STELLAR: A Generic EM Side-Channel Attack Protection through Ground-Up Root-cause Analysis

The threat of side-channels is becoming increasingly prominent for resource-constrained internet-connected devices. While numerous power side-channel countermeasures have been proposed, a promising approach to protect the non-invasive electromagnetic side-channel attacks has been relatively scarce. Today\u27s availability of high-resolution electromagnetic (EM) probes mandates the need for a low-overhead solution to protect EM side-channel analysis (SCA) attacks. This work, for the first time, performs a white-box analysis to root-cause the origin of the EM leakage from an integrated circuit. System-level EM simulations with Intel 32 nm CMOS technology interconnect stack, as an example, reveals that the EM leakage from metals above layer 8 can be detected by an external non-invasive attacker with the commercially available state-of-the-art EM probes. Equipped with this `white-box\u27 understanding, this work proposes \textit{STELLAR}: Signature aTtenuation Embedded CRYPTO with Low-Level metAl Routing, which is a two-stage solution to eliminate the critical signal radiation from the higher-level metal layers. Firstly, we propose routing of the entire cryptographic cores power traces using the local lower-level metal layers, whose leakage cannot be picked up by an external attacker. Then, the entire crypto IP is embedded within a Signature Attenuation Hardware (SAH) which in turn suppresses the critical encryption signature before it routes the current signature to the highly radiating top-level metal layers. System-level implementation of the STELLAR hardware with local lower-level metal routing in TSMC 65 nm CMOS technology, with an AES-128 encryption engine (as an example cryptographic block) operating at 40 MHz, shows that the system remains secure against EM SCA attack even after $1 M$ encryptions, with $67\%$ energy efficiency and $1.23\times$ area overhead compared to the unprotected AES

Deciphering Environmental Air Pollution with Large Scale City Data

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors ranging from artificial emissions to natural phenomena are known to be primary causal agents or influencers behind rising air pollution levels. However, the lack of large scale data involving the major artificial and natural factors has hindered the research on the causes and relations governing the variability of the different air pollutants. Through this work, we introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time. We also introduce a transformer based model - cosSquareFormer, for the problem of pollutant level estimation and forecasting. Our model outperforms most of the benchmark models for this task. We also analyze and explore the dataset through our model and other methodologies to bring out important inferences which enable us to understand the dynamics of the causal agents at a deeper level. Through our paper, we seek to provide a great set of foundations for further research into this domain that will demand critical attention of ours in the near future.Comment: Accepted as a Oral Spotlight Paper at International Joint Conference of Artificial Intelligence (IJCAI) 202

Partial characterization of a novel anti-inflammatory protein from salivary gland extract of Hyalomma anatolicum anatolicum (Acari: Ixodidae) ticks

Aim: Hyalomma anatolicum anatolicum ticks transmit Theileria annulata, causative agent of tropical theileriosis to cattle and buffaloes causing a major economic loss in terms of production and mortality in tropical countries. Ticks have evolved several immune evading strategies to circumvent hosts’ rejection and achieve engorgement. Successful feeding of ticks relies on a pharmacy of chemicals located in their complex salivary glands and secreted saliva. These chemicals in saliva could inhibit host inflammatory responses through modulating cytokine secretion and detoxifying reactive oxygen species. Therefore, the present study was aimed to characterize anti-inflammatory peptides from salivary gland extract (SGE) of H. a. anatolicum ticks with a view that this information could be utilized in raising vaccines, designing synthetic peptides or peptidomimetics which can further be developed as novel therapeutics. Materials and Methods: Salivary glands were dissected out from partially fed adult female H. a. anatolicum ticks and homogenized under the ice to prepare SGE. Gel filtration chromatography was performed using Sephadex G-50 column to fractionate the crude extract. Protein was estimated in each fraction and analyzed for identification of anti-inflammatory activity. Sodium dodecyl sulfate - polyacrylamide gel electrophoresis (SDS-PAGE) was run for further characterization of protein in desired fractions. Results: A novel 28 kDa protein was identified in H. a. anatolicum SGE with pronounced anti-inflammatory activity. Conclusion: Purification and partial characterization of H. a. anatolicum SGE by size-exclusion chromatography and SDSPAGE depicted a 28 kDa protein with prominent anti-inflammatory activity

Cell Tracking in Video Microscopy using Bipartite Graph Matching

Automated visual tracking of cells from video microscopy has many important biomedical applications. In this paper, we model the problem of cell tracking over pairs of video microscopy image frames as a minimum weight matching problem in bipartite graphs. The bipartite matching essentially establishes one-to-one correspondences between the cells in different frames. A key advantage of using bipartite matching is the inherent scalability, which arises from its polynomial time-complexity. We propose two different tracking methods based on bipartite graph matching and properties of Gaussian distributions. In both the methods, i) the centers of the cells appearing in two frames are treated as vertices of a bipartite graph and ii) the weight matrix contains information about distance between the cells (in two frames) and cell velocity. In the first method, we identify fast-moving cells based on distance and filter them out using Gaussian distributions before the matching is applied. In the second method, we remove false matches using Gaussian distributions after the bipartite graph matching is employed. Experimental results indicate that both the methods are promising while the second method has higher accuracy

Non-Hodgkin's lymphoma of the maxilla: A rare case report and review

Non-Hodgkin's lymphomas (NHLs) embody a diverse group of malignancies that originate from the lymphoid system. NHL often exhibit in an extranodal pattern, pertaining to the head and neck region. Intraoral sites are much less frequent, accounting for approximately 3.5% of all oral malignancies. Although the exact cause of NHL still remains inconspicuous, however, research has focused on some factors that may contribute to the development of lymphoma, including genetic factors, impaired immune system and viruses, such as HIV or EBV. Clinically, the bony lesion may present as localized or diffuse swelling, with low-grade pain, sweating, unexplained weight loss, fever, etc. Radiographically, these lesions resemble osteomyelitis or other malignancies creating a diagnostic dilemma. Microscopically, diffused lymphomas consist of large tumor cells with large nuclei that are more than twice the size of lymphocytes which may either exhibit centroblastic or immunoblastic features. Here, we report a rare case of NHL affecting the jaws of a 60-year-old male patient

PG-CAS: Pro-Active EM-SCA Probe Detection Using Switched-Capacitor-Based Patterned-Ground Co-Planar Capacitive Asymmetry Sensing

This paper presents the design and analysis of a pro-active strategy to detect the presence of an electromagnetic (EM) side-channel analysis (SCA) attack, using Patterned-Ground co-planar Capacitive Asymmetry Sensing (PG-CAS) system. The PG-CAS system senses the asymmetry created in the plate-ground capacitance and turns on a SCA countermeasure in presence of an EM probe. The proposed PG-CAS system for approaching probe consists of the EM SCA detection sensor plate and circuits. The EM SCA detection sensor is implemented as a grid of four metal plates of the same dimensions using the top metal layer along with a patterned-ground plane at the immediate lower metal layer. The EM SCA detection system consists of a proximity to capacitance conversion circuit, digital synchronization logic circuit to detect and alarm the IC, and an EM SCA countermeasure. When an attack is detected, the countermeasure is turned on based on the deviation of the symmetry of the plate-ground capacitance pairs. The PG-CAS system-level post-layout simulation results using TSMC 65nm technology and Ansys Maxwell show a >5\times improvement in the detection range and a $\sim 29\times$ improvement in power consumption over existing inductive sensing methods for attack detection