December 28, 2021
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
Your first line of defense is to upgrade to the most current Log4j versions. Initially, Apache released a patch that turned out to still have vulnerabilities. The most current versions are Log4j v.2.17.0, if you are running Java 8 or later, and Log4j v.2.12.2, if you are running Java 7 across your web app infrastructure. These turn off JNDI by default and remove message lookup access, both of which lie at the heart of the various vulnerabilities. Disabling JNDI could break something in your apps, so test this carefully before you implement in any production systems. You might also want to stop any Java-based logging if you don’t need it for any of your applications. Again, test before deploying. And those of you who run your own Minecraft server should check and see if it is running Minecraft v.1.8.8 or later; these versions are vulnerable. Microsoft has released Minecraft v.1.18.1, which fixes the issue. You should upgrade immediately or find another and more trustworthy server that has been fixed. Security vendors have worked overtime to augment their tools, and you should take advantage of various free offers.
There are many factors influencing the performance of blockchain technology. First, the choice of consensus mechanism is highly important as this protocol or algorithm is responsible for striking a fine balance between the degree of decentralization, scalability, and security. Another key factor is the network latency as the strength of the dedicated bandwidth will play a vital role in broadcasting the transaction to all the nodes and help collate their responses. Similarly, node infrastructure is also a deciding factor. It is important to allocate adequate input-output operations per second (IOPS). Also, the number of nodes, smart contracts, transaction payload size, transaction pooling, and local storage are vital factors influencing the performance. The key to improving the performance and scalability is in selecting the right platform for meeting our performance goals. There are many options available in the market. The industry is constantly exploring divergent solutions to improve scalability and performance.
At this point in time, December 2021, it’s unclear whether the crypto market — now known as “Web3” — is at a market peak equivalent to 1999, or whether it’s at the very beginning of its run like the web in 1993. Either way, I’m predicting a market correction in 2022. Here’s my reasoning: My main critique of Web3 currently is that nothing useful has been built using crypto and blockchains, other than tools for speculation like crypto exchanges and NFT marketplaces. The technical infrastructure of Web3 is both flawed and also not as decentralized as many crypto proponents claim. On the other hand, this same argument could be used to prop up the 1993 comparison — when the web was also immature and not ready for the mainstream. But given the lack of viable products in Web3, my contention is that the value of this market is wildly inflated right now. Remember that the first wave of Dot Com IPOs, starting with Netscape, didn’t kick off until the second half of 1995. That was a point when web platforms were fast maturing, and had attracted attention (and intense competition) from big tech companies like Microsoft and Oracle.
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A Republican senator will soon introduce a bill that, for the first time, attempts to regulate the cryptocurrency space. The bill would reportedly add investor protections, rein in stablecoins, which are pegged to fiat currency, and create a self-regulatory body under the jurisdiction of the U.S. Securities and Exchange Commission and its sister agency, the Commodity Futures Trading Commission. The proposal, first reported by Bloomberg, stems from Sen. Cynthia Lummis, R-Wyo., a longtime crypto-evangelist and one of two U.S. senators to have reportedly invested in virtual currency. Her cryptoassets reportedly total a quarter of a million dollars. In legislation she plans to introduce in early 2022, Lummis intends to provide regulatory clarity on stablecoins - long the subject of congressional debate over concerns around risks and liquidity - and define the different asset classes, while introducing additional protections to insulate investors against substantial losses, scams and potentially lax cybersecurity.
Rapid cloud-based adoption and disruptive business models have led Unicorns to experience unprecedented growth in revenue and customer acquisition – especially within the fields of Fintech, Healthtech and internet services. Data operations have scaled up to meet demand, however, data security hasn’t kept pace. A prime example of this is the data breach at Robinhood, in which an unknown third party used social engineering to glean information from a customer service representative over the phone. The bad-faith actor was able to gain access to sensitive customer support data, ultimately affecting over five million customers. Clearly, the customer support employee was over-privileged, meaning they had access to more data than was necessary for them to do their job effectively. Startups, especially those experiencing rapid growth, such as Robinhood, often start off with trust-based data access policies, where employees are given broad access to data, which initially fuels faster decision making.
One of the most popular applications of CNN is in the field of image classification. In terms of superposition and parallel computation, quantum computers offer significant advantages. Quantum Convolutional Neural Network improves CNN performance by incorporating quantum environments. In this section, we’ll look at how the QCNN can help with image classification. The quantum convolution layer is a layer in a quantum system that behaves like a convolution layer. To obtain feature maps composed of new data, the quantum convolution layer applies a filter to the input feature map. Unlike the convolution layer, the quantum convolution layer uses a quantum computing environment for filtering. Quantum computers offer superposition and parallel computation, which are not available in classical computing and can reduce learning and evaluation time. Existing quantum computers, on the other hand, are still limited to small quantum systems.