What Lurks in the Dark Web?
October 20, 2016
Organizations concerned about cyber security can effectively thwart any threats conditionally they know a threat is lurking in the dark. An Israeli SaaS-based startup claims it can bridge this gap by offering real-time analysis of data on Dark Web.
TechCrunch in an article Sixgill claims to crawl the Dark Web to detect future cybercrime says:
Sixgill has developed proprietary algorithms and tech to connect the Dark Web’s dots by analyzing so-called “big data” to create profiles and patterns of Dark Web users and their hidden social networks. It’s via the automatic crunching of this data that the company claims to be able to identify and track potential hackers who may be planning malicious and illegal activity.
By analyzing the data, Sixgill claims that it can identify illegal marketplaces, data leaks and also physical attacks on organizations using its proprietary algorithms. However, there are multiple loopholes in this type of setup.
First, some Dark Web actors can easily insert red herrings across the communication channels to divert attention from real threats. Second, the Dark Web was created by individuals who wished to keep their communications cloaked. Mining data, crunching it through algorithms would not be sufficient enough to keep organizations safe. Moreover, AI can only process data that has been mined by algorithms, which is many cases can be false. TOR is undergoing changes to increase the safeguards in place for its users. What’s beginning is a Dark Web arms race. A pattern of compromise will be followed by hardening. Then compromise will occur and the Hegelian cycle repeats.
Vishal Ingole, October 20, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
SLI Systems Hopeful as Losses Narrow and Revenue Grows
June 14, 2016
The article titled SLI Systems Narrows First-Half Loss on Scoop reports revenue growth and plans to mitigate losses. SLI Systems is a New Zealand-based software as a service (SaaS) business that provides cloud-based search resources to online retailers. Founded in 2001, SLI Systems has already weathered a great deal of storms in the form of the dot-com crash that threatened to stall the core technology (developed at GlobalBrain.) According to a statement from the company, last year’s loss of $502K was an improvement from the loss of $4.1M in 2014. The article states,
“SLI shares have dropped 18 percent in the past 12 months, to trade recently at 76 cents, about half the level of the 2013 initial public offering price of $1.50. The software developer missed its sales forecast for the second half of the 2015 year but is optimistic new chief executive Chris Brennan and Martin Onofrio as chief revenue officer, both Silicon Valley veterans, can drive growth in revenue and earnings.”
The SLI of SLI stands for Search, Learn and (appropriately) Improve. The company hopes to achieve sustainable growth without raising additional capital by continuing to focus on innovation and customer retention rates, which slipped from 90% to 87% recently. Major clients include Lenovo, David Jones, Harvey Norman, and Paul Smith.
Chelsea Kerwin, June 14, 2016
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
Cerebrant Discovery Platform from Content Analyst
April 29, 2015
A new content analysis platform boasts the ability to find “non-obvious” relationships within unstructured data, we learn from a write-up hosted at PRWeb, “Content Analyst Announces Cerebrant, a Revolutionary SaaS Discovery Platform to Provide Rapid Insight into Big Content.” The press release explains what makes Cerebrant special:
“Users can identify and select disparate collections of public and premium unstructured content such as scientific research papers, industry reports, syndicated research, news, Wikipedia and other internal and external repositories.
“Unlike alternative solutions, Cerebrant is not dependent upon Boolean search strings, exhaustive taxonomies, or word libraries since it leverages the power of the company’s proprietary Latent Semantic Indexing (LSI)-based learning engine. Users simply take a selection of text ranging from a short phrase, sentence, paragraph, or entire document and Cerebrant identifies and ranks the most conceptually related documents, articles and terms across the selected content sets ranging from tens of thousands to millions of text items.”
We’re told that Cerebrant is based on the company’s prominent CAAT machine learning engine. The write-up also notes that the platform is cloud-based, making it easy to implement and use. Content Analyst launched in 2004, and is based in Reston, Virginia, near Washington, DC. They also happen to be hiring, in case anyone here is interested.
Cynthia Murrell, April 29, 2015
Sponsored by ArnoldIT.com, publisher of the CyberOSINT monograph
-
- Subscribe to Beyond Search
Feature archive
News archive
-
