Artificial intelligence (AI) is swiftly fueling the development of a more dynamic world. AI, a subfield of computer science that is interconnected with other disciplines, promises greater efficiency and higher levels of automation and autonomy. Simply put, it is a dual-use technology at the heart of the fourth industrial revolution. Together with machine learning (ML) -- a subfield of AI that analyzes large volumes of data to find patterns via algorithms -- enterprises, organizations, and governments are able to perform impressive feats that ultimately drive innovation and better business. The use of both AI and ML in business is rampant.
Cyber criminals could exploit emerging technologies including artificial intelligence and machine learning to help conduct attacks against autonomous cars, drones and Internet of Things-connected vehicles, according to a report from the United Nations, Europol and cybersecurity company Trend Micro.While AI and machine learning can bring "enormous benefits" to society, the same technologies can also bring a range of threats that can enhance current forms of crime or even lead to the evolution of new malicious activity. "As AI applications start to make a major real-world impact, it's becoming clear that this will be a fundamental technology for our future," said Irakli Beridze, head of the Centre for AI and Robotics at the United Nations Interregional Crime and Justice Research Institute. "However, just as the benefits to society of AI are very real, so is the threat of malicious use," he added.SEE: Cybersecurity: Let's get tactical (ZDNet/TechRepublic special feature) Download the free PDF version (TechRepublic)In addition to super-powering phishing, malware and ransomware attacks, the paper warns that by abusing machine learning, cyber criminals could conduct attacks that could have an impact on the physical world.For example, machine learning is being implemented in autonomous vehicles …
Differential privacy is a data anonymization technique that's used by major technology companies such as Apple and Google. The goal of differential privacy is simple: allow data analysts to build accurate models without sacrificing the privacy of the individual data points. But what does "sacrificing the privacy of the data points" mean? Well, let's think about an example. Suppose I have a dataset that contains information (age, gender, treatment, marriage status, other medical conditions, etc.) about every person who was treated for breast cancer at Hospital X.
Words for health and the human body often make their way into the language we use to describe IT. Computers get viruses; companies manage their security hygiene; incident response teams train on their cyber fitness. Framing IT concepts in terms of health can also be useful when looking at security operations centers (SOCs) and jobs in cybersecurity. For many businesses and other entities today, SOCs are not the healthiest they could be. Jobs in cybersecurity can be stressful and overwhelming due to the volume of alerts.
The most common type of fraud banks face globally is credit card fraud via identity theft. The level of credit card fraud reports increased more than fivefold between 2014 and 2019. And this figure continues to grow. There are a lot of ready-made solutions for fraud prevention in banking, like the one developed by SPD Group, and also, it is possible to come up with a personalized protective tool to meet the needs and respond to the risks of a certain bank.
The significance of artificial intelligence and machine learning (AIML) has increased by much in technology in recent years. It has gone to a point where they are helping businesses gain an advantage over their competitors. With the ever-increasing volumes of data generated each day, it becomes essential to process it in real-time. This is where AIML comes into the picture as the technology can help process and analyze volumes of data within minutes. The relevance of IoT devices, too, has been on the rise.
Artificial intelligence is probably the future of security software regarding how many processes it can improve and how little resources it requires. Positively, it will be integrated into the advanced antivirus programs and take on more and more features. Although not all the antiviruses have AI integrated, it is still essential to protect personal gear and information from intruders and hacker attacks. If you need to find a porter antivirus, read professional and common user reviews. This way, you'll be able to see how good is AVG antivirus, Avast, or any other one, before AI can handle all the security processes. So, for starters, artificial intelligence can be classified into two types.
In the wake of an increase in cyber attacks against machine learning (ML) systems, Microsoft along with MITRE and contributions from 11 other organizations, have released the Adversarial ML Threat Matrix. The Adversarial ML Threat Matrix is an open ATT&CK-style framework to help security analysts detect, respond to, and remediate threats against ML systems. Machine learning (ML) is often seen as a subset of artificial intelligence (AI) and is based on the ability of systems to automatically learn and improve from its experience. Many industries, such as finance, healthcare, and defense, have used ML to transform their businesses and positively impact people worldwide. With the ML and AI advancements, however, Microsoft warned that many organizations have not kept up on security of their ML systems.
The future of corporate cybersecurity seems to lie in artificial intelligence (AI) and machine learning (ML) solutions, a new report from global IT company Wipro suggests. According to Wipro's annual State of Cybersecurity Report (SOCR), almost half (49 percent) of all cybersecurity-related patents filed in the last four years have centered on AI and ML application. Almost half of the 200 organizations that participated in the report also said they are expanding cognitive detection capabilities to tackle unknown attacks in their Security Operations Centers (SOC). From a global perspective, one of the main threats for organizations in the private sector seems to be potential espionage attacks from nation-states. Almost all (86 percent) cyberattacks that came from state-sponsored actors fall under the espionage category and almost half (46 percent) of those attacks targeted the private sector.