Most security professionals will tell you that it's a lot easier to attack code systems than it is to defend them, and that this is especially true for large systems. The white hat's job is to secure each and every point of contact, while the black hat's goal is to find just one that's insecure. Whether black hat or white hat, it also helps a lot to know how the system works and exactly what it's doing. When you've got the source code, either because it's open-source, or because you're working inside the company that makes the software, you've got a huge advantage both in finding bugs and in fixing them. In the case of closed-source software, the white hats arguably have the offsetting advantage that they at least can see the source code, and peek inside the black box, while the attackers cannot.

In this paper, we investigate the following two limitations for the existing distractor generation (DG) methods. First, the quality of the existing DG methods are still far from practical use. There is still room for DG quality improvement. Second, the existing DG designs are mainly for single distractor generation. However, for practical MCQ preparation, multiple distractors are desired. Aiming at these goals, in this paper, we present a new distractor generation scheme with multi-tasking and negative answer training strategies for effectively generating \textit{multiple} distractors. The experimental results show that (1) our model advances the state-of-the-art result from 28.65 to 39.81 (BLEU 1 score) and (2) the generated multiple distractors are diverse and show strong distracting power for multiple choice question.

A team of leading security researchers was recently crowned top hackers after finding vulnerabilities across multiple devices including an Alexa-powered Amazon Echo and a Samsung Galaxy S10. Amat Cama and Richard Zhu, who go by Team Fluoroacetate, compromised the devices at an international bug bounty event called Pwn2Own in Tokyo late last week. The event, hosted by Zero Day Initiative, is home to "white hat" hackers who are paid top dollar if they find previously unknown bugs in gadgets supplied by big tech companies. The vulnerability Cama and Zhu found in the Echo allowed them to "take control" of the gadget, according to Pwn2Own. And finding the bug earned them $60,000.

Today we are starting a six-part series on Negative SEO. The series will be broken into three areas and will show how negative search engine optimization (SEO) has an effect on links, content and user signals. Positive SEO under this broader view would be any tactic performed with the intent to positively impact rankings for a uniform resource locator (URL), and possibly its host domain, by manipulating a variable within the links, content or user signals areas. Negative SEO would be any tactic performed with the intent to negatively impact rankings for a URL, and possibly its host domain, by manipulating a variable within the links, content or user signal buckets. If you can accidentally hurt your rankings by shifting a variable, then it would logically suggest that an external entity shifting that same variable associated with your site could result in a ranking decrease or outright deindexation.

Unless you've had your head in the sand over the past few years, you'll have heard about the unprecedented -- and largely unexpected -- advancement in Artificial Intelligence (AI). Perhaps the most public example of this was when Google's company DeepMind used an AI called AlphaGo to beat one of the world's top Go players in 2016. Today, it plays a role in voice recognition software -- Siri, Alexa, Cortana and Google Assistant. It's helping retailers predict what we want to buy. It's even organising our email accounts by sorting the messages we want to see from those we don't.