Security Aspects of Internet of Things aided Smart Grids: a Bibliometric Survey

The integration of sensors and communication technology in power systems, known as the smart grid, is an emerging topic in science and technology. One of the critical issues in the smart grid is its increased vulnerability to cyber threats. As such, various types of threats and defense mechanisms are proposed in literature. This paper offers a bibliometric survey of research papers focused on the security aspects of Internet of Things (IoT) aided smart grids. To the best of the authors' knowledge, this is the very first bibliometric survey paper in this specific field. A bibliometric analysis of all journal articles is performed and the findings are sorted by dates, authorship, and key concepts. Furthermore, this paper also summarizes the types of cyber threats facing the smart grid, the various security mechanisms proposed in literature, as well as the research gaps in the field of smart grid security.Comment: The paper is published in Elsevier's Internet of Things journal. 25 pages + 20 pages of reference

Physical layer attack identification and localization in cyber–physical grid: An ensemble deep learning based approach

The massive integration of low-cost communication networks and Internet of Things (IoT) in today\u27s cyber–physical grids has been accompanied by significant concerns regarding potential security threats. Specifically, wireless communication technology introduces additional vulnerability in terms of network security. In addition to cyber-security issues that have been investigated extensively, we must consider physical layer security. As such, considerable efforts have been employed toward developing a solution to address cyber-security issues. However, there are limited efforts on developing intrusion detection systems for physical layer security. In this paper, we propose an intelligent attack detection and identification model capable of classifying the attack type in the physical layer based on an ensemble of machine learning methods. Furthermore, the proposed model localizes the attack or fault to specific features or measurements in the system to assist cyber-security professionals in mitigating the effect of the attack in communication networks. The proposed model is evaluated on a smart grids dataset simulated by the Oak Ridge National Laboratories and is compared with traditional machine learning classifiers. The localization of attacks and faults is tested by splitting the data and measuring the correlation of the localization metrics produced by the proposed model. The results demonstrate the effectiveness of the proposed method at classifying and localizing attacks compared to peer approaches

Designing monoclonal antibody fragment-based affinity resins with high binding capacity by thiol-directed immobilisation and optimisation of pore/ligand size ratio

Monoclonal antibody (mAb) based affinity resins usually suffer from low binding capacity, most probably as a result of steric hindrance by the large 150 kDa size of the mAb and a random immobilisation approach. The present work investigates the influence of a variety of factors on dynamic binding capacity (DBC) such as pore/ligand size ratio, accessibility of ligand and ligand density. The effect of pore/ligand size ratio was investigated using Fab and scFv fragments on various resins with different pore sizes. The accessibility of the ligand was investigated by a site-directed immobilisation approach, where three C-terminal tags, PPKPPK, FLAG™ and Cys, were introduced into the Fab fragments for immobilisation on resins via amino-, carboxyl- and thiol-groups, respectively. The scFv fragments were tagged at the C-terminal only with FLAG™ to enable a straight forward purification procedure, and were immobilised to resins via amino- and carboxyl-groups. The target protein had a molecular weight (MW) of 50 kDa. A 3-fold higher dynamic binding capacity at 100% breakthrough (DBC100%) was observed for Fab wild-type (wt) on CNBr-activated Sepharose 4 FF relative to mAb on same resin at the same ligand density. However, no major difference in DBC100% was observed between Fab wt and scFv immobilised on CNBr-activated Sepharose 4 FF at the same ligand density. Thus, further increase of pore/ligand size ratio from Fab to scFv on a resin with average pore size of 300 Å, did not seem to be beneficial. Among the tested tags, only the C-terminal Cys tag proved to site-direct the ligands during immobilisation as it allowed the DBC100% to increase 1.6-fold as compared to Fab wt immobilised via amino-groups on CNBr-activated Sepharose 4 FF and Actigel ALD Superflow at the same ligand density. The influence of ligand density was investigated by selecting immobilised Fab Cys on Sulfhydryl-reactive resin. Increasing ligand density from 0.103 to 0.202 μmol/mL resulted in the same utilisation yield (82–85%), whereas a further increase in ligand density from 0.202 to 0.328 μmol/mL resulted in a 20%-unit decrease in utilisation yield and less steep breakthrough curve, suggesting steric hindrance in the pores of the resin. In addition, site-directed affinity ligands resulted in a more pronounced, sigmoid-shaped breakthrough curve, leading to more efficient use of capacity. The highest DBC100% was obtained for Fab Cys on Sulfhydryl-reactive resin and scFv on Actigel ALD Superflow; 11 mg/mL and 10 mg/mL, respectively, as compared to the DBC100% of 0.8 mg/mL for mAb on CNBr-activated Sepharose 4 FF. Pore/ligand size ratio of 3, which was achieved for Fab ligands on the studied resins, was shown to be feasible for capturing a protein in MW of 50 kDa. Totally, a 13.8-fold improvement in DBC100% was achieved with the Fab-based affinity resin coupled via the C-terminal Cys as compared to the mAb-based affinity resin

