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Can a critical industrial system, such as a nuclear reactor, be made self-aware and cognizant of its operational history? Can it alert authorities covertly to malicious intrusion without exposing its defense mechanisms? What if the intruders are highly knowledgeable adversaries, or even insiders that may have designed the system? This thesis addresses these research questions through a novel physical process defense called Covert Cognizance (C2). C2 serves as a last line of defense to industrial systems when existing information and operational technology defenses have been breached by advanced persistent threat (APT) actors or insiders. It is an active form of defense that may be embedded in an existing system to induce intelligence, i.e., self-awareness, and make various subsystems aware of each other. It interacts with the system at the process level and provides an additional layer of security to the process data therein without the need of a human in the loop. The C2 paradigm is founded on two core requirements – zero-impact and zero-observability. Departing from contemporary active defenses, zero-impact requires a successful implementationto leave no footprint on the system ensuring identical operation while zero-observability requires that the embedding is immune to pattern-discovery algorithms. In other words, a third-party such as a malicious intruder must be unable to detect the presence of the C2 defense based on observation of the process data, even when augmented by machine learning tools that are adept at pattern discovery. In the present work, nuclear reactor simulations are embedded with the C2 defense to induce awareness across subsystems and defend them against highly knowledgeable adversaries that have bypassed existing safeguards such as model-based defenses. Specifically, the subsystems are made aware of each other by embedding critical information from the process variables of one sub-module along the noise of the process variables of another, thus rendering the implementation covert and immune to pattern discovery. The implementation is validated using generative adversarial nets, representing a state-of-the-art machine learning tool, and statistical analysis of the reactor states, control inputs, outputs etc. The work is also extended to data masking applications via the deceptive infusion of data (DIOD) paradigm. Future work focuses on the development of automated C2 modules for “plug ‘n’ play” deployment onto critical infrastructure and/or their digital twins.

Additional file 2: Figure 1. Fragmentation profiles of libraries as assessed by Tapestation and GC content and bias of reads from different libraries. Figure 2. Sequencing data yield and performance of libraries. Figure 3. Comparison of genome coverage to the theoretical Poisson distribution. Figure 4. Correlation between variant calling performance and insert size. Figure 5. Intersections of variant calls.

Additional File 3. Supplementary Figures.

Additional File 1. Purity plots of T cell suspensions.

Primers used for RT-qPCR. Gene name and symbol with forward and reverse primer sequences (5â ˛ -3â ˛) are given. (DOCX 19 kb)

of A novel ultra high-throughput 16S rRNA gene amplicon sequencing library preparation method for the Illumina HiSeq platform

Cluster dendrogram for differentially abundant mRNAs in Atlantic salmon eggs from good versus poor egg quality groups, over the course of postovulatory retention period. The uncertainty in hierarchical cluster analysis was performed by pvclust using average distance method in R statistical package. P-values of clusters are assigned for each branch. The red is approximately unbiased (AU), the green is bootstrap probability (BP); 10,000 bootstrap iterations were used. (PDF 116 kb)

Plot of the optimum number of clusters for k-means clustering of all differentially accumulated transcripts determined by within groups sum of squares by number of clusters. The plot determines six clusters (red arrow) as the best solution to partition the differentially accumulated transcripts. (PDF 16 kb)

ddRAD-seq pilot study on Calanus finmarchicus; Protocols of library preparation and capture in Calanus spp.; Custom analyses scripts used for sequence capture data analyses.

RNA concentration and quality scores for sequencing library preparation. RNA quality was measured by bioanalyzer. RNA was extracted using Trizol and RNeasy kit. N/Aâ =â not available. (DOCX 22 kb)

Relative abundance of hepcidin-1 in Atlantic salmon egg grouped as poor and good quality (n =â 8) as measured by RT-qPCR method. (DOCX 109 kb)

Heatmaps of differentially accumulated mRNAs in eggs of Atlantic salmon over the course of postovulatory retention period. Each row represents a transcript; dark red color represents high accumulation and yellow color represents low accumulation. (PDF 58 kb)

Mixed ligand complexes (1a–1c) of type MAB (where M = Ni(II)/Cu(II)/Zn(II); A = 5-FU; B = phe) were isolated in their solid state and characterized by various physico-chemical and spectral techniques. Thermal studies (TG/DTA) on the synthesized mixed ligand complexes (1a–1c) show an initial loss of coordinated water molecule followed by decomposition of the organic residue. XRD and SEM analyses suggest the microcrystalline and homogeneous morphology for MAB complexes. The in vitro antimicrobial, antioxidant, nuclease cleavage and binding interactions of the free ligand(A) and its mixed ligand systems were studied. Further, the 3D molecular modeling and analysis of Cu(II)–5-FU(A)–phe(B) mixed ligand complex has been undertaken. In addition, the complex formation equilibria were also studied pH-metrically in aqueous medium (I = 0.15 M) for Cu(II) ion with 5-Fluorouracil (5-FU; A) and some bioactive amino acid ligands(B) viz., glycine(gly), l -alanine(ala), l -valine(val) and l -phenylalanine(phe). The calculated stabilization parameters Δlog K, log X, log X′ and % R.S. indicate that mixed ligand systems have higher stabilities than their binary counterparts. Thermodynamic parameters (Δ‡G, Δ‡H and Δ‡S) have also been determined from the stability constants at 300, 310, 320 and 330 ± 0.1 K. The complexation behavior has been studied by means of spectrophotometry at different pH. The biological studies of the mixed ligand systems show moderate activities.