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Machine Learning

Open Security & Safety Alliance membership doubles; open platform initiatives surge forward

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05/07/2019

SAN RAMON, Calif.—In the Fall of 2018, five companies — Bosch Building Technologies, Hanwha Techwin, Milestone Systems, Pelco by Schneider Electric and VIVOTEK, Inc. — came together for the good of the open platform community, becoming founding companies of the Open Security & Safety Alliance (OSSA), or simply, “the Alliance.”

AI coming to the aid of security-related applications

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Wednesday, March 20, 2019

Our May 2019 News Poll got me really thinking about Artificial Intelligence (AI) and Machine Learning (ML), and the possibilities. My previous AI-related thoughts have been around Watson, the IBM-created, question-answering computer system that answers in natural language, and robots, and how AI can take over the world one day, according to some! Spooky! But, I wanted to know if AI is a legit, practical application for security-related functions, so I scoured the internet and found some exciting and unique, currently deployed uses. 

Physical Security

According the to China Morning Post, AI is revolutionizing physical security in Asia. It can detect people acting out of the ordinary and flag them, and then transmit that information to a command center, where human operators can make an informed decision. Additionally, AI and high-definition cameras can work together to first communicate to a human that a smoke detector, for example, has been activated, with the cameras identifying the exact location of the fire. 

Financial Security

Shoplifting literally costs billions of dollars here in the United States, which trickles down to honest consumers who end up paying more for goods and services. Vaak, a Tokyo-based company, spent more than 100 hours showing their AI system closed-circuit television footage of honest shoppers and shoplifters. The system can now identify suspicious activity based on more than 100 aspects of shoppers’ behavior including gait, hand movements, facial expressions, clothing choices and even “restless” and “sneaking” behaviors. Store employees are alerted of suspiciousness via an app and they can decide what to do. 

Life Security

Paris-based startup, Pharnext, was founded by Daniel Cohen, who “mapped” the human genome and demonstrated it is possible to use Big Data and automation to speed up the processing of DNA samples. Today, Cohen is using AI to analyze and map the chain of reactions of disease in the body. With this information, he and his team are combining existing drugs, known as “repurposing,” to create therapeutic effects that each drug lacks on its own. His overall goal is to use existing medicines to treat all disease, preventing the design of new medicines. 

Cybersecurity

Post-doctoral research fellow at Stanford University, Dr. Srijan Kuman, is developing an AI method — REV2 — to identify online conflict using data and machine learning to predict internet trolling before it happens. (Trolling is an action by a person who posts inflammatory and often deceptive and disinformation online to provoke others to respond on pure emotion.) Kuman uses statistical analysis, graph mining, embedding and deep learning to determine normal and malicious behaviors. His method is currently being used by Flipkart, an online store, to identify fake reviews and reviewers, and he was able to accurately predict when one Reddit community will troll another. 

Be sure to check out our editor’s blog that talks about worldwide spending on AI systems to reach $35.8 billion in 2019, according to International Data Corporation. 

 

New tech holds the key to stopping cybercrime, study finds

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Tuesday, February 12, 2019

You don’t have to look too hard to find a sobering example of cybercrime, as it's as pervasive as ever these days, even on the national level with recent reports that cyber criminals have access to critical infrastructure such as our national power grids and gas lines. The good news, though, is technology may be our best weapon against these invisible criminals.

In fact, the use of big data and blockchain technologies are key to fighting cybercrime, according to a new study from Frost & Sullivan that looks at how effective machine learning is in aiding early detection of cyber anomalies, and how good blockchain is at creating a trustworthy network between endpoints.

Frost and Sullivan noted that the rise of the Internet of Things has opened up numerous points of vulnerabilities, compelling cybersecurity companies, especially startups, to develop innovative solutions to protect enterprises from emerging threats. As cybercrime becomes more sophisticated and even a method of warfare, the research firm found, technologies such as machine learning, big data, and blockchain will become prominent.

"Deploying Big Data solutions is essential for companies to expand the scope of cybersecurity solutions beyond detection and mitigation of threats,” Hiten Shah, research analyst, TechVision, said in the announcement of the findings. "This technology can proactively predict breaches before they happen, as well as uncover patterns from past incidents to support policy decisions."

The study, Envisioning the Next-Generation Cybersecurity Practices, presents an overview of cybersecurity in enterprises and analyzes the drivers and challenges to the adoption of best practices in cybersecurity. It also covers the technologies impacting the future of cybersecurity and the main purchase factors.

"Startups need to make their products integrable with existing products and solutions as well as bundle their solutions with market-leading solutions from well-established companies," noted Shah. "Such collaborations will lead to mergers and acquisitions, ultimately enabling companies to provide more advanced solutions."

Technologies that are likely to find the most application opportunities include:

•    Big Data: It enables automated risk management and predictive analytics. Its  adoption will be mostly driven by the need to identify usage and behavioral patterns to help security operations spot anomalies.
•    Machine Learning: It allows security teams to prioritize corrective actions and automate real-time analysis of multiple variables. Using the vast pools of data collected by companies, machine-learning algorithms can zero in on the root cause of the attack and fix detected anomalies in the network.
•    Blockchain: The data stored on blockchain cannot be manipulated or erased by design. The tractability of activities performed on blockchain is integral to establishing a trustworthy network between endpoints. Furthermore, the decentralized nature of blockchain greatly increases the cost of breaching blockchain-based networks, which discourages hackers.

Envisioning the Next-Generation Cybersecurity Practices is part of Frost & Sullivan’s global Information & Communication Growth Partnership Service program.