1. Prelert Blog

    1. Anomaly Detection in Periodic Data

      Explore Anomaly Detection Analytics (Apr 10 2015)

      Anomaly Detection in Periodic Data

      This technique, combined with our probabilistic approach - ranks the level of “anomalousness” of the situation based upon the probability of it occurring. This enables not only accurate anomaly detection, but scores those anomalies on a normalized scale between 0 and 100, allowing for proactive alerting for only the most unlikely situations

      (Read Full Article)

      Comment Mentions:   Prelert

    2. What’s in Store for the Future of IT Security & Machine Learning?

      Explore Anomaly Detection Analytics (Mar 23 2015)

      What’s in Store for the Future of IT Security & Machine Learning?

      Before Bill Stangel was Senior Vice President of Strategy and Architecture at Fidelity Investments, he served as the Chief Enterprise Architect for Raytheon and advisory board member for Netezza. We sat down with Bill to pick his brain about the future of IT security and machine learning.

      (Read Full Article)

      Comment Mentions:   IT Security

    3. Analyze bigger data with summarized input

      Explore Anomaly Detection Analytics (Mar 17 2015)

      Analyze bigger data with summarized input

      The benefits of a smaller data size would be proportionally much greater if the summarization work was being distributed across a cluster of machines running a big data store such as Hadoop , Riak or Elasticsearch . But even this tiny example demonstrates the point and it’s one you can try on your own computer if you download an evaluation version of Prelert's Anomaly Detective Engine API  (http://www.prelert.com/reg/anomaly-detective-engine-api.html)

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    4. Just "ML" the Sucker!

      Explore Anomaly Detection Analytics (Mar 10 2015)

      Just "ML" the Sucker!

      A recent blog post by Gartner Analyst Dr. Anton Chuvakin caught my attention.  Titled 'SIEM/ DLP Add-on Brain?,' it mentions that “we now [have] a decent number of vendors that offer, essentially, an add-on brain for your SIEM.” We think Dr. Chuvakin is being a little harsh on the SIEM tools, implying they don’t have a brain.

      (Read Full Article)

      Comment Mentions:   Prelert   Gartner   IT Security

    5. Distilling Alert Noise to Find Real Problems

      Explore Anomaly Detection Analytics (Mar 4 2015)

      Distilling Alert Noise to Find Real Problems

      Whether your concern is IT security or APM/operations, it is highly likely that you are dealing with way more alarms than you have the resources to follow up on. Even modest sized organizations today are dealing with such overwhelming volumes of alerts that they aren't even sure what percentage are false positives. Alert fatigue is one of the biggest drivers behind investigations of advanced analytics for operations and security.

      (Read Full Article)

      Comment Mentions:   IT Security   Analytics

    6. Slow Attack Detection

      Explore Anomaly Detection Analytics (Mar 3 2015)

      Slow Attack Detection

      Detecting “brute force” attacks is a very common and obvious approach to identifying those users who are attempting to “break-in” using high-velocity, high-combinations of authentication credentials. But what about the opposite situation - an attempt to gain access via slow, but pervasive attempts at authenticating while keeping “below the radar” and avoiding potential failed authentication lock-out schemes?

      (Read Full Article)

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    7. John O’Donnell Joins Prelert as CFO to Support Company’s Accelerated Growth Strategy

      Explore businesswire.com (Feb 10 2015)

      John O’Donnell has joined Prelert, the leading provider of machine learning anomaly detection, as Chief Financial Officer. In this role, he will manage all financial, legal, human resources and administrative operations, working directly with Prelert’s senior management team to drive growth and success in each area. With more than 20 years of industry experience, O’Donnell most recently served as CFO of Aveksa, a leading provider of business-driven identity and access management software. While there, he built a global finance and administration organization to support the company’s dynamic growth, leading to its eventual acquisition by EMC Corporation. 

      (Read Full Article)

      Comment Mentions:   CA Technologies   Mark Jaffe

    8. Today's IT Challenges Require Advanced Analytics

      Explore Anomaly Detection Analytics (Feb 4 2015)

      Today's IT Challenges Require Advanced Analytics

      In both IT security and operations, a common complaint is the overwhelming "noise" of largely false positive alerts generated by problematic detection systems. In both cases as well, the lack of information contained in these alerts forces a diagnostic or investigative approach requiring humans to manually mine through huge volumes of data as they search for unusual behavior patterns that might not even be detectable by the human brain.

      (Read Full Article)

      Comment Mentions:   Analytics

    9. Data Breach Notification: You Need to “Know” Before You Can “Notify”

      Explore Anomaly Detection Analytics (Jan 26 2015)

      Data Breach Notification: You Need to “Know” Before You Can “Notify”

      Data breach notification is not simply a matter of “fessing up” when your customer’s data has been compromised. The devil is in the details. Before an organization can “notify” about a data breach, it first has to realize that something happened, figure out what happened, when it happened, what data was accessed, and which individuals were affected. In other words, the organization needs to “know” before it can “notify.”

