(www.MaritimeCyprus.com) Developments in connectivity and the transfer of data in greater volumes between ship and shore continue to bring significant gains for fleet management efficiency and crew welfare, but they also increase the vulnerability of critical systems onboard vessels to cyber attacks. A 2019 recorded 58% of respondents to a survey of stakeholders as confirming that cybersecurity guidelines had been incorporated into their company or fleet by 2018. The increase over the 37% giving this answer in 2017 explained a sharp drop in the number of maritime companies reporting themselves as victims of cyber-attacks according to authors – 22% compared to 34%. However, the enduring feature of cyber threats is their ability to adapt and evolve, with new lines of attack developed as barriers are put in place, and strategies to expose vulnerabilities constantly emerging. A June 2020 White Paper from the British Ports Association and cyber risk management specialists Astaara suggests that reliance on remote working during the COVID-19 crisis coincided with a fourfold increase in maritime cyber attacks from February onwards, for example. In fact, cybersecurity was ranked as the second-highest risk for shipping in 2019, behind natural disasters, according to a survey of over 2,500 risk managers conducted by Allianz. Given that, according to IBM, companies take on average about 197 days to identify and 69 days to contain a cyber breach, it is clear that an attack on a vessel’s critical systems could threaten the safety of a ship as well as the business of shipping. The fact that a 2019 Data Breach Investigations Report from Verizon indicates that nearly one-third of all data breaches...
Introducing: Machine Learning in R Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don’t need to interfere any more. This small tutorial is meant to introduce you to the basics of machine learning in R: more specifically, it will show you how to use R to work with the well-known machine learning algorithm called “KNN” or k-nearest neighbors. If you’re interested in following a course, consider checking out our Introduction to Machine Learning with R or DataCamp’s Unsupervised Learning in R course! Using R For k-Nearest Neighbors (KNN) The KNN or k-nearest neighbors algorithm is one of the simplest machine learning algorithms and is an example of instance-based learning, where new data are classified based on stored, labeled instances. More specifically, the distance between the stored data and the new instance is calculated by means of some kind of a similarity measure. This similarity measure is typically expressed by a distance measure such as the Euclidean distance, cosine similarity or the Manhattan distance. In other words, the similarity to the data that was already in the system is calculated for any new data point that you input into the system. Then,...
Security is a vastly complex subject and only the largest organisations can spend a fortune to stop threats in the digital age. So with the world moving online, every CIO has to face a perplexing question – ‘how does one manage risk?’ Risk management is the process of identifying, assessing, and responding to risk. Companies invest in application security, network security, database security, and endpoint security. Each of these security attributes operates in silos and require large IT teams to manage them. Three-year-old Bengaluru-based startup Seconize uses risk management processes to enable organisations to prioritise decisions regarding cybersecurity. Seconize offers a SaaS product – DeRisk Centre – that looks at all the asset types of the company and provides a unified view of the security. The startup was founded by Chethan Anand and Sashank Dara in 2017. The early days Chethan has 23 years of experience in the industry and has an MS (University of Illinois Urbana-Champaign) as well as an executive MBA from IIM-B. Sashank has 18 years of experience and a PhD from IIIT-B. They both were working for Cisco when they met. Chethan was a Senior Product Manager, creating products and taking them to the market, and Sashank was a Technical Leader in the security group. While at Cisco, both began interacting to discuss security-related aspects of some of the products that Chethan was working on. “During our conversations, we realised that companies were getting impacted despite the increase in security spending. These cyber-attacks were getting automated and sophisticated, so companies had to address the problem in a slightly different way, instead of trying to address...
Data Science and Marketing Analytics: The Formula for Better Marketing Data science and marketing analytics play a critical role in understanding your audience and what motivates them to buy so you can develop and execute successful marketing strategies. These technologies enable marketing professionals to gain powerful insights into buyer behavior by leveraging big data. According to Deloitte, “Data science and analytics are driving big shifts in marketing. In fact, the possibilities are unfolding so quickly that new applications for data science-led marketing are emerging nearly as fast as marketers can imagine them.” Marketing analytics is nothing new. How we go about getting the data for those analytics, however, has dramatically changed over the years. In the past, marketers looked at basic sales data and rudimentary data to create their own customer profiles and marketing strategies. They analyzed data mostly manually, inferring all kinds of assumptions based on the best, albeit limited, data they had. Data science wasn’t even a thing. My, how things have changed. Today, we have web analytics, predictive analytics, artificial intelligence and machine learning to not only access big data but crunch the data for us in ways we couldn’t have dreamed of just a decade ago. These technologies help marketers save time and money while building more successful marketing campaigns that reach the right people at the right time on the right channels. Data science and marketing analytics are powerful marketing tools. They used to be a differentiator but have now become table stakes. Marketers who aren’t leveraging these technologies are not only at a major disadvantage, they are likely in danger of becoming completely...
A Dalby farmer who has been scammed to the tune of $90,000 is warning others to be on the lookout for similar activity. Noel and Suzi Rockliff thought nothing of it when they received their usual email from Arrow Energy at the end of July reminding them to make their quarterly payment for land they lease from the company, together with details of changes to the bank account money was to be credited into. They only realised they’d been involved in a fraudulent transaction when they received communication from Arrow a week later asking why they hadn’t met their payment commitment. The fake cover letter accompanying the email received by the Rockliffs, which appeared similar to advice from other companies they deal with. “We receive similar messages regularly from other businesses and didn’t think to check,” Mr Rockliff said. “It was identical to the usual message we receive and didn’t ring any alarm bells.” They immediately contacted their bank and the police, who confirmed that the money hadn’t gone to Arrow Energy. “The police told me when they looked into it, that account had cleared a couple of hundred thousand dollars,” Mr Rockliff said. The matter is currently under investigation by Dalby CIB, who were contacted for comment on the case. “We have had our computer checked by an IT specialist who confirmed it was highly unlikely that our machine had been compromised,” Mr Rockliff said. The real invoice received by the Rockliffs is almost identical to the fake one on the right. The family met with Arrow Energy five weeks ago to negotiate a way forward and said...
Credit: CC0 Public Domain A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. The model, developed by lead researcher Guowei Wei, professor in the departments of Mathematics and Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein—a protein primarily responsible for facilitating infection—and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients. Some mutations even reduce infectiousness. But some mutations lead to a more infectious virus. Wei and his team have studied and analyzed mutation patterns and locations for months, tracking changes against the official viral genome sample captured in January. “Knowledge about the infectivity of SARS-CoV-2 is a vital factor for preventive measurements against COVID-19 and reopening the global economy,” Wei said. “A crucial question is what are the ramifications of these mutations to COVID-19 transmission, diagnostics, prevention and treatment.” Viral infection occurs when the spike protein interacts with a human host cell receptor called angiotensin-converting enzyme 2—ACE2 for short. As it relates to ACE2, scientists are concerned about a concept known as binding affinity, or the strength of the binding interaction between the spike protein and host receptor during the initial stage of infection. “Viral infectivity increases if the binding affinity strengthens,” Wei said. “Currently, more than 50 mutations have been found along with the binding interface on the spike proteins receptor-binding domain—RBD for short—which has 194...
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