Cybersecurity: A Booming Business
September 23, 2020
The United Kingdom has seen record growth for cyber security startups. The record growth in the cybersecurity field is due to the COVID-19 pandemic and the heavy demand on Internet and digital services. Internet and digital services must be protected from potential bad actors stealing individuals’ information or be mischievous during Zoom meetings. Tech Round explains more about cybersecurity’s growth in: “Cybersecurity: The Fastest Growing UK Startup Sector During COVID-19.”
Before the pandemic struck, cybersecurity focused on financial and regulatory risks. Cyber risk management is now a hot ticket for investors. COVID-19 also points to a future where more people will be working remotely, organizations will host their data offsite, and more services will be online:
“Ajay Hayre, Senior Consultant Technology at Robert Walters comments: “Historically IT security has represented only 5% of a company’s IT budget but due to remote working and transition to online or cloud-based solutions, cybersecurity has been thrust to the centre of business continuity plans, having proved its worth in enabling business objectives during lockdown. Not only will every company see the benefit of having this expertise in-house, but they will be looking externally for tools, services and advisors to help guarantee the future-proofing of their business by way of solid and robust cybersecurity provisions.”
What is even more interesting are the venture capitalists behind the investing. The PHA Group breaks down who the “5 Key VCs Backing Cybersecurity Startups” are. According to the LORCA Report 2020, a half billion pounds were fundraised in the first half of 2020 for cybersecurity startups. This is a 940% increase compared to 2019. Venture capitalists also want to invest their money in newer technologies, such as AI, encryption, secure containers, and cloud security. The five companies that invested the most in UK cybersecurity are Ten Eleven Ventures, Energy Impact Partners, Index Ventures, and Crosslink Capital.
Whitney Grace, September 23, 2020
Financial Crime: Business As Usual?
September 22, 2020
DarkCyber noted “HSBC Moved Vast Sums of Dirty Money after Paying Record Laundering Fine.” The article makes clear that banks do what banks do: Move money. Why? To make money, earn bonuses, and become a master of the banking universe.
Is anyone surprised? The authors of the write up seem to be. We noted this passage:
The FinCEN Files investigation found that HSBC’s highly profitable branch in Hong Kong played a key role in keeping the dirty money flowing. Although providing only a partial view of HSBC’s suspicious activity reports, the records show that between 2013 and 2017, HSBC’s U.S. compliance staff, who are charged with monitoring customer activity, filed reports lacking crucial customer information on 16 shell companies that had processed nearly $1.5 billion in more than 6,800 transactions through the bank’s Hong Kong operations alone. More than $900 million of that total involved shell companies linked to alleged criminal networks…
Institutions have processes. Once processes kick in, the paper pushing and the employees keep the wheels turning. The “work” is following the “rules” in order to complete tasks. Changing work processes in a large organization is difficult, often impossible. Quibi makes videos few watch. Facebook sells targeted ads across borders based on free flowing data. Successful organizations are successful because individuals find ways to generate profit from tasks others find giant money losers.
The write up hits the problem right between the eyes, stating:
Compliance officers said that the bank did not give them enough time to meaningfully investigate suspicious transactions and that branches outside the U.S. often ignored requests for crucial customer information. They said they were treated as a second-class workforce within the bank, with little power to shut down problematic accounts.
The exposition about the HSBC big bank is a reminder that institutions are, supercharged with online systems, smart software, and people who follow prescribed work procedures. In these efficient organizations, making money is the driver.
Regulators, compliance officers, and employees are unable to take meaningful action. Is it a surprise that “The Risk Makers: Viral Hate, Election Interference, and Hacked Accounts: Inside the Tech Industry’s Decades-Long Failure to Reckon with Risk” reaches an obvious conclusion: Money is the driver.
Consider the question, “What’s gone wrong?”
The answer is, “Nothing.” The system is what regulators, employees, and people want it seems.
Observations:
- A new definition of “crime” may be needed to embrace the reality of institutional behavior
- Regulatory authorities struggle to deal with corporate entities which are more impactful than governments
- Individuals appear willing to skirt social norms in order to feather their nest and craft a life outside of certain institutions.
