Security & Privacy


Using the Power of Deep Learning for Cyber Security (Part 2)

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We are in the midst of a deep learning revolution. Unprecedented success is being achieved in designing deep neural network models for building computer vision and Natural Language Processing (NLP) applications. State-of-the-art benchmarks are disrupted and updated on a regular basis in tasks like object detection, language translation, and sentiment analysis. It's a great time to work with deep learning! The application of deep learning in Information Security (InfoSec) is still very much in its nascent stages.


Machine Learning And Data Privacy: Contradiction Or Partnership?

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The battle for future markets and bigger market shares is in full swing. The world's most influential companies are in a steady race to develop better automated systems and, in turn, boost artificial intelligence technology – taking them ahead of their competitors. By 2020, AI is expected to turn over more than 21 billion euros worldwide. However, further development of machine learning and artificial intelligence technologies seems to be blocked by a major obstacle: data privacy. The more data is consumed, the better these computer algorithms can recognize and capture patterns in the data.


Securing Connected Cars against Cybersecurity Risks – Tech Check News

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FREMONT, CA – The rapid growth of automobile technology has enhanced connectivity in modern vehicles. The digital revolution in the automobile industry has made it possible for manufacturers to integrate AI-powered services to improve navigation and functionality of their products. The integration of artificial intelligence (AI) and the internet of things (IoT) has enabled vehicles to communicate with digital devices. However, the integration of complex networks in the connected cars has opened new pathways for hackers, escalating the risk of cyberattacks.


How the EU is Mastering the Challenge of Trustworthy Artificial Intelligence - insideBIGDATA

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From treating chronic diseases and reducing fatality rates in traffic accidents, to fighting climate change and anticipating cybersecurity threats, Artificial Intelligence (AI) is no longer considered a futuristic construct – it is already a reality and is helping humanity solve pressing global challenges. It significantly improves peoples' lives, helps with day-to-day tasks and benefits society and the economy. Nevertheless, AI applications should not only be consistent with the law, but also adhere to ethical principles. The ethical dimension of AI is not a luxury feature or an add-on: it needs to be an integral part of AI development. The European Commission recognises AI as one of the 21st century's most strategic technologies and is therefore increasing its annual investment in AI by 70% as part of the research and innovation programme Horizon 2020, reaching €1.5 billion for the period 2018-2020.


AI – To infinity and beyond? Let's focus on wireless networks and cybersecurity first – Tech Check News

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Artificial Intelligence (AI) is deemed to be one of the biggest technological innovations of this decade. However, like with all innovations, we must focus on fundamental applications first before we quite literally reach for the stars. AI has huge potential for wireless networks and for the people that must protect them, as well as those who try and attack them. So how will AI come into play this year and how will it shape the future? Let's focus on wireless networks and cybersecurity first


Tech Data Partners with Deep Learning Cybersecurity Platform – Tech Check News

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Tech Data on Wednesday said it's partnerubg with Deep Instinct for its end-to-end deep learning framework for cybersecurity. The Deep Instinct platform provides enhanced protection by predicting harmful known and unknown cyberattacks, terminating execution and preventing any possible damage. Tech Data's Alex Ryals "We believe artificial intelligence (AI)-based deep learning tools are the next wave in advanced cyberprotection," said Alex Ryals, vice president of security solutions, Americas, at Tech Data. "Because these tools are autonomous and constantly learning, not only do they require fewer updates, but unlike typical machine […]


Evaluation of Machine Learning Algorithms for Intrusion Detection System

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To gauge the accuracy of machine learning models we use various parameters. The metrics used here will be Average Accuracy, False Positive Rates and False Negative Rates. K-Means is excluded from this metric as it is an unsupervised algorithm. Average Accuracy is defined as the ratio of the correctly classified data points to the total number of data points. False Positives are those cases which were supposed to be returned as threats but aren't. False negatives are just the opposite.


Artificial intelligence in cyber security market is valued at $4.94bn in 2019, according to Visiongain

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Visiongain has launched a new cyber report Artificial Intelligence in Cyber Security Market Report 2019-2029: Forecasts by Component (Hardware, Software, Services), by Deployment Type (On-premise, Cloud, Hybrid), by Security Type (Endpoint Security, Network Security, Application Security, Cloud Security), by Technology (Machine Learning, Natural Language Processing, Context Awareness Computing), by Application (Antivirus/Antimalware, Identity and Access Management (IAM), Risk and Compliance Management, Intrusion Detection/Prevention Systems, Encryption, Unified Threat Management (UTM), Data Loss Prevention (DLP), Others), by Industry (Enterprise, BFSI, Government & Defence, Retail, Healthcare, Manufacturing, Automotive & Transportation, Others), Leading Company Analysis, Regional and Leading National Market Analysis. The increasing number of cyber frauds and malicious attacks is one of the prime growth factors of artificial intelligence in the cyber security market. In addition, the growing adoption of bring your own devices (BYOD) in organisations is also anticipated to drive the growth of artificial intelligence in the cyber security market. With the rising incidences of cyber-crimes, artificial intelligence in the cybersecurity market will gain traction in years to come. Cyber-frauds such as identity and payment card thefts, account for over 55% of all cyber-crimes and may prove costly for organisations, if not resolved quickly.


How AI and Big Data are Revolutionizing Traditional Banking Socialnomics

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Does your bank help you reflect on spending habits and help you make wiser decisions? Did it detect that it wasn't you who made that last purchase? By combining the powers of big data, artificial intelligence, and machine learning, banks can do much more than keep your money and pay you a nominal interest rate. "Banking is necessary, banks are not." That's something Bill Gates said way back in the 90s, but the true weight of that short sentence is being felt today.


Lets' catch up with the new Cybersecurity Judge… - ELE Times

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Today, cyber threats have become one of the major concerns for several industry leaders, as they cause electrical blackouts, breaches of national security secrets as well as the military equipment. They can also result in the theft of valuable and sensitive data like medical records. They are capable of disrupting phone and computer networks or paralyze systems, making the data unavailable. However, such situations can be avoided with the modern technologies like Artificial Intelligence and Machine Learning. AI has been portraying limitless potential in various applications across different industries.