Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high-priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98 percent (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63 percent :the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.Comment: 6 page

A Reliable Data Provenance and Privacy Preservation Architecture for Business-Driven Cyber-Physical Systems Using Blockchain

Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate and feed different information retrieval requests using tokens specifically generated for each type of request. Providing both traceability of data operations, and unlinkability of end user activities, a robust blockchain-based CPS is prototyped. Evaluation indicates the architecture is capable of assured data provenance validation and user privacy preservation at a low overhead

Hidden Markov Model and Cyber Deception for the Prevention of Adversarial Lateral Movement

Advanced persistent threats (APTs) have emerged as multi-stage attacks that have targeted nation-states and their associated entities, including private and corporate sectors. Cyber deception has emerged as a defense approach to secure our cyber infrastructure from APTs. Practical deployment of cyber deception relies on defenders\u27 ability to place decoy nodes along the APT path optimally. This paper presents a cyber deception approach focused on predicting the most likely sequence of attack paths and deploying decoy nodes along the predicted path. Our proposed approach combines reactive (graph analysis) and proactive (cyber deception technology) defense to thwart the adversaries\u27 lateral movement. The proposed approach is realized through two phases. The first phase predicts the most likely attack path based on Intrusion Detection System (IDS) alerts and network trace, and the second phase is determining optimal deployment of decoy nodes along the predicted path. We employ transition probabilities in a Hidden Markov Model to predict the path. In the second phase, we utilize the predicted attack path to deploy decoy nodes. However, it is likely that the attacker will not follow that predicted path to move laterally. To address this challenge, we employ a Partially Observable Monte-Carlo Planning (POMCP) framework. POMCP helps the defender assess several defense actions to block the attacker when it deviates from the predicted path. The evaluation results show that our approach can predict the most likely attack paths and thwarts the adversarial lateral movement

Attacker Capability Based Dynamic Deception Model for Large-Scale Networks

In modern days, cyber networks need continuous monitoring to keep the network secure and available to legitimate users. Cyber attackers use reconnaissance mission to collect critical network information and using that information, they make an advanced level cyber-attack plan. To thwart the reconnaissance mission and counterattack plan, the cyber defender needs to come up with a state-of-the-art cyber defense strategy. In this paper, we model a dynamic deception system (DDS) which will not only thwart reconnaissance mission but also steer the attacker towards fake network to achieve a fake goal state. In our model, we also capture the attacker’s capability using a belief matrix which is a joint probability distribution over the security states and attacker types. Experiments conducted on the prototype implementation of our DDS confirm that the defender can make the decision whether to spend more resources or save resources based on attacker types and thwart reconnaissance mission

Trabajos originales de investigación

Cada año, el gobierno de los Estados Unidos detiene y deporta a cientos de miles de personas que antes de su deportación son recluidas durante un promedio de 55 días. Los efectos a corto y largo plazo de la pandemia del coronavirus en la detención y deportación de migrantes continúan siendo evaluados en tiempo real, incluyendo una mejor manera de estudiarlo. Este artículo proporciona un análisis puntual sobre la relación entre la aplicación de la Ley de Inmigración y el confinamiento, las emergencias de salud pública y los métodos etnográficos. Esto proporciona dos aportes. El primero es metodológico y se enfoca en los desafíos y oportunidades de los métodos etnográficos en entornos penitenciarios cuando los protocolos relacionados con la pandemia han planteado desafíos adicionales a la etnografía carcelaria presencial. La segunda contribución es empírica y documenta cómo adaptamos métodos etnográficos a un diseño de investigación interdisciplinario y a las exigencias de la pandemia para estudiar la propagación del coronavirus en cuatro centros de detención de migrantes en Nueva Jersey, EE.UU.Each year the United States government detains and deports hundreds of thousands of people who prior to their removal are held in confinement for an average of 55 days. The short and long-term effects of the coronavirus pandemic on migrant detention and deportation continue to be evaluated in real time, including how we can best study it. This paper provides a timely analysis on the relationship between immigration enforcement and confinement, public health emergencies, and ethnographic methods. It makes two contributions. The first is methodological and focuses on the challenges and opportunities of ethnographic methods in carceral settings when pandemic-related protocols have raised additional challenges to conventional in-person prison ethnography. The second contribution is empirical and documents how we adapted ethnographic methods to an interdisciplinary research design and to the exigencies of the pandemic to study the spread of the coronavirus in four immigrant detention facilities in New Jersey, USA.Cada año, el gobierno de los Estados Unidos detiene y deporta a cientos de miles de personas que antes de su deportación son recluidas durante un promedio de 55 días. Los efectos a corto y largo plazo de la pandemia del coronavirus en la detención y deportación de migrantes continúan siendo evaluados en tiempo real, incluyendo una mejor manera de estudiarlo. Este artículo proporciona un análisis puntual sobre la relación entre la aplicación de la Ley de Inmigración y el confinamiento, las emergencias de salud pública y los métodos etnográficos. Esto proporciona dos aportes. El primero es metodológico y se enfoca en los desafíos y oportunidades de los métodos etnográficos en entornos penitenciarios cuando los protocolos relacionados con la pandemia han planteado desafíos adicionales a la etnografía carcelaria presencial. La segunda contribución es empírica y documenta cómo adaptamos métodos etnográficos a un diseño de investigación interdisciplinario y a las exigencias de la pandemia para estudiar la propagación del coronavirus en cuatro centros de detención de migrantes en Nueva Jersey, EE.UU