Intriguing challenges for the institutions, their employees, and the governments charged with enforcing rules, laws, and mandated behaviors.
Stephen E Arnold, September 23, 2020
Body Cameras: A Study Review
September 22, 2020
Anyone interested in the use of body cams by police should check out this review of 30 studies assembled by Campbell Collaboration—“The Impacts of Body-Worn Cameras in Policing.” Adoption of body cameras by police has risen steeply over the last decade as costs have decreased and concern about police misconduct have escalated. While the intention is to increase transparency and accountability, some have been concerned the practice would discourage the reporting of crimes or cause officers to hesitate to take appropriate proactive or preventative measures.
The review summarizes studies that used either randomized controlled trials or quasi-experimental research designs that measured police or citizen behaviors. The studies reported on a dozen different types of outcome measures and examine 116 effects of the cameras on those outcomes. Most were conducted in single jurisdictions in the US.
So, is the use of body cams doing more good than harm? The write-up summarizes the findings:
“BWCs are one of the most rapidly diffusing and costly technologies used by police agencies today. This review questions whether BWCs bring the expected benefits to the police and their communities. Existing research does not evaluate whether police accountability or police-citizen relationships are strengthened by BWCs. Much more knowledge is needed about when BWCs do create desired effects, and whether they are cost-effective. … For the many police agencies that have already purchased BWCs, researchers should continue testing for ways in which both police and citizens might gain benefits from the cameras’ continued use. These could include limiting the discretion that officers have with BWC use, using BWCs for coaching, training or evidentiary purposes, and finding ways that BWCs can be used to strengthen police-citizen relationships, internal investigations, or accountability systems.”
Count that as a definite maybe. To read the report in full, navigate to its Wiley Online Library page.
Cynthia Murrell, September 22, 2020
Like Life, Chatbots Are Semi Perfect
September 22, 2020
Chatbots are notoriously dumb pieces of AI that parrot information coded into their programs. They are also annoying, because they never have the correct information. Chatbots, however, are useful tools and developers are improving them to actually be useful. Medium runs down the differences between chatbots: “Updated: A Comparison Of Eight Chatbot Environments.”
Most chatbot environments have the same approach for a conversational interface, but there are four distinct development groups: avant-garde, NLU/NLP tools, use-the-cloud-you’re-in, and leading commercial cloud offerings. There are cross-industry trends across these groups:
“ The merging of intents and entities
• Contextual entities. Hence entities sans a finite list and which is detected by their context within a user utterance.
• Deprecation of the State Machine. Or at least, towards a more conversational like interface.
• Complex entities; introducing entities with properties, groups, roles etc.”
Beyond the industry trends, chatbots are transitioning from the stupid instant messaging programs to interactive, natural language driven, digital employee that “thinks and acts” like a real human. Companies want to use chatbots to grow by being able to comprehend past and current conversations, from multiple sources, and from CRM sources.
Chatbots cannot be compared because their frameworks are so different, but there are five consideration points. The NLU features, ecosystem maturity, licensing/usage costs, graphic call flow front-end developing and editing, and scalability and enterprise readiness are the important consideration points.
Chatbots are becoming smarter and already handle many customer service jobs. If they can actually resolve the problems customers contact companies for, then science fiction truly has become reality.
Whitney Grace, September 22, 2020
Web Scraping: Better Than a Library for Thumbtypers
September 22, 2020
Modern research. The thumbtyper way.
Nature explains the embrace of a technology that, when misused, causes concern in the post, “How We Learnt to Stop Worrying and Love Web Scraping.” The efficiency and repeatability of automation are a boon to researchers Nicholas J. DeVito, Georgia C. Richards, and Peter Inglesby, who write:
“You will end up with a sharable and reproducible method for data collection that can be verified, used and expanded on by others — in other words, a computationally reproducible data-collection workflow. In a current project, we are analyzing coroners’ reports to help to prevent future deaths. It has required downloading more than 3,000 PDFs to search for opioid-related deaths, a huge data-collection task. In discussion with the larger team, we decided that this task was a good candidate for automation. With a few days of work, we were able to write a computer program that could quickly, efficiently and reproducibly collect all the PDFs and create a spreadsheet that documented each case. … [Previously,] we could manually screen and save about 25 case reports every hour. Now, our program can save more than 1,000 cases per hour while we work on other things, a 40-fold time saving. It also opens opportunities for collaboration, because we can share the resulting database. And we can keep that database up to date by re-running our program as new PDFs are posted.”
