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Top 20 Predictions Of How AI Is Going To Improve Cybersecurity In 2021

#artificialintelligence

Gartner's latest Information Security and Risk Management forecast predicts the market will achieve ... [ ] an 8.3% Compound Annual Growth Rate (CAGR) growth rate from 2019 through 2024, reaching $211.4 billion. Bottom Line: In 2021, cybersecurity vendors will accelerate AI and machine learning app development to combine human and machine insights so they can out-innovate attackers intent on escalating an AI-based arms race. Attackers and cybercriminals capitalized on the chaotic year by attempting to breach a record number of enterprise systems in e-commerce, financial services, healthcare and many other industries. AI and machine learning-based cybersecurity apps and platforms combined with human expertise and insights make it more challenging for attackers to succeed in their efforts. Accustomed to endpoint security systems that rely on passwords alone, admin accounts that don't have fundamental security in place, including Multi-Factor Authentication (MFA) and more and attackers created a digital pandemic this year. Interested in what the leading cybersecurity experts are thinking will happen in 2021, I contacted twenty of them who are actively researching how AI can improve cybersecurity next year. Leading experts in the field include including Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, BJ Jenkins, President and CEO of Barracuda Networks, Ali Siddiqui, Chief Product Officer and Ram Chakravarti, Chief Technology Officer, both from BMC, Dr. Torsten George, Cybersecurity Evangelist at Centrify, Tej Redkar, Chief Product Officer at LogicMonitor, Bill Harrod, Vice President of Public Sector at Ivanti, Dr. Mike Lloyd, CTO at RedSeal and many others.


Insider Threat Mitigation: The Role of AI and ML

#artificialintelligence

It needs no telling how damaging insider threats can be. Amongst its numerous impacts, the most significant involve the loss of critical data and operational disruption, according to statistics from the Bitglass 2020 Insider Threat Report. Insider threats can also damage a company's reputation and make it lose its competitive edge. Insider threat mitigation is difficult because the actors are trusted agents, who often have legitimate access to company data. As most legacy tools have failed us, many cybersecurity experts agree that it is time to move on.


Top 20 Predictions Of How AI Is Going To Improve Cybersecurity In 2021

#artificialintelligence

Gartner's latest Information Security and Risk Management forecast predicts the market will achieve ... [ ] an 8.3% Compound Annual Growth Rate (CAGR) growth rate from 2019 through 2024, reaching $211.4 billion. Bottom Line: In 2021, cybersecurity vendors will accelerate AI and machine learning app development to combine human and machine insights so they can out-innovate attackers intent on escalating an AI-based arms race. Attackers and cybercriminals capitalized on the chaotic year by attempting to breach a record number of enterprise systems in e-commerce, financial services, healthcare and many other industries. AI and machine learning-based cybersecurity apps and platforms combined with human expertise and insights make it more challenging for attackers to succeed in their efforts. Accustomed to endpoint security systems that rely on passwords alone, admin accounts that don't have fundamental security in place, including Multi-Factor Authentication (MFA) and more and attackers created a digital pandemic this year. Interested in what the leading cybersecurity experts are thinking will happen in 2021, I contacted twenty of them who are actively researching how AI can improve cybersecurity next year. Leading experts in the field include including Nicko van Someren, Ph.D. and Chief Technology Officer at Absolute Software, BJ Jenkins, President and CEO of Barracuda Networks, Ali Siddiqui, Chief Product Officer and Ram Chakravarti, Chief Technology Officer, both from BMC, Dr. Torsten George, Cybersecurity Evangelist at Centrify, Tej Redkar, Chief Product Officer at LogicMonitor, Brian Foster, Senior Vice President Product Management at MobileIron, Dr. Mike Lloyd, CTO at RedSeal and many others.


Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing

arXiv.org Artificial Intelligence

Key components of current cybersecurity methods are the Intrusion Detection Systems (IDSs) were different techniques and architectures are applied to detect intrusions. IDSs can be based either on cross-checking monitored events with a database of known intrusion experiences, known as signature-based, or on learning the normal behavior of the system and reporting whether some anomalous events occur, named anomaly-based. This work is dedicated to the application to the Internet of Things (IoT) network where edge computing is used to support the IDS implementation. New challenges that arise when deploying an IDS in an edge scenario are identified and remedies are proposed. We focus on anomaly-based IDSs, showing the main techniques that can be leveraged to detect anomalies and we present machine learning techniques and their application in the context of an IDS, describing the expected advantages and disadvantages that a specific technique could cause.


Cybersecurity data science: an overview from machine learning perspective

#artificialintelligence

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.


How Big Data is Aiding Effective Digital Transformation

#artificialintelligence

Data is an important factor for the success of any organization. Undoubtedly, organizations are playing high on big data. The huge amount of data that is generated and collected every day, the potential for a successful enterprise lies beyond impactful insights. As organizations are already invaded by the innovative technologies, the scope of digital transformation gets accelerated. But in order to successfully digitise an organization, implementation of useful data with proper methodology is imperative.


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.


Global Big Data Conference

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Bad actors use machine learning to break passwords more quickly and build malware that knows how to hide, experts warn. Three cybersecurity experts explained how artificial intelligence and machine learning can be used to evade cybersecurity defenses and make breaches faster and more efficient during a NCSA and Nasdaq cybersecurity summit. Kevin Coleman, the executive director of the National Cyber Security Alliance, hosted the conversation as part of Usable Security: Effecting and Measuring Change in Human Behavior on Tuesday, Oct. 6. Elham Tabassi, chief of staff information technology laboratory, National Institute of Standards and Technology, was one of the panelists in the "Artificial Intelligence and Machine Learning for Cybersecurity: The Good, the Bad, and the Ugly" session.text Attackers can use AI to evade detections, to hide where they can't be found, and automatically adapt to counter measures," Tabassi said.


Global Big Data Conference

#artificialintelligence

Even though security solutions are becoming modern and robust, cyber threats are ever-evolving and always on the peak. The main reason for this is because the conventional methods to detect the malware are falling apart. Cybercriminals are regularly coming up with smarter ways to bypass the security programs and infect the network and systems with different kinds of malware. The thing is, currently, most antimalware or antivirus programs use the signature-based detection technique to catch the threats, which is ineffective in detecting the new threats. This is where Artificial Intelligence can come to rescue.


The Importance of Predictive Artificial Intelligence in Cybersecurity

#artificialintelligence

Data security is currently more essential than any other time in recent memory. The present cybersecurity threats are unimaginably smart and advanced. Security experts face an every day fight to identify and assess new dangers, identify possible mitigation measures, and find some solution for the residual risk. This upcoming age of cybersecurity threats requires agile and smart projects that can quickly adjust to new and unexpected attacks. AI and machine learning's ability to address this difficulty is perceived by cybersecurity experts, most of whom trust it is a key to the eventual future of cybersecurity The utilization of AI systems, in the realm of cybersecurity, can have three kinds of impact, it is constantly expressed in the work: «AI can: grow cyber threats (amount); change the run of the mill character of these dangers (quality); and present new and obscure dangers (quantity and quality).