diagnosis. Various kinds of noises can contaminate the ECG signals which lead to incorrect diagnosis. In this paper a new method is developed for removal of baseline wander based on Daubechies wavelet decomposition using adaptive thresholding techniques and Savitzky-Golay filtering. Here ECG records are taken from non-invasive fetal electrocardiogram database, noise is generated using MATLAB instructions and added to original ECG signal. In fact DWT has the quality of better signal decomposition and thresholding has the ability of removing noise from decomposed signal. If we apply Savitzky-Golay filter further then preserving the peak it can smooth out the signal without much destroying its original property. In this paper we have done a comparative study between our proposed method and conventional wavelet method consisting only Daubechies wavelet decomposition along with thresholding techniques. This comparison is done by evaluating different statistical parameters like mean square error (MSE), signal to interference ratio (SIR) and peak signal to noise ratio (PSNR)

ArsenazoIII functionalized gold nanoparticles: SPR based optical sensor for determination of uranyl ions (UO22+) in groundwater

Surface plasmon resonance (SPR) based spectrophotometric determination of UO22+ was carried out by arsenazoIII functionalized gold nanoparticles (AZ-AuNPs) based miniaturized detection assay in ground water samples. AZ-AuNPs were synthesized, characterized by transmission electron microscopy (TEM), x-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS), infrared spectroscopy (IR) and dynamic light scattering (DLS) techniques; AZ-AuNPs were of uniform size (∼10nm), dispersed, highly stable and negative charge surface. The addition of analyte (UO22+) into the detection assay led to UO22+-arsenazoIII complex formation and subsequent release of uncapped gold nanoparticules in solution. Agglomeration based SPR response of gold nanoparticles resulted in visual and spectrophotometric change in the detection assay. The UV-vis spectroscopic investigations showed changes in AZ-AuNPs characteristic absorption peak and an additional peak correspond to UO22+-arsenazoIII complex. Ratio of A650nm/A535nm was used to quantify the concentration of UO22+ in environmental samples. The method showed a linear response from 50−300 ppb (R2> 0.95) for UO22+ with the detection limit of 0.081 µM for ground water samples of total dissolve solids concentration of ∼1000 ppm