Security analysts gather essential information about cyber attacks, exploits, vulnerabilities, and victims by manually searching social media sites. This effort can be dramatically reduced using natural language machine learning techniques. Using a new English text corpus containing more than 250K discussions from Stack Exchange, Reddit, and Twitter on cyber and non-cyber topics, we demonstrate the ability to detect more than 90% of the cyber discussions with fewer than 1% false alarms. If an original searched document corpus includes only 5% cyber documents, then our processing provides an enriched corpus for analysts where 83% to 95% of the documents are on cyber topics. Good performance was obtained using term frequency (TF) – inverse document frequency (IDF) (TF–IDF) features and either logistic regression or linear support vector machine (SVM) classifiers. A classifier trained using prior historical data accurately detected 86% of emergent Heartbleed discussions and retrospective experiments demonstrate that classifier performance remains stable up to a year without retraining.

Honeypots are fake resources that gain value in being probed and attacked. They deceive network intruders into detailing the intruder's behavior and the nature of an intended attack. A honeypot's success relies on the quality of its deception and the perceived value to the attacker. In this paper, we emphasize the latter. We model a repeated game where a defender must select from a list of honeypot configurations to detect an adversary's attack. The adversary's attacks each contain their own unique value function and required features to execute an exploit. Each exploits "evolves" by having its value decreases with the number of detections and new attacks may be added to the adversary's arsenal as the game progresses. We show that this model demands the defender to act strategically, by showing the adversary can exploit naive defense strategies. To solve this problem, we leverage the Multi-Armed Bandit (MAB) framework, a class of machine learning problems that demand balance between exploration and exploitation.

Retirees who lost big in the 2008 financial crisis have good reason to worry about President Trump's new rollbacks of Wall Street regulations. In an executive order issued today, Trump sought to undo parts of the 2010 Dodd-Frank act, which curbed some of the finance industry's most egregious recession-spawning practices. In a separate memo, the president ordered a review of the "fiduciary duty rule," which requires retirement fund investors to act in the best interests of their clients. The Wall St bankers may be popping champagne, but Americans haven't forgotten the 2008 financial crisis – and they won't forget today. The opposition may have an unlikely ally in this fight: fintech, the segment of the tech industry trying to automate away so much of what Wall Street does.

This week, more than 2,000 Google employees walked out of work to protest President Trump's immigration ban. Far from disciplining them for leaving their desks, CEO Sundar Pichai and co-founder Sergey Brin treated workers to impassioned speeches of support. "Proud, moved, and touched to be at a company that boldly stands for its people," Googler Sam Tse tweeted. While Pichai and Brin were no doubt speaking from personal conviction--Brin's family fled the former Soviet Union when he was a boy--they also had little choice but to back their employees. Trump's directive cut to the heart of Silicon Valley's treasured values of globalism and openness, values widely embraced by the workers themselves.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Need help preparing a romantic dinner for two? ARMAR will give you a hand.

U.S. Capitol Police are probing the activities of several IT contractors who worked for dozens of House Democrats after they allegedly inappropriately accessed House computers, took congressional computer hardware and made questionable IT-related purchases on behalf of lawmakers. House officials already have revoked the IT and access privileges for the five congressional IT contractors, as police investigate. No arrests have yet been made. Fox News is told the contract employees, shared by about 40 House Democratic members, removed hundreds of thousands of dollars of equipment from offices, including computers and servers. There also were bookkeeping and inventory irregularities where the employees would purchase equipment at one price, bill the congressional office for another and potentially pocket the difference.

Former ExxonMobil Corp. Chief Executive Rex Tillerson was sworn in only Wednesday, and already Congress is moving to benefit the new secretary of state's former--and only--place of work by shredding two major oil industry regulations. Early Friday morning, the Republican-led Senate voted 52 to 47 on a House resolution scrapping a Securities and Exchange Commission rule requiring companies like Exxon and Chevron Corp. to disclose payments they make to foreign governments for the ability to extract oil, minerals and natural gas from their territory. Known as the "extraction rule," it was meant to curb corruption and boost transparency within the oil industry. Standing before the upper house Thursday night, Sen. Elizabeth Warren of Massachusetts railed against the effort to discard the rule. "One of the Republican Party's first orders of business is a giveaway to ExxonMobil that will help corrupt and repressive foreign regimes and make it easy to funnel money to terrorists around the world," she said, adding that companies like Exxon "regularly pay millions" to "corrupt officials" for the rights to drill on their land, and highlighting the "years" necessary to garner bipartisan and even investor support for the law's passage.

It might not have the speed of using a net or the finesse of a rod and line, but a military funded robot made out of jelly could give fishermen some competition. Engineers at the Massachusetts Institute of Technology have created a swimming robot out of gel that can capture a live fish in its'tentacles'. The robot, which looks remarkably similar to a jellyfish and was partly funded by the US Office of Naval Research, can move by pumping water through their body. The robot (pictured) is made from a jelly like material called hydrogel, which gives it a soft rubbery texture and makes it almost see-through. They can also rapidly curl and uncurl their arms by inflating them with water, giving them a fast and firm grip.

Babies learn about their world by pushing and poking objects, putting them in their mouths and throwing them. Carnegie Mellon University scientists are taking a similar approach to teach robots how to recognize and grasp objects around them. Manipulation remains a major challenge for robots and has become a bottleneck for many applications. But researchers at CMU's Robotics Institute have shown that by allowing robots to spend hundreds of hours poking, grabbing and otherwise physically interacting with a variety of objects, those robots can teach themselves how to pick up objects. In their latest findings, presented last fall at the European Conference on Computer Vision, they showed that robots gained a deeper visual understanding of objects when they were able to manipulate them.

Science fiction author Isaac Asimov famously predicted that we'll one day have to program robots with a set of laws that protect us from our mechanical creations. But before we get there, we need rules to ensure that, at the most fundamental level, we're developing AI responsibly and safely. At a recent gathering, a group of experts did just that, coming up with 23 principles to steer the development of AI in a positive direction--and to ensure it doesn't destroy us. The new guidelines, dubbed the 23 Asilomar AI Principles, touch upon issues pertaining to research, ethics, and foresight--from research strategies and data rights to transparency issues and the risks of artificial superintelligence. Previous attempts to establish AI guidelines, including efforts by the IEEE Standards Association, Stanford University's AI100 Standing Committee, and even the White House, were either too narrow in scope, or far too generalized.