The authors explain how scraping works to extract data from web pages’ HTML and describe how to get started. One could adopt a pre-made browser extension like webscraper.io or write a customized scraper—a challenging task but one that gives users more control. See the post for details on that process.
With either option, we are warned, there are several considerations to keep in mind. For some projects, those who possess the data have created an easier way to reach it, so scraping would be a waste of time an effort. Conversely, other websites hold their data so tightly it is not available directly in the HTML or has protections built in, like captchas. Those considering scraping should also take care to avoid making requests of a web server so rapidly that it crashes (an accidental DoS attack) or running afoul of scraping rules or licensing and copyright restrictions. The researchers conclude by encouraging others to adopt the technique and share any custom code with the community.
Cynthia Murrell, September 22, 2020
Podcast Search: Illuminating the Rich Media Darkness
September 22, 2020
Search for podcasts is broken. We learn of a possible first step toward a fix from Podnews in the brief write-up, “The Podfather Launches a New, Open Podcast Directory.” James Cridland writes:
“‘The digital ad space is watching as the bottom falls out of their data collection methods. But how exactly does Apple’s Age of Privacy impact podcasting?’ – in today’s Sounds Profitable, our new adtech newsletter, with Podsights.
“Adam Curry has launched a new, open podcast directory for app developers, working with developer Dave Jones. Speaking on a new podcast, Podcasting 2.0, Curry and Jones worry that ‘Apple is starting to tinker with their directory’, and say that the company is ‘a very centralized private entity that is controlling pretty much what everybody considers the default yellow pages for podcasting.’ His alternative, The Podcast Index, promises that the ‘core, categorized index will always be available for free, for any use’. You can sign up to be a developer on their developer portal. We support this initiative. As of today, Podnews uses The Podcast Index for our main podcast search.”
The index is a simple type-and-search format. It seems to work acceptably well on Podnews’ database, though it could use a little relevance refinement. Will the open directory attract developers and reach the larger segment? We hope this or another solution is implemented soon.
Cynthia Murrell, September 22, 2020
DarkCyber for September 22, 2020, Now Available: Bogus Passports, Chinese Data and Apps, and the Dronut Drone
September 22, 2020
DarkCyber for September 22, 2020, is now available. This week’s program features an update on falsified documents, three stories about China, and a report about the Dronut. You can view the video on YouTube. The video is available via the Beyond Search blog.
Kenny Toth, September 22, 2020
Predictive Analytics: Follow These Puffy Thought Bubbles
September 21, 2020
Predictive analytics is about mathematics; for instance, Bayesian confections and Markov doodling. The write up “Predictive Analytics: 4 Primary Aspects of Predictive Analytics” uses the bound phrase “predictive analytics” twice in one headline and cheerfully ignores the mathy reality of the approach.
Does this marshmallow approach make a difference? Yes, I believe it does. Consider this statement from the write up:
These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data. These statistical models are growing as a result of the wide swaths of available current data as well as the advent of capable artificial intelligence and machine learning.
Okay, marketers. Predictive analytics are right in your wheelhouse. The assumption that “statistical models are growing” is interesting. The statistical models with which I am familiar require work to create, test, refine, and implement. Yep, work, mathy work.
The source of data is important. However, data have to be accurate or verifiable or have some attribute that tries to ensure that garbage in does not become the mode of operation. Unfortunately data remain a bit of a challenge. Do marketers know how to identify squishy data? Do marketers care? Yeah, sure they do in a meeting during which smartphone fiddling is taking place.
The idea of data utility is interesting. If one is analyzing nuclear fuel pool rod placement, it does help to have data relevant to that operation. But are marketers concerned about “data utility”? Once again, thumbtypers say, “Yes.” Then what? Acquire data from a third party and move on with life? It happens.
