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cyberwarfare


How will AI and Machine Learning (ML) Affect Cyber Security?

#artificialintelligence

The internet is increasingly becoming a part of our lives, growing every second. A new change takes place every day, rendering the prevailing system obsolete. Adjusting to this change is not always easy. The risks associated with the internet are many and affect the security of the users to a great extent. With the advent of Artificial Intelligence and Machine Learning, every process is being automated.


The Incident Response Challenge 2020 -- Results and Solutions Announced

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In April 2020, Cynet launched the world's first Incident Response Challenge to test and reward the skills of Incident Response professionals. The Challenge consisted of 25 incidents, in increasing difficulty, all inspired by real-life scenarios that required participants to go beyond the textbook solution and think outside of the box. Over 2,500 IR professionals competed to be recognized as the top incident responders. Now that the competition is over (however, the challenge website is still open for anyone who wants to practice solving the challenges), Cynet makes the detailed solutions available as a free resource for knowledge and inspiration. Providing the thought process and detailed steps to solve each of the challenges will serve as a training aid and knowledge base for incident responders.


Limitations

#artificialintelligence

AI algorithms namely machine learning and deep learning algorithms are powerful tools. However, they suffer from some limitations which require that human analytics should work with AI tools collaboratively. In this post, we will look at the most important shortcoming of Artificial Intelligence in the cybersecurity domain. Though Benefits are more, AI also comprises limits [4]. Cybercriminals are creative and come up with new ways to conduct cyberattacks.


AI + Automation -- future of cybersecurity. -- Artificial Intelligence +

#artificialintelligence

Artificial Intelligence and Automation should be used in cyber threat detection to increase security, efficiency and help organizations be pro-active, helping them see the threats in advance and keep their infrastructure and data safe. As organizations dwell into smarter and innovative products, they are dependent on critical data which is under constant threat. A breach of critical data can put the organization and its customers at serious risk. A combination of AI and Automation can be leveraged to counter these threats and provide insight into obscure and malicious activity on systems, networks, and infrastructure. In 2017, the average number of breached records by country was 24,089.


AI and Machine Learning Algorithms are Increasingly being Used to Identify Fraudulent Transactions, Cybersecurity Professional Explains

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The retail banking sector has been hit with numerous scams during the past few years. Cybercriminals are now also beginning to increasingly go after much larger corporate accounts by launching sophisticated malware and phishing attacks, according to Beate Zwijnenberg, chief information security officer at ING Group. Zwijnenberg recommends using advanced AI defense systems to identify potentially fraudulent transactions which may not be immediately recognizable by human analysts. Financial institutions across the globe have been spending a lot of money to deal with serious cybersecurity threats. They've been using static, rules-based verification processes to identify suspicious activity.


Is the Future of Cyber Security in the Hands of Artificial Intelligence (AI)? -- 1

#artificialintelligence

Chinese philosophy yin and yang represent how the seemingly opposite poles can complement each other and achieve harmony. In cybersecurity, this ancient philosophy perfectly represents the relationship between supervised and unsupervised machine learning. For example, monitored machine learning processes can be used for detection, while unsupervised machine learning uses clustering. In the case of cybersecurity and data security research and development, monitored machine learning is often implemented in the form of machine learning algorithms. It is not easy to describe Artificial Intelligence (AI). It has no clear definition.


Transforming cybersecurity with AI and ML: View - ET CISO

#artificialintelligence

By Abhay Pendse We are living in a digital age where digital ecosystems form the backbone of our day to day lives. Cyberattacks are increasingly targeting the digital ecosystems. As advances in Artificial Intelligence (AI) and Machine learning (ML) move at breakneck speed, use of AI and ML is expected to grow at heartening pace in cyber defences and will add tremendous intelligence and power to fight against cyberattacks. Cybersecurity attacks keep growing at an alarming speed and keep getting more sophisticated with IoT attacks, data breaches, spam and phishing, crypto jacking, mobile malware and ransomware. Data losses and disruption due to these attacks continue to be significant for businesses and organizations, both in monetary terms and in damage to their reputations.


IBM, AI And The Battle For Cybersecurity

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As Artificial Intelligence (AI) becomes a bigger part of the IT landscape, cybersecurity is becoming an AI battlefield. The latest and most aggressive attacks in cybersecurity are now leveraging AI to evade traditional security defenses and to counter adversarial responses. The cat and mouse game between attacker and defender is moving to a different level where AI is augmenting the human element. The future of cybersecurity will likely be AI versus AI. Attackers can use AI in cybersecurity attacks to evade detection (evasive), hide in many locations without detection (pervasive) and automatically adapt to counter measures (adaptive).IBM Research is using its expertise to help build the tools to defend against attacks of all kinds and protect data privacy. As enterprises experiment with AI services, machine learning models that power AI have become so important that the models themselves are the target of intrusion attacks.


Feds Charge Chinese Hackers With Ripping Off Video Game Loot From 9 Companies

WIRED

For years, a group of Chinese hackers known variously as Barium, Winnti, or APT41 has carried out a unique mix of sophisticated hacking activities that has puzzled the cybersecurity researchers tracking them. At times they appear focused on the usual state-sponsored espionage, believed to be working in the service of the Chinese Ministry of State Security. At other times their attacks looked more like traditional cybercrime. Now a set of federal indictments has called out those intruders by name, and cast their activities in a new light. Five Chinese hackers are accused of a sprawling scheme to break into the networks of hundreds of global companies in a broad range of industries, as well as think tanks, universities, foreign government agencies, and the accounts of Hong Kong government officials and pro-democracy activists.


How AI and Machine Learning Are Redefining Cybersecurity

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What do they all have in common? If you've been paying attention to cybersecurity news this past year, then you already know. All of them have experienced truly staggering data breaches in 2020. Hackers have learned to deploy every technology and trick up their sleeves to steal data, cause disruption, and exploit the billions of people out there just trying to use the internet. Fortunately, the last few years have seen the rise of new technologies that finally give us an effective way of fighting back against hackers.