PLANO, Texas, Oct. 28, 2021 (GLOBE NEWSWIRE) -- INTRUSION Inc. (NASDAQ: INTZ), a provider of cyberattack prevention solutions including those stemming from Zero-Days and malware free exploits, will release its third quarter 2021 financial results on Thursday, November 11, 2021 after market close. Joe Head, CTO, Franklin Byrd, CFO, and Gary Davis, CMO, will host a conference call at 4:00 p.m. Central Time to discuss the Companys financial results.

The conference call will be broadcast live in listen-only mode on the investor relations website at ir.intrusion.com.

Analysts and investors who would like to join the live call via teleconference are invited to dial in using the following information:

Date: Thursday, November 11, 2021Time: 4:00 p.m. Central TimeConference Call Number: 1-888-330-2041International Call Number: +1-646-960-0151Access Code: 6774917

A telephone replay of the conference call will be available approximately two hours after the conference call through November 18, 2021. The replay can be accessed by dialing 1-800-770-2030 and using the passcode 6774917. International callers should dial +1-647-362-9199 and enter the same passcode at the prompt.

About INTRUSION Inc.

INTRUSION, Inc. (NASDAQ: INTZ) protects any-sized company by leveraging advanced threat intelligence with real-time artificial intelligence to kill cyberattacks as they occur including zero-days. INTRUSIONs solution families include INTRUSION Shield, an advanced cyber-defense solution that kills cyberattacks in real-time using artificial intelligence (AI) and advanced threat intelligence; INTRUSION TraceCop for identity discovery and disclosure; and INTRUSION Savant for network data mining and advanced persistent threat detection. For more information, please visit http://www.intrusion.com.

Cautionary Statement Regarding Forward-Looking InformationThis release may contain certain forward-looking statements, including, without limitations, statements about the performance of protections provided by our INTRUSION Shield product, as well as any other statements which reflect managements expectations regarding future events and operating performance. These forward-looking statements speak only as of the date hereof and involve a number of risks and uncertainties, including, without limitation, the risks that our products and solutions do not perform as anticipated or do not meet with widespread market acceptance. These statements are made under the safe harbor provisions of the Private Securities Litigation Reform Act of 1995 and involve risks and uncertainties which could cause actual results to differ materially from those in the forward-looking statements, including, risks that we have detailed in the Companys most recent reports on Form 10-K and Form 10-Q, particularly under the heading Risk Factors.

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IR ContactJoel Achramowicz sheltonir@sheltongroup.comP: 415-845-9964

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INTRUSION to Announce Third Quarter 2021 Financial Results on Thursday, November 11, 2021 - Yahoo Finance

Machines, rather than people, will make data-intensive decisions and repetitive, manual tasks will be automated. However, the goal is not to replace human managers and the workforce. The aim is to improve their efficiency and productivity. Even when systems are operating at a high maturity, humans will still be necessary for decisions that arent made frequently, or those that require a high degree of human engagement. For other, more menial tasks, we can free up time for humans to focus on more value-based, creative or strategic thinking activities. IT processes, operational planning, marketing, sales, and accounting are all examples of areas that can be fully automated and AI:fied.

To identify the areas that automation and AI can make the largest contribution to human work in an organization, we start with what we call a discovery phase. During this phase, the technical and business requirements of a specific use case are identified. Examples of use cases include those that need encryption capabilities, for instance:

The traditional ways of working in a discovery phase is very manual and consists to a large extent of detailed interviews or workshops with subject matter experts (SMEs). One of the most significant challenges with manual process mapping is effectively extracting information from employees involved in a process. Knowledge gaps, employee validation, and human subjectivity will unavoidably arise when piecing together perceived reality to form a process map. Doing this manual work is also time consuming and takes away the SMEs time from more productive work. By automating the discovery phase, we reduce costs, remove any bias (which is more likely to be introduced through SMEs being interviewed by humans), and obtain a complete picture of processes that are otherwise hard to discover.

The discovery of use cases is done automatically with the help of technologies such as task mining, process mining and semantic search. These technologies leverage a data-driven and automated approach to a discovery, allowing the users to remain objective in their decision-making and drive improvements based on facts. These solutions take advantage of AI and machine learning (also Auto-ML) capabilities to understand more than a human ever could. Figure 1 shows the flow of events in a discovery process.

Figure 1: Automated discovery flow.

Task mining works by monitoring the actions users take. A recorder is installed on an employees computer to capture their interactions in the different applications they use, and it records data like keystrokes, clicks, data entry, copying and pasting, and other routinely performed actions to uncover how tasks are completed. Task mining focuses on tasks. Tasks are smaller components of a process or subprocess containing several steps, usually performed manually by employees at their workstations. Task mining enables organizations to discover, understand, analyze, optimize, improve, and even automate the tasks employees perform as they relate to completing larger processes. The main objective with task mining is to find ways to improve how tasks are carried out, or automate them to increase operational efficiency, reduce errors, and improve employee engagement.

