Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.

A key question in causal inference analyses is how to find subgroups with elevated treatment effects. This paper takes a machine learning approach and introduces a generative model, Causal Rule Sets (CRS), for interpretable subgroup discovery. A CRS model uses a small set of short decision rules to capture a subgroup where the average treatment effect is elevated. We present a Bayesian framework for learning a causal rule set. The Bayesian model consists of a prior that favors simple models for better interpretability as well as avoiding overfitting, and a Bayesian logistic regression that captures the likelihood of data, characterizing the relation between outcomes, attributes, and subgroup membership. The Bayesian model has tunable parameters that can characterize subgroups with various sizes, providing users with more flexible choices of models from the \emph{treatment efficient frontier}. We find maximum a posteriori models using iterative discrete Monte Carlo steps in the joint solution space of rules sets and parameters. To improve search efficiency, we provide theoretically grounded heuristics and bounding strategies to prune and confine the search space. Experiments show that the search algorithm can efficiently recover true underlying subgroups. We apply CRS on public and real-world datasets from domains where interpretability is indispensable. We compare CRS with state-of-the-art rule-based subgroup discovery models. Results show that CRS achieved consistently competitive performance on datasets from various domains, represented by high treatment efficient frontiers.

Microsoft recently acknowledged Russian hackers successfully cyberattacked them. If hackers can penetrate their internal systems, what are the chances your company will suffer the consequences of a future hack? What the Russians have done is very bad, but it's only an example of the cyber threats we all face. The cyber threat world is an arms race. The hackers are starting to use AI, and the only way to successfully defend against future threats is for your company to use AI as well.

Artificial intelligence is providing new opportunities in a range of fields, from business to industrial design to entertainment. How might machine- and deep-learning help us create safer, more sustainable, and resilient built environments? A team of researchers from the NSF NHERI SimCenter, a computational modeling and simulation center for the natural hazards engineering community based at the University of California, Berkeley, have developed a suite of tools called BRAILS--Building Recognition using AI at Large-Scale--that can automatically identify characteristics of buildings in a city and even detect the risks that a city's structures would face in an earthquake, hurricane, or tsunami. Charles (Chaofeng) Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS, says the project grew out of a need to quickly and reliably characterize the structures in a city. "We want to simulate the impact of hazards on all of the buildings in a region, but we don't have a description of the building attributes," Wang said. "For example, in the San Francisco Bay area, there are millions of buildings.

The European Union is the first major power to sound the regulatory klaxon in an attempt to govern the explosion in artificial intelligence-based technology. While some may see this as a threat to a potentially transformative area of innovation, such intervention is timely before it becomes impossible to cage the AI "beast'. In proposals published in April, the EU outlined that it would ban "unacceptable" uses of AI, which it defines as "AI systems considered a clear threat to the safety, livelihoods and rights of people." And, while the EU has taken the lead, it will not be long before others follow. Indeed, beyond AI, there is a general trend toward closer scrutiny of the technology sector with President Joe Biden installing two advocates for regulation within his administration--Lina Khan, just approved by a Senate panel to be an FTC commissioner, and Tim Wu, on the National Economic Council--and the U.K. planning to introduce a new code of practice for technology companies in a bid to curb the domination of tech giants.

European Union (EU) lawmakers have introduced new rules that will shape how companies use artificial intelligence (AI). The rules are the first of their kind to introduce regulation to the sector, and the EU's approach is unique in the world. In the US, tech firms are largely left to themselves, while in China, AI innovation is often government-led and used regularly to monitor citizens without too much hindrance from regulators. The EU bloc, however, is taking an approach that aims to maximise the potential of AI while maintaining privacy laws. There are new regulations around cases that are perceived as endangering people's safety or fundamental rights, such as AI-enabled behaviour manipulation techniques.

"I have nothing to hide" was once the standard response to surveillance programs utilizing cameras, border checks, and casual questioning by law enforcement. Privacy used to be considered a concept generally respected in many countries with a few changes to rules and regulations here and there often made only in the name of the common good. Things have changed, and not for the better. China's Great Firewall, the UK's Snooper's Charter, the US' mass surveillance and bulk data collection -- compliments of the National Security Agency (NSA) and Edward Snowden's whistleblowing -- Russia's insidious election meddling, and countless censorship and communication blackout schemes across the Middle East are all contributing to a global surveillance state in which privacy is a luxury of the few and not a right of the many. As surveillance becomes a common factor of our daily lives, privacy is in danger of no longer being considered an intrinsic right. Everything from our web browsing to mobile devices and the Internet of Things (IoT) products installed in our homes have the potential to erode our privacy and personal security, and you cannot depend on vendors or ever-changing surveillance rules to keep them intact. Having "nothing to hide" doesn't cut it anymore. We must all do whatever we can to safeguard our personal privacy. Taking the steps outlined below can not only give you some sanctuary from spreading surveillance tactics but also help keep you safe from cyberattackers, scam artists, and a new, emerging issue: misinformation. Data is a vague concept and can encompass such a wide range of information that it is worth briefly breaking down different collections before examining how each area is relevant to your privacy and security. A roundup of the best software and apps for Windows and Mac computers, as well as iOS and Android devices, to keep yourself safe from malware and viruses. Known as PII, this can include your name, physical home address, email address, telephone numbers, date of birth, marital status, Social Security numbers (US)/National Insurance numbers (UK), and other information relating to your medical status, family members, employment, and education. All this data, whether lost in different data breaches or stolen piecemeal through phishing campaigns, can provide attackers with enough information to conduct identity theft, take out loans using your name, and potentially compromise online accounts that rely on security questions being answered correctly. In the wrong hands, this information can also prove to be a gold mine for advertisers lacking a moral backbone.

The acceleration of digital transformation and technology adoption have benefited many industries. It has given rise to many innovative technologies and deepfakes are one of them. We all saw how Barack Obama called Donald Trump a'complete dipshit'. This is an example of deepfake videos. Deepfake technology uses AI, Deep Learning, and a Generative Adversarial Network or GAN to build videos or images that seem real but are actually fake.

Enigma, first produced by a German engineer named Arthur Scherbius, is an electro-mechanical encoded communication machine. Its use was evaluated for commercial purposes as it was started after WW1. However, in the aftermath of World War II, Germany was used in military and government services and had great benefits. Enigma models, which are used by Nazi Germany, are much more advanced. Because of its complex encryption systems, it was preferred to send military information.

Amazon will extend its ban on police use of its face-recognition technology beyond the one-year pause it announced last year. "We've advocated that governments should put in place stronger regulations to govern the ethical use of facial recognition technology, and in recent days, Congress appears ready to take on this challenge," the company said at the time. Facial recognition software has often been criticised for its bias against people with darker skin, which could lead to law enforcement investigating innocent citizens - and, in the United States, has been one of the factors in wrongful arrests. A National Institute of Standards and Technology study tested 189 algorithms from 99 developers and found that black and Asian faces were ten to 100 times more likely to be falsely identified by the algorithms compared to white faces. As such, Amazon and other technology companies are under pressure from civil rights activists and their own workers to halt the sale of face-recognition systems to law enforcement agencies.