Apple's Siri AI will be powered by Gemini The government still plans to fully transition to digital right-to-work checks by 2029. Protesters take part in a'No to Digital ID' demonstration. The UK government has backtracked on a plan to require all workers to have a digital ID following a backlash. It will no longer be mandatory to register with the digital ID program to prove one has the right to work in the country, as the reports. The government announced the now-scrapped digital ID requirement in September.

Advanced Persistent Threat (APT) attacks are highly sophisticated and employ a multitude of advanced methods and techniques to target organizations and steal sensitive and confidential information. APT attacks consist of multiple stages and have a defined strategy, utilizing new and innovative techniques and technologies developed by hackers to evade security software monitoring. To effectively protect against APTs, detecting and predicting APT indicators with an explanation from Machine Learning (ML) prediction is crucial to reveal the characteristics of attackers lurking in the network system. Meanwhile, Federated Learning (FL) has emerged as a promising approach for building intelligent applications without compromising privacy. This is particularly important in cybersecurity, where sensitive data and high-quality labeling play a critical role in constructing effective machine learning models for detecting cyber threats. Therefore, this work proposes XFedHunter, an explainable federated learning framework for APT detection in Software-Defined Networking (SDN) leveraging local cyber threat knowledge from many training collaborators. In XFedHunter, Graph Neural Network (GNN) and Deep Learning model are utilized to reveal the malicious events effectively in the large number of normal ones in the network system. The experimental results on NF-ToN-IoT and DARPA TCE3 datasets indicate that our framework can enhance the trust and accountability of ML-based systems utilized for cybersecurity purposes without privacy leakage.

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce incorrect output or fail to work altogether. The field of explainable AI planning (XAIP) has sought to develop techniques that make the decision making of sequential decision making AI systems more explainable to end-users. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always correct. In this work, we examine novice user interactions with a non-robust IDS system -- one that occasionally recommends the wrong action, and one that may become unavailable after users have become accustomed to its guidance. We introduce a novel explanation type, subgoal-based explanations, for planning-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance, improve user ability to distinguish optimal and suboptimal IDS recommendations, are preferred by users, and enable more robust user performance in the case of IDS failure

The rapid increase in the use of IoT devices brings many benefits to the digital society, ranging from improved efficiency to higher productivity. However, the limited resources and the open nature of these devices make them vulnerable to various cyber threats. A single compromised device can have an impact on the whole network and lead to major security and physical damages. This paper explores the potential of using network profiling and machine learning to secure IoT against cyber-attacks. The proposed anomaly-based intrusion detection solution dynamically and actively profiles and monitors all networked devices for the detection of IoT device tampering attempts as well as suspicious network transactions. Any deviation from the defined profile is considered to be an attack and is subject to further analysis. Raw traffic is also passed on to the machine learning classifier for examination and identification of potential attacks. Performance assessment of the proposed methodology is conducted on the Cyber-Trust testbed using normal and malicious network traffic. The experimental results show that the proposed anomaly detection system delivers promising results with an overall accuracy of 98.35% and 0.98% of false-positive alarms.

In the past year, thousands of people in the U.S. have tried to trick facial identification verification to fraudulently claim unemployment benefits from state workforce agencies, according to identity verification firm ID.me Inc. The company, which uses facial-recognition software to help verify individuals on behalf of 26 U.S. states, says that between June 2020 and January 2021 it found more than 80,000 attempts to fool the selfie step in government ID matchups among the agencies it worked with. That included people wearing special masks, using deepfakes--lifelike images generated by AI--or holding up images or videos of other people, says ID.me Chief Executive Blake Hall. A look at how innovation and technology are transforming the way we live, work and play. Facial recognition for one-to-one identification has become one of the most widely used applications of artificial intelligence, allowing people to make payments via their phones, walk through passport checking systems or verify themselves as workers.

Italian researchers have developed a method by which it's possible to bypass facial recognition ID checks for any user, in systems that have been trained on a Deep Neural Network (DNN). The approach works even for target users that enrolled into the system after the DNN was trained, and potentially enables the providers of end-to-end encrypted systems to unlock the data of any user via facial ID authentication, even in scenarios where that is not supposed to be possible. The paper, from the Department of Information Engineering and Mathematics at the University of Siena, outlines a possible compromising of user-encrypted facial ID verification systems by introducing'poisoned' facial images into the training data sets that power them. Once introduced into the training set, the owner of the poisoned face is able to unlock the account of any user through facial ID authentication. Images used in the'Master Key' system, to be included at the training phase.

Binary classification is widely used in ML production systems. Monitoring classifiers in a constrained event space is well known. However, real world production systems often lack the ground truth these methods require. Privacy concerns may also require that the ground truth needed to evaluate the classifiers cannot be made available. In these autonomous settings, non-parametric estimators of performance are an attractive solution. They do not require theoretical models about how the classifiers made errors in any given sample. They just estimate how many errors there are in a sample of an industrial or robotic datastream. We construct one such non-parametric estimator of the sample errors for an ensemble of weak binary classifiers. Our approach uses algebraic geometry to reformulate the self-assessment problem for ensembles of binary classifiers as an exact polynomial system. The polynomial formulation can then be used to prove - as an algebraic geometry algorithm - that no general solution to the self-assessment problem is possible. However, specific solutions are possible in settings where the engineering context puts the classifiers close to independent errors. The practical utility of the method is illustrated on a real-world dataset from an online advertising campaign and a sample of common classification benchmarks. The accuracy estimators in the experiments where we have ground truth are better than one part in a hundred. The online advertising campaign data, where we do not have ground truth data, is verified by an internal consistency approach whose validity we conjecture as an algebraic geometry theorem. We call this approach - algebraic ground truth inference.

Government usually isn't the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia's chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation's push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens. "We want the government to be as lean as possible," says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden's Umeå University on using the Internet of Things and sensor data in government services. Estonia's government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.

Government usually isn't the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia's chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation's push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens. "We want the government to be as lean as possible," says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden's Umeå University on how to use AI in government services. Estonia's government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.

There are significant, real-world benefits to having an accepted and recognized identity. That's why the concept of a digital identity is being pursued around the world, from Australia to India. From airports to health records systems, technologists and policy makers with good intentions are digitizing our identities, making modern life more efficient and streamlined. Governments seek to digitize their citizens in an effort to universalize government services, while the banking, travel, and insurance industries aim to create more seamless processes for their products and services. In places like Syria and Jordan, refugees are often displaced without an identity.