Trabajos originales de investigación

Cada año, el gobierno de los Estados Unidos detiene y deporta a cientos de miles de personas que antes de su deportación son recluidas durante un promedio de 55 días. Los efectos a corto y largo plazo de la pandemia del coronavirus en la detención y deportación de migrantes continúan siendo evaluados en tiempo real, incluyendo una mejor manera de estudiarlo. Este artículo proporciona un análisis puntual sobre la relación entre la aplicación de la Ley de Inmigración y el confinamiento, las emergencias de salud pública y los métodos etnográficos. Esto proporciona dos aportes. El primero es metodológico y se enfoca en los desafíos y oportunidades de los métodos etnográficos en entornos penitenciarios cuando los protocolos relacionados con la pandemia han planteado desafíos adicionales a la etnografía carcelaria presencial. La segunda contribución es empírica y documenta cómo adaptamos métodos etnográficos a un diseño de investigación interdisciplinario y a las exigencias de la pandemia para estudiar la propagación del coronavirus en cuatro centros de detención de migrantes en Nueva Jersey, EE.UU.Each year the United States government detains and deports hundreds of thousands of people who prior to their removal are held in confinement for an average of 55 days. The short and long-term effects of the coronavirus pandemic on migrant detention and deportation continue to be evaluated in real time, including how we can best study it. This paper provides a timely analysis on the relationship between immigration enforcement and confinement, public health emergencies, and ethnographic methods. It makes two contributions. The first is methodological and focuses on the challenges and opportunities of ethnographic methods in carceral settings when pandemic-related protocols have raised additional challenges to conventional in-person prison ethnography. The second contribution is empirical and documents how we adapted ethnographic methods to an interdisciplinary research design and to the exigencies of the pandemic to study the spread of the coronavirus in four immigrant detention facilities in New Jersey, USA.Cada año, el gobierno de los Estados Unidos detiene y deporta a cientos de miles de personas que antes de su deportación son recluidas durante un promedio de 55 días. Los efectos a corto y largo plazo de la pandemia del coronavirus en la detención y deportación de migrantes continúan siendo evaluados en tiempo real, incluyendo una mejor manera de estudiarlo. Este artículo proporciona un análisis puntual sobre la relación entre la aplicación de la Ley de Inmigración y el confinamiento, las emergencias de salud pública y los métodos etnográficos. Esto proporciona dos aportes. El primero es metodológico y se enfoca en los desafíos y oportunidades de los métodos etnográficos en entornos penitenciarios cuando los protocolos relacionados con la pandemia han planteado desafíos adicionales a la etnografía carcelaria presencial. La segunda contribución es empírica y documenta cómo adaptamos métodos etnográficos a un diseño de investigación interdisciplinario y a las exigencias de la pandemia para estudiar la propagación del coronavirus en cuatro centros de detención de migrantes en Nueva Jersey, EE.UU

Historical and Current Adenosine Receptor Agonists in Preclinical and Clinical Development

Adenosine receptors (ARs) function in the body’s response to conditions of pathology and stress associated with a functional imbalance, such as in the supply and demand of energy/oxygen/nutrients. Extracellular adenosine concentrations vary widely to raise or lower the basal activation of four subtypes of ARs. Endogenous adenosine can correct an energy imbalance during hypoxia and other stress, for example, by slowing the heart rate by A1AR activation or increasing the blood supply to heart muscle by the A2AAR. Moreover, exogenous AR agonists, antagonists, or allosteric modulators can be applied for therapeutic benefit, and medicinal chemists working toward that goal have reported thousands of such agents. Thus, numerous clinical trials have ensued, using promising agents to modulate adenosinergic signaling, most of which have not succeeded. Currently, short-acting, parenteral agonists, adenosine and Regadenoson, are the only AR agonists approved for human use. However, new concepts and compounds are currently being developed and applied toward preclinical and clinical evaluation, and initial results are encouraging. This review focuses on key compounds as AR agonists and positive allosteric modulators (PAMs) for disease treatment or diagnosis. AR agonists for treating inflammation, pain, cancer, non-alcoholic steatohepatitis, angina, sickle cell disease, ischemic conditions and diabetes have been under development. Multiple clinical trials with two A3AR agonists are ongoing