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Cyberwarfare


Survey finds 96% of execs are considering adopting 'defensive AI' against cyberattacks

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Register for a free or VIP pass today. "Offensive AI" will enable cybercriminals to direct attacks against enterprises while flying under the radar of conventional, rules-based detection tools. That's according to a new survey published by MIT Technology Review Insights and Darktrace, which found that more than half of business leaders believe security strategies based on human-led responses are failing. The MIT and Darktrace report surveyed more than 300 C-level executives, directors, and managers worldwide to understand how they perceive the cyberthreats they're up against. A high percentage of respondents (55%) said traditional security solutions can't anticipate new AI-driven attacks, while 96% said they're adopting "defensive AI" to remedy this.


How AI and Machine Learning Are Helping In Cybersecurity?

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The internet is becoming a vital part of our day-to-day lives and with every second that passes by, a new change takes place over the internet. The internet is no doubt a very useful place but there are risks that are associated with the internet, especially those that affect the security and privacy of the users. With the advent of AI and Machine Learning, every process is automated and this is making things convenient for internet users, especially cybersecurity which has improved drastically due to the advent of AI & Machine Learning. AI & Machine Learning can recognize different patterns that are used in data helping the security systems to learn from them. Cybersecurity is the protection of computers, networks, and other similar devices from damage, information theft, or any other harm.


How AI and Machine Learning Are Helping In Cybersecurity?

#artificialintelligence

It acts as a protective layer to prevent online frauds and data breaches from happening. What are Artificial Intelligence (AI) and Machine Learning?


Relief is coming for your security team: 6 ways AI is a game-changer

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Artificial intelligence (AI) and machine learning (ML) give security teams the ability to catch bad guys with the power of math. Through the use of effective analytical methods, organizations can become more cyber resilient. With statistical learning; supervised, semi-supervised, and unsupervised ML; advanced visualizations; and other principled approaches tailored for cybersecurity, you will be one step ahead of the game. Here are six ways AI and ML, along with analytics, can boost your company's cyber resilience. AI and ML can remove friction in managing identities through adaptive authentication, which dynamically escalates the factors needed to verify an identity based on risk.


Using AI in Cybersecurity

#artificialintelligence

When it comes to integrating AI-based processes into security, it isn't just useful; it's become essential and is rapidly becoming mission critical to organizations of all sizes. The rapidly expanding attack surface of virtually every enterprise, with the proliferation of Internet of Things (IoT) devices and cloud systems is a leading reason why artificial intelligence (AI) has become essential in security. Organizations have moved from securing thousands of devices to potentially millions. Within this new surge in network traffic are billions of time-varying signals, all of which must be analyzed to assess risk. Security is becoming incredibly more complex in just a handful of years because there is far more to attack.


Artificial Intelligence And Cybersecurity - AI Summary

#artificialintelligence

Think of artificial intelligence (AI), deep learning (DL) and machine learning (ML) as the layers of an onion. Think about machine learning as a part of AI, but AI does not always utilize machine learning methods. Machine learning refers to an algorithm that can create abstractions (models) by training on a dataset and is a method of training an algorithm to accomplish a task. Rapid advances in big data, data analytics, and machine learning are used to convert millions of scattered data points into databases for use in various cybersecurity arenas, such as threat intelligence analysis. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes. Therefore, businesses seeking to leverage machine learning enabled technology need to threat model and perform risk assessments when creating machine learning systems for cybersecurity purposes.


Blackjack: A game model for applying AI to cybersecurity

#artificialintelligence

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.


Blackjack: A game model for applying AI to cybersecurity

#artificialintelligence

Cyber-attacks continue to threaten organizations large and small. The impacts of a data breach or ransomware attack may have significant and material impacts on both customers and shareholders. To help combat cyber threats, some organizations have started exploring how big data and artificial intelligence (AI) may help to reduce cybersecurity risk. Machine learning algorithms are now common in cybersecurity. We find machine learning offered in more commercial products, from those that are fully integrated into products and require no knowledge of machine learning to those that require rolling up your sleeves to put together the algorithms and perform statistical analysis. Machine learning for cybersecurity has most frequently been applied to detecting patterns that represent attacks. This includes algorithms that evaluate audit log data, that spot anomalies for network intrusion detection systems, and that identify and block malware on computer systems. In some applications, machine learning is used to train models of normal activity on networks in hope of later detecting anomalous events that may represent a cyber-attack.


ENISA AI Threat Landscape Report Unveils Major Cybersecurity Challenges

#artificialintelligence

Today, the European Union Agency for Cybersecurity (ENISA) released its Artificial Intelligence Threat Landscape Report, unveiling the major cybersecurity challenges facing the AI ecosystem. ENISA's study takes a methodological approach at mapping the key players and threats in AI. The report follows up the priorities defined in the European Commission's 2020 AI White Paper. The ENISA Ad-Hoc Working Group on Artificial Intelligence Cybersecurity, with members from EU Institutions, academia and industry, provided input and supported the drafting of this report. The benefits of this emerging technology are significant, but so are the concerns, such as potential new avenues of manipulation and attack methods.


Panasonic, McAfee team up to tackle vehicle cybersecurity

ZDNet

Panasonic and McAfee are joining forces to establish a vehicle security operations center (SOC) to tackle the ongoing threat of cyberattacks. Announced on Tuesday, the new partnership involves both companies jointly creating an SOC to "commercialize vehicle security monitoring services," with a specific focus on early detection and response. Smart and intelligent vehicle features, now becoming more common in new models, require connectivity. This is usually established through Bluetooth and internet connections, which -- unless properly protected -- can also give attackers a chance to establish a foothold into a vehicle's system. In addition, software vulnerabilities can also be exploited to tamper with a car's functionality.