Optimizing selectivity of anion hydrophobic multimodal chromatography for purification of a single-chain variable fragment

Single-chain variable fragments (scFv) are widely used in several fields. However, they can be challenging to purify unless using expensive Protein L-based affinity adsorbents or affinity tags. In this work, a purification process for a scFv using mixed-mode (MM) chromatography was developed by design of experiments (DoE) and proteomics for host cell protein (HCP) quantification. Capture of scFv from human embryonic kidney 293 (HEK293) cell feedstocks was performed by hydrophobic charge induction chromatography (MEP HyperCel™), whereafter polishing was performed by anion hydrophobic MM chromatography (Capto Adhere™). The DoE designs of the polishing step included both binding and flow-through modes, the latter being the standard mode for HCP removal. Chromatography with Capto Adhere™ in binding-mode with elution by linear salt gradient at pH 7.5 resulted in optimal yield, purity and HCP reduction factor of 98.9 > 98.5%, and 14, respectively. Totally, 258 different HCPs were removed, corresponding to 84% of identified HCPs. The optimized conditions enabled binding of the scFv to Capto Adhere™ below its theoretical pI, while the majority of HCPs were in the flow-through. Surface property maps indicated the presence of hydrophobic patches in close proximity to negatively charged patches that could potentially play a role in this unique selectivity

Improving the Developability of an Antigen Binding Fragment by Aspartate Substitutions

Aggregation can be a major challenge in the development of antibody-based pharmaceuticals as it can compromise the quality of the product during bioprocessing, formulation, and drug administration. To avoid aggregation, developability assessment is often run in parallel with functional optimization in the early screening phases to flag and deselect problematic molecules. As developability assessment can be demanding with regard to time and resources, there is a high focus on the development of molecule design strategies for engineering molecules with a high developability potential. Previously, Dudgeon et al. [(2012) Proc. Natl. Acad. Sci. U. S. A. 109, 10879-10884] demonstrated how Asp substitutions at specific positions in human variable domains and single-chain variable fragments could decrease the aggregation propensity. Here, we have investigated whether these Asp substitutions would improve the developability potential of a murine antigen binding fragment (Fab). A full combinatorial library consisting of 393 Fab variants with single, double, and triple Asp substitutions was first screened in silico with Rosetta; thereafter, 26 variants with the highest predicted thermodynamic stability were selected for production. All variants were subjected to a set of developability studies. Interestingly, most variants had thermodynamic stability on par with or improved relative to that of the wild type. Twenty-five of the variants exhibited improved nonspecificity. Half of the variants exhibited improved aggregation resistance. Strikingly, while we observed remarkable improvement in the developability potential, the Asp substitutions had no substantial effect on the antigenic binding affinity. Altogether, by combining the insertion of negative charges and the in silico screen based on computational models, we were able to improve the developability of the Fab rapidly