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Control and communication technologies are key building blocks of cyber-physical systems (CPSes) that can improve the efficiency of the physical processes. However, they also make a CPS vulnerable to cyberattacks that can cause disruptions or even severe damage. This article focuses on one particular type of CPS cyberattack, namely the time delay attack (TDA), which exploits vulnerabilities in the communication channels to cause potentially serious harm to the system. Much work proposed for TDA detection is tested offline only and under strong assumptions. In order to construct a practical solution to deal with real-world scenarios, we propose a deep learning-based method to detect and characterize TDA. Specifically, we design a hierarchical long short-term memory model to process raw data streams from relevant CPS sensors online and continually monitor embedded signals in the data to detect and characterize the attack. Moreover, various strategies of interpreting the outputs of the model are proposed, which allow the user to tune the performance based on different objectives. We evaluate our model on two representative types of CPS, namely power plant control system (PPCS) and automatic generation control (AGC).Code and dataset can be found at: https://github.com/prakharg24/tda For TDA detection, our solution achieves an accuracy of 92% in PPCS, compared with 81% by random forests (RFs) and 72% by k-nearest neighbours (kNNs). For AGC, our solution achieves 98% accuracy, compared with 74% by RFs and 71% by kNNs. It also reduces the mean absolute error in the delay value characterization from about six to two seconds in the PPCS, and from about three seconds to half a second in the AGC, with about 3x to 4x shorter reaction latency in both systems.

Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and, thus, are too resource-hungry and computation-intensive to suit low-capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted a lot of research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models., To appear in TACL 2021. The arXiv version is a pre-MIT Press publication version

Due to recent cyber attacks on various cyber-physical systems (CPSes), traditional isolation based security schemes in the critical systems are insufficient to deal with the smart adversaries in CPSes with advanced information and communication technologies (ICTs). In this paper, we develop real-time assessment and mitigation of an attack's impact as a system's built-in mechanisms. We study a general class of attacks, which we call time delay attack, that delays the transmissions of control data packets in the CPS control loops. Based on a joint stability-safety criterion, we propose the attack impact assessment consisting of (i) a machine learning (ML) based safety classification, and (ii) a tandem stability-safety classification that exploits a basic relationship between stability and safety, namely that an unstable system must be unsafe whereas a stable system may not be safe. In this assessment approach, the ML addresses a state explosion problem in the safety classification, whereas the tandem structure reduces false negatives in detecting unsafety arising from imperfect ML. We apply our approach to assess the impact of the attack on power grid automatic generation control, and accordingly develop a two-tiered mitigation that tunes the control gain automatically to restore safety where necessary and shed load only if the tuning is insufficient. We also apply our attack impact assessment approach to a thermal power plant control system consisting of two PID control loops. A mitigation approach by tuning the PID controller is also proposed. Extensive simulations based on a 37-bus system model and a thermal power plant control system are conducted to evaluate the effectiveness of our assessment and mitigation approaches. Energy Market Authority (EMA) Nanyang Technological University National Research Foundation (NRF) Accepted version This work was supported in part by the National Research Foundation, Prime Minister’s Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, in part by the Energy Innovation Research Programme (EIRP) administered by the Energy Market Authority (EMA), under Award NRF2014EWTEIRP002-026 and Award NRF2017EWTEP003-061, in part by the Device and System-level Detection and Identification of IoT Attacks, funded by SUTD-ZJU IDEA programme, under Award SUTD-ZJU (VP) 201805, and in part by the NTU Start-up Grant.

The Next Generation Sequencing Technique (NGS) has provided affordable and fast method for generating genetic data. Generation of whole Genome Sequence and extract relevant information from this data is still a computationally expensive process. In this paper we demonstrate a novel approach for local alignment of DNA reads with respect to reference genome. For this process we have used Skip-gram model for creating encoding(Nucl2Vec) and k-nearest neighbor for the alignment. With our new approach we have reduced computation cost for local alignment, while achieving accuracy comparable to existing defacto standard BWA-MEM tool.Index TermsGenome, Alignment, Local Alignment, k-nearest Neighbor, Distributed vector representation, Skip-gram