Deep neural networks face persistent challenges in defending against backdoor attacks, leading to an ongoing battle between attacks and defenses. While existing backdoor defense strategies have shown promising performance on reducing attack success rates, can we confidently claim that the backdoor threat has truly been eliminated from the model? To address it, we re-investigate the characteristics of the backdoored models after defense (denoted as defense models). Surprisingly, we find that the original backdoors still exist in defense models derived from existing post-training defense strategies, and the backdoor existence is measured by a novel metric called backdoor existence coefficient. It implies that the backdoors just lie dormant rather than being eliminated. To further verify this finding, we empirically show that these dormant backdoors can be easily re-activated during inference stage, by manipulating the original trigger with well-designed tiny perturbation using universal adversarial attack.

At its most basic level, machine learning (ML) can be defined as the ability that allows computers to learn more without the need for extensive programming.

The highest level in the Endsley situation awareness model is called projection when the status of elements in the environment in the near future is predicted. In cybersecurity situation awareness, the projection for an Advanced Persistent Threat (APT) requires predicting the next step of the APT. The threats are constantly changing and becoming more complex. As supervised and unsupervised learning methods require APT datasets for projecting the next step of APTs, they are unable to identify unknown APT threats. In reinforcement learning methods, the agent interacts with the environment, and so it might project the next step of known and unknown APTs. So far, reinforcement learning has not been used to project the next step for APTs. In reinforcement learning, the agent uses the previous states and actions to approximate the best action of the current state. When the number of states and actions is abundant, the agent employs a neural network which is called deep learning to approximate the best action of each state. In this paper, we present a deep reinforcement learning system to project the next step of APTs. As there exists some relation between attack steps, we employ the Long- Short-Term Memory (LSTM) method to approximate the best action of each state. In our proposed system, based on the current situation, we project the next steps of APT threats.

What is Mobile Machine Learning and How do you deploy #MachineLearning on mobile devices? Mobile #ML, A mobile app intelligent to modify itself according to user's needs without your control, analyze big sets of different information (text, visual, audio, biometric) in order to make decisions, can tailor your app according to personal needs of every single user. How Are Developers Using Machine Learning for Mobile Apps? - For Filtering and #Security - For #ArtificialIntelligence - For Predictive #Analytics ML & Mobile - Deploying Models on The Edge, Virtually all cutting edge technologies can benefit from it, - Improve unsupervised #algorithms - Enhance personalization - Increase adoption of #quantumcomputing - Improve #cognitive services - Rise of #robots Strategic Implications of ML and Mobile, Training #Data - Data which has been tagged, categorized, or otherwise sorted by humans. Software - The #software library which builds the machine learning models by evaluating training data. Hardware: The CPUs and GPUs which run the software's calculations.

WSJ: Artificial Intelligence Touches All Industries "Just about every industry will be impacted in the years ahead, 'primarily through AI's ability to reduce costs, but also through its ability to help grow sales.'"

It seems like every vendor in the data security industry makes predictions this time of year. Which ones should you pay attention to? All of them, says Dan Lohrmann, who formerly served as the state of Michigan's CISO and CTO. See Also: IoT is Happening Now: Are You Prepared? "I really view it as something that professionals need to widen their perspectives," Lohrmann says in an interview with Information Security Media Group.

Since the 2013 Target breach, it's been clear that companies need to respond better to security alerts even as volumes have gone up. With this year's fast-spreading ransomware attacks and ever-tightening compliance requirements, response must be much faster. Adding staff is tough with the cybersecurity hiring crunch, so companies are turning to machine learning and artificial intelligence (AI) to automate tasks and better detect bad behavior. In a cybersecurity context, AI is software that perceives its environment well enough to identify events and take action against a predefined purpose. AI is particularly good at recognizing patterns and anomalies within them, which makes it an excellent tool to detect threats.

WIRED Money takes place in Studio Spaces, London on May 18, 2017. For more details and to purchase your ticket visit wiredevent.co.uk "Breaking: Two Explosions in the White House and Barack Obama is injured." At the time of the tweet, AP's account had around two million followers. The post was favourited, retweeted, and spread. At 13:13, AP confirmed the tweet was fake.

Cyber criminals are constantly seeking new ways to perpetrate a breach but thanks to artificial intelligence (AI) and its subset machine learning, it's becoming possible to fight off these attacks automatically. The secret is in machine learning's ability to monitor network traffic and learn what's normal within a system, using this information to flag up any suspicious activity. As the technology's name suggests, it's able to use the vast amounts of security data collected by businesses every day to become more effective over time. At the moment, when the machine spots an anomaly, it sends an alert to a human – usually a security analyst – to decide if an action needs to be taken. But some machine learning systems are already able to respond themselves, by restricting access for certain users, for example.

While we don't know much about the Radeon Vega Mobile GPU yet, it's not exactly a surprising announcement. Gamers have been waiting eagerly to see when AMD's new graphics hardware would make it into high-powered laptops. In October, the company revealed that Vega was coming to its new Ryzen mobile processors. It was only a matter of time until it had a more powerful dedicated offering. AMD is also positioning it as something you'd find in ultrathin notebooks, and not just chunky gaming machines.