Goto

Collaborating Authors

Results


How Security Systems are Implementing AI and ML for Threat Detection

#artificialintelligence

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.


Council Post: Lack Of Cybersecurity Consideration Could Upend Industry 4.0

#artificialintelligence

Industry 4.0 signifies a seismic shift in the way the modern factories and industrial systems operate. They consist of large-scale integration across an entire ecosystem where data inside and outside the organization converges to create new products, predict market demands and reinvent the value chain. In Industry 4.0, we see the convergence of information technology (IT) and operational technology (OT) at scale. The convergence of IT/OT is pushing the boundaries of conventional corporate security strategies where the focus has always been placed on protecting networks, systems, applications and processed data involving people and information. In the context of manufacturing industries with smart factories and industrial systems, robotics, sensor technology, 3D printing, augmented reality, artificial intelligence, machine learning and big data platforms work in tandem to deliver breakthrough efficiencies.


Machine learning and evolving threats

#artificialintelligence

Cybercriminals today are extremely organized and often take advantage of social trends to deliver weaponized bundles used to launch an attack against victims. These bundles are typically delivered via phishing emails or malware web sites that include misinformation targeting fears and uncertainty. In recent months, for example, threat intelligence researchers have been seeing an evolution in ransomware attacks targeting those most impacted by COVID-19, such as hospitals and health care providers. In fact, 41 hospitals announced ransomware attacks during the first half of 2020. Ransomware gangs, typically associated with well-established and known criminal organizations are also evolving their tactics for extortion, including publicly shaming victim organizations and threatening to publish files to the internet or auction off PII (personally identifiable information) to the highest bidder.


Twitter Data Case Sparks Dispute, Delay Among EU Privacy Regulators

WSJ.com: WSJD - Technology

European Union privacy regulators are clashing over how much--if anything--to fine Twitter Inc. for its handling of a data breach disclosed last year, delaying progress of the most advanced cross-border privacy case involving a U.S. tech company under the EU's strict new privacy law. The dispute, disclosed in a statement Thursday from Ireland's Data Protection Commission, is one of the first major tests for enforcement of the EU's privacy law, known as GDPR, which took effect in 2018. It raises the specter of disagreements and...


Trustworthy AI Inference Systems: An Industry Research View

arXiv.org Artificial Intelligence

In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems.


This Amazing iPhone App Uses AI to Screen Calls and Block Scammers

#artificialintelligence

Even with the government's do not call list, spammers are still able to get through to millions of mobile phones each year. Not only are they annoying, but some people fall victim to their scams and give out personal information. To avoid this pitfall, you could decide to ignore all incoming phone calls or even try to screen them yourself, but these tactics are time-consuming and come with risks. Let an app like CallHero do the work for you by screening incoming calls and automatically blocking those that are spam. CallHero is part digital bouncer and part artificial intelligence secretary app that answers your calls when you don't want to.


Machine Learning Cybersecurity: How It Works and Companies to Know

#artificialintelligence

In May of 2017, a nasty cyber attack hit more than 200,000 computers in 150 countries over the course of just a few days. Dubbed "WannaCry," it exploited a vulnerability that was first discovered by the National Security Agency (NSA) and later stolen and disseminated online. It worked like this: After successfully breaching a computer, WannaCry encrypted that computer's files and rendered them unreadable. In order to recover their imprisoned material, targets of the attack were told they needed to purchase special decryption software. Guess who sold that software? The so-called "ransomware" siege affected individuals as well as large organizations, including the U.K.'s National Health Service, Russian banks, Chinese schools, Spanish telecom giant Telefonica and the U.S.-based delivery service FedEx.


Machine Learning based Anomaly Detection for 5G Networks

arXiv.org Machine Learning

Protecting the networks of tomorrow is set to be a challenging domain due to increasing cyber security threats and widening attack surfaces created by the Internet of Things (IoT), increased network heterogeneity, increased use of virtualisation technologies and distributed architectures. This paper proposes SDS (Software Defined Security) as a means to provide an automated, flexible and scalable network defence system. SDS will harness current advances in machine learning to design a CNN (Convolutional Neural Network) using NAS (Neural Architecture Search) to detect anomalous network traffic. SDS can be applied to an intrusion detection system to create a more proactive and end-to-end defence for a 5G network. To test this assumption, normal and anomalous network flows from a simulated environment have been collected and analyzed with a CNN. The results from this method are promising as the model has identified benign traffic with a 100% accuracy rate and anomalous traffic with a 96.4% detection rate. This demonstrates the effectiveness of network flow analysis for a variety of common malicious attacks and also provides a viable option for detection of encrypted malicious network traffic.


Machine Unlearning: Fighting for the Right to Be Forgotten

#artificialintelligence

Data protection and privacy have been discussed nonstop as more and more people come to realize just how much personal information they are sharing through the countless apps and websites they regularly visit. It's no longer so surprising to see products you've talked about with friends or concerts you've searched on Google promptly appear as advertisements in your social media feeds. And that has many people concerned. Recent government initiatives such as the EU's General Data Protection Regulation (GDPR) are designed to protect individuals' data privacy, with a core concept being "the right to be forgotten." The bad news is, it's generally difficult to revoke things that have already been shared online or to properly delete such data.


Dark Web's Doppelgängers Aim to Dupe Antifraud Systems

Communications of the ACM

Deep within the encrypted bowels of the dark Web, beyond the reach of regular search engines, hackers and cybercriminals are brazenly trading a new breed of digital fakes. Yet unlike AI-generated deepfake audio and video--which embarrass the likes of politicians and celebrities by making them appear to say or do things they never would--this new breed of imitators is aimed squarely at relieving us of our hard-earned cash. Comprising highly detailed fake user profiles known as digital doppelgängers, these entities convincingly mimic numerous facets of our digital device IDs, alongside many of our tell-tale online behaviors when conducting transactions and e-shopping. The result: credit card fraudsters can use these doppelgängers to attempt to evade the machine-learning-based anomaly-detecting antifraud measures upon which banks and payments service providers have come to rely. It is proving to be big criminal business: many tens of thousands of doppelgängers are now being sold on the dark Web.