The thrill of “deep learning” is like the promise of spring. Everyone likes spring? Who remembers the problems? Progress is evident in the application of different smart software methods. However, there is a difference between saying “deep learning” or “machine learning” and making a particular application benefit from available tools, libraries, and methods. The whiz kids who used smart software to beat a human fighter pilot got the job done. The work required to achieve the digital victory was significant, took time, and was difficult. Very difficult. Marketers, were you on the team?
Finally, what’s the point of predictive analytics? Good question. For the article, the purpose of predictive analytics is to refine a guess-timate. And the math? Just use a smart solution, click and icon, and see the future.
Yikes, puffy thought bubbles.
Stephen E Arnold, September 21, 2020
Information Manipulation: A Rich Tradition
September 21, 2020
“Scientists Use Big Data to Sway Elections and Predict Riots — Welcome to the 1960s” is an interesting write up. The essay begins with a quote from a high profile Xoogler, Anthony Levandowski. He’s the engineer who allegedly found information in his possession which was not supposed be in his possession. Things just happen, of course. The quote in the write up reminded me that Sillycon Valley in an interesting place.
The point of the write up is to romp through information manipulations related to elections in the US. One company — Simulmatics — applied systems and methods refined by other experts. I am not comfortable naming these people because it is 2020. Proper nouns can be tricky business.
The write up asserts:
The press called Simulmatics scientists the “What-If Men”, because their work — programming an IBM 704 — was based on endless what-if simulations. The IBM 704 was billed as the first mass-produced computer capable of doing complex mathematics. Today, this kind of work is much vaunted and lavishly funded. The 2018 Encyclopedia of Database Systems describes ‘what-if analysis’ as “a data-intensive simulation”. It refers to it as “a relatively recent discipline”. Not so.
The “not so” nails down the obvious. Information manipulation has been around for more years than Silicon Valley’s luminaries have been reshaping the world with digital services.
This quote warranted a check mark:
Although none of the researchers he had met “had malignant political designs on the American public”, Burdick warned, their very lack of interest in contemplating the possible consequences of their work stood as a terrible danger. Indeed, they might “radically reconstruct the American political system, build a new politics, and even modify revered and venerable American institutions — facts of which they are blissfully innocent”.
Yep, Sumulmatics. The other thought the write up evoked is, “When and to what does one pay attention?” Thumbtypers, what do you think?
Stephen E Arnold, September 21, 2020
Efficiency: Modern Analytic Techniques Are Logical
September 21, 2020
I don’t pay much attention to writing about motion pictures. The title “Why Christopher Nolan Actually Blew Up A Real Plane For Tenet” was a bit of a baffler. I did not recognize the name “Christopher Nolan.” I knew the meaning of the word “tenet” but I had zero clue it was entertainment. When I looked him up, I did not recognize his cinematic masterpieces. Nevertheless, the premise of the essay was interesting:
Skip using a computer to fake blowing up a large airplane. But a 747 and just blow it up.
Interesting. Were there environmental costs? Were their additional safety related costs? Were there clean up costs? Were there additional legal costs associated with making sure that someone would have to pay if the whole deal went south? Maybe a post explosion maintenance worker catching on fire or just getting sick from breathing fumes?
Hey, breathing. What’s the big deal?
The write up does not address these questions, and my hunch is that expert cinema professionals think much about these problems even if some bright young sprout asked, “What happens if we screw up, kill a bunch of people, and maybe pollute the creek running next to the shoot?”
Hey, ducks and fish. Who cares? These folks are creating art for real people. Ducks? Or, “Hey, don’t rain on my parade” could well have been the response.
Thinking and acting efficiently is the way of the world among a certain cohort of professionals. If movie makers cannot ask, “How much will it cost if we screw this up?”, what other intellectual shortcuts have been taking place.
My hunch is a lot. Efficiency? Love it. Do large technology companies think in the manner of an esteemed, powerful creator of motion pictures? Yeah, good question.
Stephen E Arnold, September 21, 2020
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