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Artificial Intelligence Update


These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.

100 critical IT policies every company needs, ready for download


Whether you're writing corporate policies for business workers or university policies for faculty and staff, crafting an effective IT policy can be a daunting and expensive task. You could spend hours writing a policies and procedures manual yourself, but consider how much your time is worth. According to job site Glassdoor, the average salary of an IT Director in the U.S. is over $140,000 (depending on geographic location, company, education, etc.). If it takes you one work day to write an IT policy, that single policy cost you $536 ($67 x 8 hours). Don't have time to write a business or university policy?

The Ecosystem of Data Protection and Privacy


She would end up sharing some of those thoughts with her circle, few will be researched further, few will be written down or few will be acted upon. She wasn't entirely aware of the data ecosystem of which she will be more a part of today than she was yesterday The image reflects the current ecosystem of data flow and activities where a user generates data through interaction with the environment around us like websites, search engines, government agencies, retail stores, banks, etc. Data is then collected from these multiple sources, collated, and mapped to build a massive database with PII (Personally identifiable information), behavioral, transactional, demographical information, etc. Which is then sold to companies, law enforcement agencies, and the same person is targeted/threatened/ surveilled. The person interacts again, and the cycle continues.

Unstructured Privacy Data Risks: AI Can Help


As per Gartner, 65% of world population's data will be impacted due to privacy regulations by 2023. In fact, it might happen sooner as most countries wish to provide economic nationalism by restricting cross country data transfers and data rationing by global technology businesses. Another Independent trend coupled with the rise of tighter privacy regulations is the volume of unstructured data being collected. Combined, both structured & unstructured data are projected to grow at the rate of 7-12% on an annual basis. Technological advances along with ever falling storage prices have made it quite easy to collect unstructured data from the customers.

Automating the GDPR Compliance Assessment for Cross-border Personal Data Transfers in Android Applications Artificial Intelligence

Abstract-- The General Data Protection Regulation (GDPR) aims to ensure that all personal data processing activities are fair and transparent for the European Union (EU) citizens, regardless of whether these are carried out within the EU or anywhere else. To this end, it sets strict requirements to transfer personal data outside the EU. However, checking these requirements is a daunting task for supervisory authorities, particularly in the mobile app domain due to the huge number of apps available and their dynamic nature. In this paper, we propose a fully automated method to assess compliance of mobile apps with the GDPR requirements for cross-border personal data transfers. We have applied the method to the top-free 10,080 apps from the Google Play Store. The results reveal that there is still a very significant gap between what app providers and third-party recipients do in practice and what is intended by the GDPR. A substantial 56% of analysed apps are potentially non-compliant with the GDPR cross-border transfer requirements. THE distributed nature of today's digital systems and services across the world [1], or shared between chains of thirdparty not only facilitates the collection of personal data service providers [6], even without the app developer's from individuals anywhere, but also their transfer to different knowledge [7]. Second, apps are distributed through countries around the world [1]. This raises potential global stores, enabling app providers to easily reach markets risks to the privacy of individuals, as the organizations and users beyond its country of residence. In this sending and receiving personal data can be subject to different context, there is a need for constant vigilance by the various data protection laws and, therefore, may not offer an stakeholders, including app developers, supervisory equivalent level of protection.

How Online Privacy Issues Will Shape Future Use Of Artificial Intelligence In Advertising


Privacy restrictions are pushing many marketer toward the use of artificial intelligence in order to ... [ ] delive more targeted messages. The trend toward greater focus on privacy issues has been going on for some time and is starting to come to a head. More restrictions on the sharing and merging of data on individuals has been leading to advertisers to look for effective ways to target and reach consumers, including using the use of behavioral targeting supplemented by the use of artificial intelligence (AI). At a time when privacy regulations are sometimes fragmented and confusing but changing, it is critically important for marketers to monitor changes in the regulatory environment. Against this backdrop, I interviewed Sheri Bachstein, IBM's Global Head of Watson Advertising to get her insights and predictions on the future of privacy regulation and how it will affect advertisers, particularly as regards the use of AI and came away with three major takeaways: The European Union's General Data Protection Regulation and the California Consumer Privacy Act are already leading to the devaluation of traditional third-party cookies and the way many advertisers do business.

Blockchained Federated Learning for Threat Defense Artificial Intelligence

Given the increasing complexity of threats in smart cities, the changing environment, and the weakness of traditional security systems, which in most cases fail to detect serious threats such as zero-day attacks, the need for alternative more active and more effective security methods keeps increasing. Such approaches are the adoption of intelligent solutions to prevent, detect and deal with threats or anomalies under the conditions and the operating parameters of the infrastructure in question. This research paper introduces the development of an intelligent Threat Defense system, employing Blockchain Federated Learning, which seeks to fully upgrade the way passive intelligent systems operate, aiming at implementing an Advanced Adaptive Cooperative Learning (AACL) mechanism for smart cities networks. The AACL is based on the most advanced methods of computational intelligence while ensuring privacy and anonymity for participants and stakeholders. The proposed framework combines Federated Learning for the distributed and continuously validated learning of the tracing algorithms. Learning is achieved through encrypted smart contracts within the blockchain technology, for unambiguous validation and control of the process. The aim of the proposed Framework is to intelligently classify smart cities networks traffic derived from Industrial IoT (IIoT) by Deep Content Inspection (DCI) methods, in order to identify anomalies that are usually due to Advanced Persistent Threat (APT) attacks.

Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS Artificial Intelligence

Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This paper is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this paper, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.

The value of a good defence


Let us consider a scenario: one night, an executive responsible for operations for a remote downstream oil and gas refinery gets a call from one of their subordinates saying things started acting up ever since they plugged in a USB they brought from home. Multiple processes have become unstable and commands sent to equipment are not executed as requested. Panicking, they say there has been a cyber attack on the supervisory control and data acquisition (SCADA) system. Valves, pumps, and compressors connected to the system are going haywire, and the organisation's legacy systems were not equipped to prevent whatever new malware snuck into the system. Production comes to a halt for two days.

How Security Systems are Implementing AI and ML for Threat Detection


A recent study showed that over 90% of security operating centres are now implementing or considering the use of AI and machine learning to detect and defend against digital threats. What is the traditional method for threat detection, what has AI and ML allowed, and how is the hardware world reacting to threats? Since their introduction, computers have played a key role in modern life, providing services such as internet access, online banking, message exchange, and remote work. However, the transmission of sensitive information along with the processing capabilities of any single computer has also resulted in the development of malware by cybercriminals. These programs fall under several categories, including viruses, trojans, and worms, all of which perform different tasks. Of these, their exact function can be separated further; some malware works to destroy a system while others may steal sensitive information.