NITDA: Fixing Cyber Security with Sound PR Strategy By Kabir Abdulsalam No doubt, cybercrime is increasing each day and evolving to become more complex and more formidable. As it is possible for government agencies like the Central Bank of Nigeria (CBN), Joint Admissions and Matriculation Board (JAMB), Nigerian Army Education Corps (NAEC), Nigeria Police Force, and others to have their websites hacked, private organizations are equally vulnerable. For instance, the registrar of JAMB, Professor Ishaq Oloyede, while speaking to journalists had disclosed that hackers had stolen over N10 million after gaining access to its site and altering the profiles of its ad hoc staff. In 2011, cyberpunks also attacked two Nigerian government websites, Niger Delta Development Commission (NDDC) and National Agency for Poverty Alleviation to protest against the $6 million budget for the inauguration ceremony of President Goodluck Jonathan. Again, during ENDSARS protests in 2020, Anonymous, a popular decentralized international ‘hacktivist’ group took the side of Nigerian protesters by organizing several attacks against government accounts and handles. As Nigerians become cyber-creatures, spending more time online, exploring more digital contents, so does cybercrime affects a huge number of organisations and individuals. Recently, the Director-General, National Information Technology Development Agency (NITDA), Kashifu Inuwa Abdullahi, announced its ambitious target to achieve 95 percent digital literacy by 2030. Kashifu, at a digital journalism workshop organised for journalists in Kano, noted that the government had put structures in place to protect the ecosystem under its strategic roadmap and action plans on cybersecurity. He said: “Cybersecurity is one of the strategic pillars under our strategic roadmap and action plan which is more about social engineering. So,...
Let the OSS Enterprise newsletter guide your open source journey! Sign up here. OctoML, a platform that helps enterprises optimize and deploy machine learning (ML) models, has raised $85 million in a series C round of funding. While countless companies are dabbling with ways to leverage AI to improve their businesses and bottom line, transitioning AI projects beyond the pilot stage and into real-world production scenarios comes is no easy feat. Indeed, in its State of AI in 2020 report, McKinsey found that just 16% of respondents from across industries had taken their deep learning beyond the pilot stage — and this, ultimately, is what OctoML is all about. From pilot to production Founded out of Seattle in 2019, OctoML helps companies deploy ML models through to production environments. It does this by automatically tailoring models to suit the target hardware platform, cloud provider, or edge device, with no manual rewriting or re-architecting required — in other words, it saves a significant amount of time and resources. The company has cemented official partnerships with major hardware firms such as AMD, Arm, and Qualcomm. OctoML is built on the open source Apache TVM, which is a machine learning compiler framework for central processing units (CPUs), graphics processing units (GPUs), and machine learning accelerators — it enables ML engineers to run more efficient computations on any hardware. Perhaps most notably, OctoML was founded by the Apache TVM creators, which includes CEO Luis Ceze. “The ever-growing ecosystem of ML hardware backends and diverse models are generating an insurmountable amount of manual work to optimize and fine-tune models before deployment,” Ceze noted in a press release. “This is resulting...
Mobile phones have become an indispensable part of our daily life. We use mobile phones to communicate with our loved ones, for quick access to information through the Internet, to make transactions through mobile banking apps or to relax reading a good book. In a way, a big part of our private life has moved into the digital environment. Mobile phones seem to be a pocket-sized treasure of secrets and information, hiding our most valuable photos, mails, contacts and even banking information. There’s no wonder why we need mobile phones to have bullet-proof security. Android is the most common operating system for mobile devices and is particularly interesting from the security point of view. It is very permissive, allowing its users to customize about anything, administrative privileges (a.k.a. rooting) can be unlocked on most phones, it has a very fuzzy system for the permissions required by applications and it features different ways for one application to interact with other applications. In this blog post, we are going to focus on how Android apps can interact with each other and how the security of those interactions can be tested. How apps can interact with each other The main methods for inter-app communications are: As you can see, there are a many interactions. Testing in a old-fashioned way (by creating an app for every test you have in mind, installing it on your device and running the test-app) is very time consuming, so it’s not really a solution. Here is where Drozer comes into play. Introducing Drozer Drozer is an ambitious project developed by MWR InfoSecurity. Although it is a Swiss army knife kind of tool,...
