While IoT adoption continues to grow, the standards, compliance requirements and secure coding practices surrounding IoT have not advanced at the same rate. Recent high profile software supply chain attacks have brought the issue of secure coding into sharp focus, prompting the Biden administration to issue an executive order addressing new requirements for federal agencies to only purchase and deploy secure software. This pivotal shift will have an immediate impact on global software development processes and lifecycles, especially when you consider the vast reach of U.S. federal procurement. Virtually all device manufacturers and software companies will be impacted directly as the administration begins to increase obligations on the private sector and establish new security standards across the industry. Specific to IoT, the order directs the federal government to initiate pilot programs to educate the public of the security capabilities of IoT devices, and to identify IoT cybersecurity criteria and secure software development practices for a consumer-labeling program.
The real problem is mixing two languages in one body of code. The dbUtil handle is just a boilerplate reduction device here. The raw SQL is still there. We still can’t test the complex individual statements separate from the simple yet crucial control logic captured in the if-statements, which depend solely on the state of the person object, not on the database. Sure, we can test this control logic fine if we mock out the calls to the database. The mock for dbUtil returns a prepared list of person objects, and we can verify the correct invocation of it for the two different conditions. That unavoidably leaves the SQL untested. If we want to test the execution of these statements, we need to run the entire code inside the for loop, this time using a real database. That test needs to set up the conditions for all the three execution paths (condition 1, 1 and 2, or none), as well as verify what happened to the state after executing the void statement executions. It can be done, but we are of necessity testing both the Java and SQL realms here. That’s hardly the lean unit testing we’re looking for.
Ansible is an open-source automation engine that helps in DevOps and comes to the rescue to improve your technological environment’s scalability, consistency, and reliability. It is mainly used for rigorous IT tasks such as configuration management, application deployment, intraservice orchestration, and provisioning. In recent times, Ansible has become the top choice for software automation in many organizations. Automation is one of the most crucial aspects of industries these days. Unfortunately, many IT environments are too complex and often require to be scaled too quickly for system administrators and developers to keep up, rather than manually. ... Docker is an open-source platform application for developing, shipping, and running applications. It enables developers to package applications into containers, a set of standardized and executable components that combine the application source code with the operating system libraries and dependencies required to run that code in an executable environment. Containers can even be created without Docker, but the platform and user interface make it easier, simpler, and safer to build, deploy and manage containers.
The basic goal of delegation of authority is to enable efficient organization. Just as no single individual in a company can do all of the tasks required to achieve a group's goals, it becomes arduous for the management to wield all decision-making authority as a business expands. This is because there is a limit to the number of people a manager can successfully monitor and make decisions. When this threshold is reached, the authority must be handed to subordinates. While centralization was still a possibility before the pandemic, this was no longer the case after back-to-back lockdowns and economic slowdowns. In such a situation, the delegation came as a boon that not only kept the workflow active but also helped in scaling the growth. ... Delegating gives your team greater confidence, makes them feel important, and allows them to demonstrate their abilities. This will result in mutual appreciation with colleagues motivating one another to work more, and staying devoted to attaining the goals.
If companies do not make changes to their IT operations in response to a migration, finding savings can be more difficult, L’Horset says. “In the industry, there’s a lot of debate: Is cloud saving you money or not? Our research indicates that even at the basic level, yes it does,” he says. “The difference between the cost-savings, which you can get through cloud, and the value of innovation that you absolutely can and should get through cloud, is the fundamental reason you should go.” Roy Illsley, chief analyst with Omdia, the research arm of Informa Tech, says the cost benefits of cloud can be positive if the workload is variable in its resource requirements, its resource requirements match the cloud providers packaging of resources, or it requires high availability. "If the workload is stable in its resource requirements then on-premises is more cost effective," he says. Respondent companies to the Accenture survey that did not list cloud as a top priority still saw significant cost-savings, says Jim Wilson, managing director of information technology and business research at Accenture Research.
