Welcome to World University and School Wiki which anyone can add to or edit. WUaS would like to offer online CLE credits with these great universities, anticipating accrediting WUaS Law Schools in 204 countries. California, the state in which WUaS is incorporated, has 12 online law schools (none of these are ABA approved, but anyone can sit the California Bar exam, regardless of such approval, as I understand it), at present, and WUaS would like to develop another online MIT OCW/Harvard-centric law school, and eventually accredit in all 204 countries in the world, in main languages in those countries, beginning with the 6 United Nations' languages. Online Law Schools Have Yet to Pass the Bar: Many argue that fully online programs aren't the path to a traditional legal career]. WUaS is planning for a "Admitted Students' Day" for the first, matriculating Bachelor's degree class, on or around Saturday, April 14th, 2014, and the second Saturday of April for other degrees in the future.

We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology. The method is based on self-supervised deep learning that has played a key role in facilitating deep anomaly detection on images, where powerful image transformations are available. However, such transformations are widely unavailable for time series. Addressing this, we develop Local Neural Transformations(LNT), a method learning local transformations of time series from data. The method produces an anomaly score for each time step and thus can be used to detect anomalies within time series. We prove in a theoretical analysis that our novel training objective is more suitable for transformation learning than previous deep Anomaly detection(AD) methods. Our experiments demonstrate that LNT can find anomalies in speech segments from the LibriSpeech data set and better detect interruptions to cyber-physical systems than previous work. Visualization of the learned transformations gives insight into the type of transformations that LNT learns.

In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN). GLUE not only automatically learns complex dependencies between variables and uses them to better identify anomalous behavior, but also quantifies its predictive uncertainty, allowing us to account for the variation in the data as well to have more interpretable anomaly detection thresholds. Results on two real world datasets tell us that optimizing the negative Gaussian log likelihood is reasonable because GLUE's forecasting results are at par with GDN and in fact better than the vector autoregressor baseline, which is significant given that GDN directly optimizes the MSE loss. In summary, our experiments demonstrate that GLUE is competitive with GDN at anomaly detection, with the added benefit of uncertainty estimations. We also show that GLUE learns meaningful sensor embeddings which clusters similar sensors together.

Defending computer networks from cyber attack requires timely responses to alerts and threat intelligence. Decisions about how to respond involve coordinating actions across multiple nodes based on imperfect indicators of compromise while minimizing disruptions to network operations. Currently, playbooks are used to automate portions of a response process, but often leave complex decision-making to a human analyst. In this work, we present a deep reinforcement learning approach to autonomous response and recovery in large industrial control networks. We propose an attention-based neural architecture that is flexible to the size of the network under protection. To train and evaluate the autonomous defender agent, we present an industrial control network simulation environment suitable for reinforcement learning. Experiments show that the learned agent can effectively mitigate advanced attacks that progress with few observable signals over several months before execution. The proposed deep reinforcement learning approach outperforms a fully automated playbook method in simulation, taking less disruptive actions while also defending more nodes on the network. The learned policy is also more robust to changes in attacker behavior than playbook approaches.

The city council of Presidio, Texas, voted on June 7, 2021 to approve locating a new camera system for Customs and Border Patrol on city property. The Sentry camera is a re-deployable 30-foot-tall tower bristling with sensors and powered by solar panels. It's made by Anduril, a security technology startup. As the city council agenda notes, Presidio approved locating one such Sentry "on city property near the City of Presidio Waste Water Treatment Plant." Presidio, population 4,000, sits on the US side of the confluence of the Rio Grande and Rio Conchos rivers, across from Ojinaga in Mexico, in the broader Big Bend region of the state.