To kick off the new year we’re sharing thoughts and predictions on what may shape the global cyber security landscape in 2022. We’ve interviewed industry leaders from our four key geographies – the UK, the Asia and-Pacific (APAC) region, North America and Europe – to understand the key developments in each region over the last twelve months, and what we might expect from the year ahead and beyond. Three key themes were evident across all four regions… 1. Governments are taking a far more interventionist approach to regulating and legislating cyber security and resilience There appears to be broad consensus amongst policymakers that the free market approach to delivering secure, resilient infrastructure in the digital age has – thus far – failed. As such, our interviewees are seeing the governments in their respective regions becoming more interventionist, introducing an increasing number of seemingly stringent regulations that organisations need to abide by. In some cases, there has also been a shift from outcomes-based regulation to more prescriptive regulation, potentially indicating regulated entities’ lack of maturity in understanding what they need to do to achieve the required outcomes. These trends are particularly evident when it comes to securing critical infrastructure, with the EU expanding what constitutes critical infrastructure, the UK introducing flagship legislation on telecoms security, and governments in the the Asia and Pacific region taking steps to better protect their essential infrastructure. Meanwhile, in, and from, the US, the Biden administration is leading the global charge to improve supply chain security. One sector that may go against the grain, according to Global Head of Research Jennifer Fernick, is decentralised finance...
In this article, you will learn the basics of doing Machine Learning on sound data. Sound data (often called audio data) is a data type that is not very intuitive to work with. At the end of this article, you will understand: Sound data The big difficulty when starting with sound data is that, unlike tabular data or images, sound data is not easy to represent in a tabular format. As you may or may not know, images can be easily represented as matrices, because they are based on pixels. Each of the pixels has a value that indicates the intensity of the black and white (or for color images you have an intensity of red, green, and blue separately). Sound, on the other hand, is a much more complex data format to work with: An Example Sound Data Set for Genre Classification Before going into depth, let’s first introduce an example data set. As we are working with songs, it is difficult to obtain a dataset of which we can be certain that there are no copyright violation problems. For this article, I have therefore decided to create a small database myself that uses only beats from a website for copyright-free music called Uppbeat: I do not want to re-distribute the songs, but if you want to follow along, you can easily create a free account and download a number of songs from the two categories to follow along. How to organize your data and environment for working with sound For following along in your own environment, I advise you to create a folder called tracks, in which...
The Indian Institute of Technology (IIT) Guwahati will start Post Graduate Certificate Programme in Cyber security and Post Graduate Certificate Programme in Artificial Intelligence & Deep Learning. The premier higher educational institute has signed an agreement with Times Professional Learning (TPL) to start the programmes. The programmes are intended to provide career enhancement and skill up-gradation for working professionals, said a statement. The courses are also aligned with the National Education Policy 2020’s directives to Higher Education Institutes to play an active role not only in conducting research on disruptive technologies such as AI but also in creating initial versions of instructional materials and courses including online courses in cutting-edge domains. The PG Certificate Programme in Cyber security is an eight months course. The course is intended to provide career enhancement and skill up-gradation as per the requirements of fast-moving job roles like network security specialist, cyber security analyst, cyber security architects, cyber security manager, and leading up to C-suite positions such as Chief Information Security Officer. The PG Certificate Programme in Artificial Intelligence & Deep Learning is a nine-month course. It will provide candidates detailed understanding of AI and Deep Learning through a comprehensive curriculum. The programme focuses on fundamental and advanced learning through subjects like Python programming, data analytics, neural networks, computer vision, and image recognition, etc. It will help candidates prepare for competitive and cutting-edge job roles such as AI and ML Engineer, Computer Vision Expert, Software R&D Engineer, Cloud Support Engineer, among others, that involve a high level of technical skill and training. “AI-based technology has made rapid inroads through its seamless mechanisms and improved...
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