information security


A Machine-Learning Approach to Phishing Detection and Defense: Iraj Sadegh Amiri, O.A. Akanbi, E. Fazeldehkordi: 9780128029275: Amazon.com: Books

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Dr. Iraj Sadegh Amiri received his B. Sc (Applied Physics) from Public University of Urmia, Iran in 2001 and a gold medalist M. Sc. in optics from University Technology Malaysia (UTM), in 2009. He was awarded a PhD degree in photonics in Jan 2014. He has published well over 350 academic publications since the 2012s in optical soliton communications, laser physics, photonics, optics and nanotechnology engineering. Currently he is a senior lecturer in University of Malaysia (UM), Kuala Lumpur, Malaysia. O.A. Akanbi received his B. Sc. (Hons, Information Technology - Software Engineering) from Kuala Lumpur Metropolitan University, Malaysia, M. Sc. in Information Security from University Teknologi Malaysia (UTM), and he is presently a graduate student in Computer Science at Texas Tech University His area of research is in CyberSecurity.


Face-reading AI will be able to detect your politics and IQ, professor says

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Michal Kosinski – the Stanford University professor who went viral last week for research suggesting that artificial intelligence (AI) can detect whether people are gay or straight based on photos – said sexual orientation was just one of many characteristics that algorithms would be able to predict through facial recognition. Kosinski, an assistant professor of organizational behavior, said he was studying links between facial features and political preferences, with preliminary results showing that AI is effective at guessing people's ideologies based on their faces. That means political leanings are possibly linked to genetics or developmental factors, which could result in detectable facial differences. Facial recognition may also be used to make inferences about IQ, said Kosinski, suggesting a future in which schools could use the results of facial scans when considering prospective students.


Espoo and Tieto testing artificial intelligence to identify service pathways

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The City of Espoo has initiated a unique artificial intelligence experiment in collaboration with Tieto, a Finnish software and service company. The experiment involves combining a huge amount of social and health data concerning every Espoo resident, and customer data relating to early childhood education from 2002 to 2016. This will help the city to provide more individualized services, thereby preventing problems such as social exclusion more cost-effectively. The City of Espoo has been a customer of Tieto's for several years as the company provides information system services for Espoo's health care, social and early childhood education services.


AI or not, machine learning in cybersecurity advances

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As companies promote AI and advanced machine learning in cybersecurity, CISOs need to ask some tough questions to get past the hype: Are these technologies bolted on to get investments as well as customers, or are they core to an innovative security platform that solves a business problem (too many alerts to efficiently monitor)? Is the company's expertise in machine learning and AI or information security? Advances in machine learning and security can help in areas such as antimalware, dynamic risk analysis and anomaly detection, found Robert Lemos, who reports on machine learning in cybersecurity in this month's cover story. The technology is really good at "crunching through data," Joseph Blankenship, senior analyst for security and risk at Forrester Research, tells Lemos.


Tieto and Espoo test artificial intelligence to boost value-based health and social care

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All data processing includes extreme measures regarding information security, and personal data, such as names, identity numbers and addresses are concealed already during data collection. This will help the city to provide more individualized services, thereby preventing problems such as social exclusion more cost-effectively. Through Tieto's artificial intelligence engine, it is possible to develop more personalized citizen services within social and healthcare area in Espoo. Tieto is developing artificial intelligent platform to provide exponential value for its customers in both public and private sectors.


Artificial Intelligence in Healthcare: Major Opportunities and Challenges

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When a patient enters a physician's office, the patient must inform the staff about their historical medical information and simple data points, such as smoking status and age. Federal mandates have pushed the hospital industry to adopt electronic health record (EHR)systems. With so many patients storing their personal information in electronic systems, data security is of utmost importance. The biggest challenges are user training and physician burnout Training users on new technology is an expensive process.


The 7 hottest jobs in IT

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The solution to this problem is either to find approaches that help us to generate data, or building more robust machine learning models which can learn from limited data. Aymen Sayed, chief product officer for CA Technologies, points out that while AR and VR tech made a splash with a range of consumer products shown at this year's CES, more promising opportunities will occur this year in the enterprise for simulation and training, which should mean more roles for AR and VR developers -- both in development and security. In fact, Gartner predicts that by 2020, augmented reality, virtual reality, and mixed reality immersive solutions will be a part of 20 percent of enterprise's digital transformation strategy." Alana Hall, corporate recruiter at Conga, says a number of cloud-related roles are the toughest to fill this year, including "cloud architects and developers, cloud infrastructure devops roles, hybrid cloud architects and developers."


Why Big Data, Machine Learning Are Critical to Security

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Big data and machine learning will play increasingly critical roles in improving information security, predicts Will Cappelli, a vice president of research at Gartner. "In terms of market size, Gartner estimates that in 2016 the world spent approximately $800 million on the application of big data and machine learning technologies to security use cases," he says in an interview with Information Security Media Group. A typical use case would be to deploy a big data log management platform and then deploy some kind of machine learning capability on top of that platform to enable the automated discovery of hidden patterns in this data that indicate, for example, unauthorized access, he says. Cappelli is a Gartner Research vice president in the enterprise management area, focusing on the application of big data and machine learning technologies to IT operations as well as application performance monitoring.


Can artificial intelligence help thwart ransomware?

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Last week, the WannaCry ransomware attack crippled their network -- one report suggested people with life-threatening injuries were told not to come to the hospital. In the future, security systems could use artificial intelligence to monitor user behavior, track activity, suggest when there may be a danger and even mount an attack against the ransomware purveyors, effectively rendering the deadly malware client inoperable. Raja Mukerji, the cofounder and Chief Customer Officer at ExtraHop Networks, equates how an AI can block ransomware to how airport security stops people from using water bottles. A new technique using AI in airport security would not block all water bottles.


HowTo: Create A Rogue A.I. (For Dummies)

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Back to the game of Jeopardy, and given the nature of the topic of this blog, you may have guessed that we are talking about the game played between the world's two best human players, and the artificial intelligence created by IBM, called Watson. is something that is not self-aware, no singularity, but a raw decision making machine, that uses information freely found on the internet to determine the consesus of humans en masse to determine what is right and wrong, or form "preference over world states." What I am trying to make clear is that, if a dumb decision making machine is out there trying to interpret human thoughts spread across the internet, how can we be sure it can understand that an article about A.I. There are many examples to give here, but I would like to focus on one of the more clear-cut examples that illustrate exactly not only the fallibility inherent in programmers writing code and creating software, but also the openness of the hardware architecture that our modern machines used, whether they sit on your desk, or are "super."