Another technology that enables automated discovery is process mining. Unlike task mining, process mining focuses on processes. Process mining turns event logs from various IT systems into an instant visual model of a given flow. It depicts the as-is process based on facts with all its cases, variants, and paths. Process mining revolves around discovering, modeling, analyzing, monitoring, and optimizing end-to-end processes and their subprocesses. It allows us to identify any deviations and understand the reason for variation so we can make the necessary improvement or alignment. It can also be used to create key performance indicators (KPIs), identify root causes of variations and support the identification of process improvements.

Finally, a third technology that enables automated discovery is semantic search. In this case, a search engine is able to guess the semantic meaning of the input text using natural language processing (NLP) and other AI algorithms.

To evaluate the benefits of activity mining, we engaged on a proof of concept with an internal Ericsson team. The Ericsson Supply ECP Order and Delivery Management team used task mining to find 18 percent effort savings through automating processes and optimizing application usage. The team wanted to discover process automation and optimization opportunities and document selected processes to transform the as-is to to-be for automation. They also needed a way to understand usage patterns of the application portfolio.

The proof of concept was limited to a team consisting of 20 selected employees who worked across 10 markets. The business processes for this team are complex and are performed using a variety of applications, the majority of which do not produce application logs (for example, in Outlook, Excel, Adobe). Their requirements towards automation were:

In this manner, a task mining tool for process discovery across eight processes was deployed and the results were used to understand how the team was performing each process with minimal input required from the end users. In addition, the tool was used to generate process documentation. In a collaborative environment, SMEs and process experts can now transform the process from their discovered as-is state to the to-be state for faster and more accurate automation development.

After analyzing the interactions of the 20 users, the following insights were found: The tool identified significant savings opportunities of 8 percent through transformation, from eight business processes in scope. An additional 10 percent saving opportunity was identified associated with task effort, representing added potential automation opportunity to save efforts in tasks performed in Excel and SAP.

Processes and interactions are basics in the execution and scaling of digital transformation, new AI capabilities and new forms of automation. Activity mining enables our organization to have a complete picture of the as-is process and help identify the areas where automation and AI can make the largest contribution.

To understand the as-is process is critical to knowing whether its worth investing in improvements, where performance problems exist, and how much variation there is in the process across the organization. Activity mining allows for automation beyond a single technology, and is opening the door to the next automation and AI operating systems with hyperautomation at the core.

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What are task and process mining in AI? - Ericsson

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University of Arkansas team, left to right, top: Haitao Liao, Ed Pohl, Xiao Liu; bottom, Xintao Wu, Roy McCann and Yue Zhao.

The National Science Foundation has awarded researchers from industrial engineering, electrical engineering and computer science at the U of A $1.45 million to investigate of the potential of artificial intelligence as a driving force for changes to critical infrastructures and industries.

Their ultimate goal is to establish a collaborative research and workforce development/education program. This four-year multi-institution, multidisciplinary project, worth $6 million in total, will be led by North Dakota State University and the U of A, with other collaborators from University of Nevada-Las Vegas and Nueta Hidatsa Sahnish College in New Town, North Dakota.

"We know firsthand that vulnerabilities in our energy grid can have serious consequences for our economy and society, so I'm proud of this multidisciplinary team investigating how artificial intelligence can help improve our energy infrastructure," said Kim Needy, dean of the College of Engineering. "We're grateful to the National Science Foundation for supporting work that will help ensure our energy infrastructure is protected against various catastrophic failures."

Led by professor Haitao Liao, the Arkansas team includes Ed Pohl, professor, and Xiao Liu, assistant professor of industrial engineering; Xintao Wu, professor of computer science and computer engineering; Roy McCann, professor, and Yue Zhao, associate professor of electrical engineering.

"It has been a great experience working on this multidisciplinary team," Wu said. "We will develop new AI models such as multi-layered network embedding and neural survival analysis and integrate them in the data-driven decision-making framework for responding to disruptions in the energy systems." Wu leads the Social Awareness and Intelligent Learning (SAIL) Lab, which conducts research in areas of data privacy and security, data mining, machine learning and artificial intelligence.

McCann said the national energy infrastructure is rapidly expanding to provide a wider range of electricity sources such as from wind and solar. "As was seen from the cold weather-related blackouts and disruptions in Texas during February, it is imperative to build resilience into energy delivery systems. Because of the increasing complexity of energy networks and supply chains, developing AI into the equipment that controls energy production and delivery is a solution for meeting future reliability demands."

Zhao continued this point by saying the U of A is home to the NSF Center on GRid-connected Advanced Power Electronic Systems, also called GRAPES. "Over the past decade, extensive research and development work has been done in GRAPES to accelerate the adoption and insertion of power electronics into the electric grid in order to improve system stability, flexibility, robustness and economy. In this project, as GRAPES faculty members, professor McCann and I will contribute to the modeling and simulation for the power grid."

