Covid-19 is a highly contagious virus which almost freezes the world along with its economy. Its ability of human-to-human and surface-to-human transmission turns the world into catastrophic phase. In this study, our aim is to predict the future conditions of novel Coronavirus to recede its impact. We have proposed deep learning based comparative analysis of Covid-19 cases in India and USA. The datasets of confirmed and death cases of Covid-19 are taken into consideration. The recurrent neural network (RNN) based variants of long short term memory (LSTM) such as Stacked LSTM, Bi-directional LSTM and Convolutional LSTM are used to design the proposed methodology and forecast the Covid-19 cases for one month ahead. Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries. Upward/downward trend of forecasted Covid-19 cases are also visualized graphically, which would be helpful for researchers and policy makers to mitigate the mortality and morbidity rate by streaming the Covid-19 into right direction.

Time Series Forecasting of Covid-19 using Deep Learning Models: India-USA Comparative Case Study

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 .

Security aspects of Internet of Things aided smart grids: A bibliometric survey

Cyber security in smart cities: A review of deep learning-based applications and case studies

Internet-of-Things (IoT) devices and systems will be increasingly targeted by cybercriminals (including nation state-sponsored or affiliated threat actors) as they become an integral part of our connected society and ecosystem. However, the challenges in securing these devices and systems are compounded by the scale and diversity of deployment, the fast-paced cyber threat landscape, and many other factors. Thus, in this article, we design an approach using advanced deep learning to detect cyber attacks against IoT systems. Specifically, our approach integrates a set of long short-term memory (LSTM) modules into an ensemble of detectors. These modules are then merged using a decision tree to arrive at an aggregated output at the final stage. We evaluate the effectiveness of our approach using a real-world data set of Modbus network traffic and obtain an accuracy rate of over 99% in the detection of cyber attacks against IoT devices.

An Ensemble of Deep Recurrent Neural Networks for Detecting IoT Cyber Attacks Using Network Traffic

A survey on machine learning-based malware detection in executable files

An improved two-hidden-layer extreme learning machine for malware hunting

Malware detection is an increasingly important operational focus in cyber security, particularly, given the fast pace of such threats (eg, new malware variants introduced every day) In recent years, there has been increased interest in exploring the use of machine learning techniques in automating and enhancing the effectiveness of malware detection and analysis In this paper, we present a deep recurrent neural network solution as a stacked long short-term memory (LSTM) with a pre-training as a regularization method to avoid random network initialization In our proposed approach, we use global and short dependencies of the inputs With pre-training, we avoid random initialization and are able to improve the accuracy and robustness of malware threat hunting The proposed method speeds up the convergence, in comparison to the stacked LSTM, by reducing the length of malware OpCode or bytecode sequences Hence, the complexity of our final method is reduced This leads to better accuracy , higher Mattews Correlation Coefficients (MCC) , and Area Under the Curve (AUC) in comparison to a standard LSTM with similar detection time Our proposed method can be applied in real-time malware threat hunting, particularly, for safety critical systems, such as electronic health or Internet of Battlefield / Military of Things, where poor convergence of the model could lead to catastrophic consequences We evaluate the effectiveness of our proposed method on Windows, Ransomware, Internet of Things (IoT), and Android malware datasets using both static and dynamic analysis For the IoT malware detection, we also present a comparative summary of the performance on an IoT-specific dataset of our proposed method and the standard stacked LSTM method More specifically, our proposed method achieves an accuracy of 991% in detecting IoT malware samples, with AUC of 0985 and MCC of 095; thus, outperforming standard LSTM-based methods in these key metrics

An Enhanced Stacked LSTM Method With No Random Initialization for Malware Threat Hunting in Safety and Time-Critical Systems

Securing Internet-of-Things (IoT)-enabled cyber–physical systems (CPS) can be challenging, as security solutions developed for general information/operational technology (IT/OT) systems may not be as effective in a CPS setting. Thus, this article presents a two-level ensemble attack detection and attribution framework designed for CPS, and more specifically in an industrial control system (ICS). At the first level, a decision tree combined with a novel ensemble deep representation-learning model is developed for detecting attacks imbalanced ICS environments. At the second level, an ensemble deep neural network is designed to facilitate attack attribution. The proposed model is evaluated using real-world data sets in gas pipeline and water treatment system. Findings demonstrate that the proposed model outperforms other competing approaches with similar computational complexity.

Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems

A deep unsupervised representation learning approach for effective cyber-physical attack detection and identification on highly imbalanced data.

Machine learning is significantly used for malware and adversary detection in the industrial internet of things networks. However, majority of these methods require a significant prior knowledge of malware properties to identify optimal features for malware detection. This is a more significant challenge in IoT environment due to limited availability of malware samples. Some researchers utilized data deformation techniques such as converting malware to images or music to generate features that can be used for malware detection. However, these processes can be time-consuming and require a significant amount of data. This paper proposes MalGan, a framework for detecting and generating new malware samples based on the raw byte code at the edge layer of the Internet of Things (IoT) networks. Convolutional Neural Network (CNN) was utilized to extract high-level features, and boundary-seeking Generative Adversarial Network technique was used to generate new malware samples. Thus, even with a few malware samples, a significant number of previously unseen malware samples are detectable with high accuracy. To capture the short-term and long-term dependency of features, we employed an attention-based model, a combination of CNN and Long Short Term Memory. The attention mechanism improves the model’s performance by increasing or decreasing attention to certain parts of the features. The proposed method is examined extensively using standard Windows and IoT malware datasets. The experimental results indicate that our proposed MalGan is the method of choice, as it offers a higher detection rate compared to the previous malware detection algorithms.

Amir Namavar Jahromi

Papers

An improved two-hidden-layer extreme learning machine for malware hunting

An Enhanced Stacked LSTM Method With No Random Initialization for Malware Threat Hunting in Safety and Time-Critical Systems

Toward Detection and Attribution of Cyber-Attacks in IoT-Enabled Cyber–Physical Systems

A deep unsupervised representation learning approach for effective cyber-physical attack detection and identification on highly imbalanced data.

Generative adversarial network to detect unseen Internet of Things malware