COMSovereign Holding Corp. (NASDAQ: COMS) ("COMSovereign" or "Company"), a U.S.-based developer of 4G LTE Advanced and 5G Communication Systems and Solutions, today announced that it has executed an agreement to acquire RVision, Inc. ("RVision"), a developer of technologically advanced, environmentally hardened video and communications products and physical security solutions designed for government and private sector commercial industries. Terms of the transaction include total consideration of approximately $5.58 million consisting exclusively of shares of restricted common stock. The transaction is expected to close within approximately 15 days subject to traditional closing conditions. Smart Cities and Smart Campuses (educational and industrial) are urban areas designed to integrate advanced technologies including IoT ("Internet of Things"), AI ("Artificial Intelligence"), machine learning, Big Data, and sustainable or "green" energy systems to benefit and secure the daily lives of its residents. Around the world today, these technologies are being deployed to efficiently improve public services and safety through enhancements to everything from mass transportation and waste management to the real-time monitoring of environmental conditions including air and water quality.

During the past few months, Microsoft Exchange servers have been like chum in a shark-feeding frenzy. Threat actors have attacked critical zero-day flaws in the email software: an unrelenting cyber campaign that the US government has described as "widespread domestic and international exploitation" that could affect hundreds of thousands of people worldwide. Gaining visibility into an issue like this requires a full understanding of all assets connected to a company's network. This type of continuous tracking of inventory doesn't scale with how humans work, but machines can handle it easily. For business executives with multiple, post-pandemic priorities, the time is now to start prioritizing security. "It's pretty much impossible these days to run almost any size company where if your IT goes down, your company is still able to run," observes Matt Kraning, chief technology officer and co-founder of Cortex Xpanse, an attack surface management software vendor recently acquired by Palo Alto Networks. You might ask why companies don't simply patch their systems and make these problems disappear. If only it were that simple. Unless businesses have implemented a way to find and keep track of their assets, that supposedly simple question is a head-scratcher. But businesses have a tough time answering what seems like a straightforward question: namely, how many routers, servers, or assets do they have? If cybersecurity executives don't know the answer, it's impossible to then convey an accurate level of vulnerability to the board of directors. And if the board doesn't understand the risk--and is blindsided by something even worse than the Exchange Server and 2020 SolarWinds attacks--well, the story almost writes itself. That's why Kraning thinks it's so important to create a minimum set of standards.

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers(or invariant checkers). In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter, ensuring that the neural network and the rule checking system are both deceived.We implemented our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average, while simultaneously avoiding detection by rule checkers. Finally, we explore whether these attacks can be mitigated by training the detectors on adversarial samples.

--Neural networks are increasingly used in security applications for intrusion detection on industrial control systems. In this work we examine two areas that must be considered for their effective use. Firstly, is their vulnerability to adversarial attacks when used in a time series setting. Secondly, is potential overestimation of performance arising from data leakage artefacts. T o investigate these areas we implement a long short-term memory (LSTM) based intrusion detection system (IDS) which effectively detects cyber-physical attacks on a water treatment testbed representing a strong baseline IDS. The first attacker is able to manipulate sensor readings on a subset of the Secure Water Treatment (SWaT) system. By creating a stream of adversarial data the attacker is able to hide the cyber-physical attacks from the IDS. For the cyber-physical attacks which are detected by the IDS, the attacker required on average 2.48 out of 12 total sensors to be compromised for the cyber-physical attacks to be hidden from the IDS. The second attacker model we explore is an L bounded attacker who can send fake readings to the IDS, but to remain imperceptible, limits their perturbations to the smallest L value needed. Additionally, we examine data leakage problems arising from tuning for F 1 score on the whole SWaT attack set and propose a method to tune detection parameters that does not utilise any attack data. If attack aftereffects are accounted for then our new parameter tuning method achieved an F 1 score of 0.811 0.0103. I NTRODUCTION Deep learning systems are known to be vulnerable to adversarial attacks at test time. By applying small changes to an input an attacker can cause a machine learning system to mis-classify with a high degree of success. There has been much work on both developing more powerful attacks [1] as well as defences [2]. However, the majority of adversarial machine learning research is focused on the image domain, with consideration of the different challenges that arise within other fields needed [3]. This phenomenon of adversarial examples becomes particularly pertinent when aiming to defend machine learn-Pre-print.

Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.