AI/MI is used in network traffic analysis, intrusion detection systems, intrusion prevention systems, secure access service edge, user and entity behavior analytics, and most technology domains described in Gartner's Impact Radar for Security. In fact, it's hard to imagine a modern security tool without some kind of AI/ML magic in it. ... Through social engineering and other techniques, ML is used for better victim profiling, and cybercriminals leverage this information to accelerate attacks. For example, in 2018, WordPress websites experienced massive ML-based botnet infections that granted hackers access to users' personal information; ... Ransomware is experiencing an unfortunate renaissance. Examples of criminal success stories are numerous; one of the nastiest incidents led to Colonial Pipeline's six-day shutdown and $4.4 million ransom payment; ... ML algorithms can create fake messages that look like real ones and aim to steal user credentials. In a Black Hat presentation, John Seymour and Philip Tully detailed how an ML algorithm produced viral tweets with fake phishing links that were four times more effective than a human-created phishing message.
First, let’s look at the importance of content to a business. In simple terms, content is the inherent value of a company. It’s NASA’s designs for their new space station, AstraZeneca’s highly regulated pharmaceutical patents, and Oxfam’s humanitarian aid records. It’s the clinical trial results for the next breakthrough vaccine, or the blueprint for the innovative new approach to flooding solutions. Content is the entire work of an organisation and is completely unique for every company. Content is the database of its most valuable insights. But to effectively realise this value, organisations need to find a single place for their content. Separating content between different silos and applications creates friction, which can stand in the way of employees accessing and sharing information, inhibiting innovation and productivity. Applications in today’s content-driven world are often judged by their ease of integration with other technologies. As a result, businesses are turning to single platforms where content can be securely stored and managed, while all compliance requirements are met and all teams have the opportunity to collaborate on the content, both internally and externally.
An IMSI catcher is equipment designed to mimic a real cell tower so that a targeted smartphone will connect to it instead of the real cell network. Various techniques may be employed to do it, such as masquerading as a neighboring cell tower or jamming the competing 5G/4G/3G frequencies with white noise. After capturing the targeted smartphone’s IMSI (the ID number linked to its SIM card), the IMSI catcher situates itself between the phone and its cellular network. From there, the IMSI catcher can be used to track the user’s location, extract certain types of data from the phone, and in some cases even deliver spyware to the device. Unfortunately, there’s no surefire way for the average smartphone user to notice/know that they’re connected to a fake cell tower, though there may be some clues: perhaps a noticeably slower connection or a change in band in the phone’s status bar (from LTE to 2G, for example). Thankfully, 5G in standalone mode promises to make IMSI catchers obsolete, since the Subscription Permanent Identifier (SUPI) – 5G’s IMSI equivalent – is never disclosed in the handshake between smartphone and cell tower.
Some companies with data scientists in place have difficulty operationalising their skills. If we look at the volumes of data processed by organisations, the different structures and architectures, it is not imperative to have a data scientist in its ranks of data experts. For companies managing an astronomical amount of data, on multiple channels and with a complex structure, the expertise of a data scientist will prove beneficial in modeling data, query it and make predictions. One of the first questions to ask is therefore related to data and business needs and to organise the structure according to an organisation’s structure and its data strategy. Companies have also realised that having a data scientist was not the answer to their data value problems. This is partly due to a lack of understanding in the environment surrounding data. A data scientist may understand the data, but not its purposes and environments or business applications. Let’s take the example of a marketing department working on implementing AI to accelerate its web ROI.
Interpretability of deep learning models is essential for widespread adoption of these techniques in the Medical image diagnosis community. Deep learning models have been phenomenally successful at beating state of the art in common medical image diagnosis tasks like segmentation and screening applications, e.g. classification of diabetic retinopathy and chest X-ray scans, among others. While these successes have created huge interest in adopting these techniques in clinical practice, a huge barrier in adoption is the lack of interpretability of these models. Convolutional Neural Networks with hundreds of layers is the workhorse for medical image diagnosis. While the initial layers are typically edge detectors and shape detectors, it is fairly impossible to explain or interpret the feature maps as one goes deeper into the network. In order for clinicians to trust the output from these networks, it is essential that a mechanism for explaining the output be present. In addition, black-box techniques will make it hard for clinicians to justify the diagnosis and follow up procedures.
Quote for the day:
"Honor bespeaks worth. Confidence begets trust. Service brings satisfaction. Cooperation proves the quality of leadership." -- James Cash Penney