IoT is becoming more common and popular due to its wide range of applications in various domains. They collect data from the real environment and transfer it over the networks. There are many challenges while deploying IoT in a real-world, varying from tiny sensors to servers. Security is considered as the number one challenge in IoT deployments, as most of the IoT devices are physically accessible in the real world and many of them are limited in resources (such as energy, memory, processing power and even physical space). In this paper, we are focusing on these resource-constrained IoT devices (such as RFID tags, sensors, smart cards, etc.) as securing them in such circumstances is a challenging task. The communication from such devices can be secured by a mean of lightweight cryptography, a lighter version of cryptography. More than fifty lightweight cryptography (plain encryption) algorithms are available in the market with a focus on a specific application(s), and another 57 algorithms have been submitted by the researchers to the NIST competition recently. To provide a holistic view of the area, in this paper, we have compared the existing algorithms in terms of implementation cost, hardware and software performances and attack resistance properties. Also, we have discussed the demand and a direction for new research in the area of lightweight cryptography to optimize balance amongst cost, performance and security.

Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

A cyber-physical system (CPS) is a combination of physical system components with cyber capabilities that have a very tight interconnectivity. CPS is a widely used technology in many applications, including electric power systems, communications, and transportation, and healthcare systems. These are critical national infrastructures. Cybersecurity attack is one of the major threats for a CPS because of many reasons, including complexity and interdependencies among various system components, integration of communication, computing, and control technology. Cybersecurity attacks may lead to various risks affecting the critical infrastructure business continuity, including degradation of production and performance, unavailability of critical services, and violation of the regulation. Managing cybersecurity risks is very important to protect CPS. However, risk management is challenging due to the inherent complex and evolving nature of the CPS system and recent attack trends. This paper presents an integrated cybersecurity risk management framework to assess and manage the risks in a proactive manner. Our work follows the existing risk management practice and standard and considers risks from the stakeholder model, cyber, and physical system components along with their dependencies. The approach enables identification of critical CPS assets and assesses the impact of vulnerabilities that affect the assets. It also presents a cybersecurity attack scenario that incorporates a cascading effect of threats and vulnerabilities to the assets. The attack model helps to determine the appropriate risk levels and their corresponding mitigation process. We present a power grid system to illustrate the applicability of our work. The result suggests that risk in a CPS of a critical infrastructure depends mainly on cyber-physical attack scenarios and the context of the organization. The involved risks in the studied context are both from the technical and nontechnical aspects of the CPS.

Generally wireless sensor networks rely of many-to-one communication approach for data gathering. This approach is extremely susceptible to sinkhole attack, where an intruder attracts surrounding nodes with unfaithful routing information, and subsequently presents selective forwarding or change the data that carry through it. A sinkhole attack causes an important threat to sensor networks and it should be considered that the sensor nodes are mostly spread out in open areas and of weak computation and battery power. In order to detect the intruder in a sinkhole attack this paper suggests an algorithm which firstly finds a group of suspected nodes by analyzing the consistency of data. Then, the intruder is recognized efficiently in the group by checking the network flow information. The proposed algorithm's performance has been evaluated by using numerical analysis and simulations. Therefore, accuracy and efficiency of algorithm would be verified.