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NSF Awards Grant to Study Use of AI to Improve Sustainable Energy Infrastructure Network - University of Arkansas Newswire

The US-Mexico border wall near Sasabe, Arizona.

Ross D. Franklin/Associated Press

US Customs and Border Protection and Immigration and Customs Enforcement use phone hacking, license-plate scanning, iris recognition, and a host of other surveillance technology to monitor the US border, according to a report released on Thursday by immigrant-rights groups.

The Deadly Digital Border Wall, compiled by Mijente, Just Futures Law, and the No Border Wall Coalition, doesnt identify every piece of software and hardware the agencies use. One example is Raven, short for Repository for Analytics in a Virtualized Environment, a data-mining and analytics tool ICE is developing. But it does provide an overview of some of the key technologies that ICE and CBP rely on.

Cinthya Rodriguez, a Mijente organizer, said the US border was a testing ground for more widespread expansions of government surveillance.

We remain steadfast in our #NoTechForICE demand and call on the Biden administration to follow up on promises made to immigrant communities with meaningful action, she added.

Here are the top 14 border-surveillance technologies, according to the report:

This is a camera system that takes pictures of car license plates as they pass by and logs other relevant information like date, time, and place. Immigration agencies install these cameras at and near borders. CBP pays Motorola Solutions for it, while ICE pays Vigilant Solutions.

Along the US-Mexico border, CBP operates tall towers designed to spot people from almost 10 miles away. The agency has paid the Israeli military contractor Elbit Systems to install these structures.

These surveillance towers are slightly smaller and movable. General Dynamics won a $177 million contract for installing these in 2013. The project is scheduled to be completed in 2023.

This consists of a telescope, a laser light, a thermal-imaging device, and a video system thats attached to the back of a truck, which itself is equipped with geospatial analytics. Since 2015, Tactical Micro has been providing CBP with dozens of the systems, and PureTech Systems has been providing the in-vehicle geospatial analytics.

CBP uses these towers to scan the landscape for people. Anduril has been providing them since 2018. They come equipped with software designed to distinguish people from animals and store images of human faces.

CBP has used large and small drones to surveil remote areas of land, and even the sea, for evidence of migrants. General Atomics, AeroVironment, FLIR Systems, Lockheed Martin, and Anduril have all provided CBP with surveillance drones.

This software sends files of fingerprint scans, pictures of faces, and iris images captured by separate pieces of hardware to both ICEs case management system for Enforcement and Removal Operations (ERO) and to the DHS-wide IDENT database, the report said. According to the Department Homeland Security, the automated biometric identification system stores 260 million unique identities and processes more than 350,000 biometric transactions per day.

According to the report, the Homeland Advanced Recognition Technology (HART) System is a database of biometric data such as face images, DNA profiles, iris scans, fingerprint scans, and audio files, called voice prints, thats hosted on Amazon Web Services and being developed by Northrop Grumman. It will eventually replace the automated biometric identification system (IDENT) currently used by DHS, the report said.

This is a face-matching tool that CBP uses at the US border and entry ports like airports. A camera takes a picture of a traveler and compares it with a passport or other form of ID. CBP says it stores comparison photos of US citizens in IDENT for 15 years.

This is a mobile app on which Border Patrol agents can collect biometric and other personal information on migrants and asylum seekers. Data that can be logged in to the app includes phone numbers, employment and family information, marital status, people traveling together, permanent address abroad, and destination in the US, as well as a photograph to be run through CBP biometric records, the report said.

Using services from the Israeli company Cellebrite, Canadas Magnet Forensics, and the US company Grayshift, CBP can search mobile phones and other devices for information about a person. Grayshift and Cellebrite can sometimes unlock phones. US border agents dont need warrants to search peoples devices, according to a 2019 court ruling, and border agents searched more than 40,000 devices in 2019, Reuters reported.

These are pieces of software designed to extract personal information stored in cars, including call logs and navigation histories. CBP has paid Berla Corp., which partnered with the Swedish mobile-forensics company MSAB, for this service.

Venntel, a subsidiary of the data broker Gravy Analytics, sells licenses to CBP and other DHS entities that allow them to use consumer cellphone data to track migrants and asylum seekers. A DHS document leaked to BuzzFeed News said it was possible to combine the consumer location data with other information and analysis to identify an individual user.

ICAD is a system of underground sensors and cameras that detects the presence or movement of individuals and sends data back to CBP. Border Patrol agents then seek out the people captured on ICAD, ask these people for their biographic data, and store this information, according to the report.

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Here are the 14 most important pieces of surveillance technology that make up the US digital border wall, according to immigrant-rights groups - News...