      (Read Full Article)

      Comment Mentions:   IT Security   Analytics   Breach

    10. Temporal vs. Population Anomaly Detection

      Explore Anomaly Detection Analytics (Jan 14 2015)

      Temporal vs. Population Anomaly Detection

      Some anomalous behaviors are temporal in nature (with respect to time) while others are population based (all others). But, what are the differences between these two types of anomalies and under what circumstances would you use one kind over the other? This blog discusses the details behind the analyses, their merits, and best practices based upon common rules of thumb.

      (Read Full Article)

      Comment Mentions:   IT Security   Analytics

    11. Security Analytics Use Case: Finding Document Thieves

      Explore Anomaly Detection Analytics (Jan 6 2015)

      Security Analytics Use Case: Finding Document Thieves

      From time to time, document thieves may gain access to [institutional] accounts (probably through phishing attacks) and use them to access licensed material (journals, for instance) that only [authorized] users have access to.  These documents are often resold in a sort of intellectual property black market. The goal is to proactively detect these sorts of situations and quickly suspend the compromised accounts, rather than waiting to notice that the content is stolen...

      (Read Full Article)

      Comment Mentions:   Analytics

    12. Anomalies as Unexpected or Rare Events by Time of Day

      Explore Anomaly Detection Analytics (Dec 31 2014)

      Anomalies as Unexpected or Rare Events by Time of Day

      This discussion deals with the uniqueness of an event occurring at a specific time, such as a user logging in at an unexpected time of day for that user...

      (Read Full Article)

      Comment Mentions:   Prelert

    13. The Secrets to Successful Data Mining

      Explore Anomaly Detection Analytics (Dec 16 2014)

      The Secrets to Successful Data Mining

      Today's IT environments are so complex that IT and DevOps teams cannot reasonably set thresholds and alerts across all meaningful metrics. Even the much-touted best practice of employing standard deviations fails when the actual distribution of values is not Gaussian. Trying to manually define anomalies in increasingly complex systems can result in a deluge of false alerts that waste your IT team's valuable time. In fact, in a random sampling of Splunk users, almost half did not bother setting thresholds for fear of creating additional "alert noise." Scary, huh?

      (Read Full Article)

      Comment Mentions:   Enterprise Management Associates

    14. Why IT Security Teams Need Machine Learning

      Explore Anomaly Detection Analytics (Dec 12 2014)

      Why IT Security Teams Need Machine Learning

      It turns out that finding anomalies in huge volumes of data is exactly what Big Data analytics approaches, such as unsupervised machine learning, are good at...

      (Read Full Article)

      Comment

    15. Security Analytics - Anomaly Detection for IT Security

      Explore Anomaly Detection Analytics (Dec 5 2014)

      Security Analytics - Anomaly Detection for IT Security

      In this short (1:25) video, IT Security professionals protect from advanced threats with security analytics. Machine learning anomaly detection finds the fingerprints of criminial activity in real-time so to detect problems early and act fast...

      (Read Full Article)

      Comment Mentions:   IT Security   Analytics

    16. Data Mining: Don't Settle for Monitoring 1% of Your IT Operations Data

      Explore Anomaly Detection Analytics (Dec 4 2014)

      Data Mining: Don't Settle for Monitoring 1% of Your IT Operations Data

      Do you have the whole automation vs. data mining thing backwards? Traditional IT monitoring approaches automatically analyze less than 1% of the data available looking for 'known bad' behaviors. When a problem is found, an alert is raised that tells us what happened. Troubleshooting teams then have to manually ‘mine’ the other 99% of the data to find out why there was an alarm in the first place. No wonder recent surveys on the state of IT operations verify that two of the biggest concerns are "time spent troubleshooting" and "problems reported by users before IT knows about them."

      (Read Full Article)

      Comment Mentions:   Troubleshooting

    17. Ensure Compliance With IT Operations Analytics

      Explore Anomaly Detection Analytics (Dec 3 2014)

      Ensure Compliance With IT Operations Analytics

      With ITOA (IT Operations Analytics), companies no longer have to remain well-read on each potential flaw or hack that has been concocted and can set up the technology to look for users that are operating outside the definition of normal. The automated nature of ITOA technology frees a company's IT team from having to set thresholds, develop signatures, or just manually search for abnormal behaviors. The ability of ITOA technology to adapt with speed and accuracy minimizes false positives and provides an organization with the resource it needs to weed out any rogue marketers before they get a chance ...

      (Read Full Article)

      Comment Mentions:   Analytics

    18. Implementing StatsReduce in Anomaly Detective

      Explore Anomaly Detection Analytics (Nov 20 2014)

      Implementing StatsReduce in Anomaly Detective

      One of the major additions to version 3.3 of Prelert Anomaly Detective ® for Splunk was a feature called StatsReduce. This feature enables Anomaly Detective to take advantage of Splunk’s distributed processing to analyse immense volumes of data quickly enough to deliver real-time insights. The addition of StatsReduce mode to our Anomaly Detective for Splunk makes it the sole native Splunk app that can deliver real-time analytics for data big enough to require a distributed Splunk installation to store it.

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      Comment

  1. Recent Articles for IT Ops & APM

    1. AppDynamics Announces Settlement of Litigation with CA, Inc.

      Explore Yahoo! Finance (Apr 19 2015)

      AppDynamics today announced that it has entered into a mutual settlement agreement with CA, Inc., that resolves all pending litigation between them, including a patent case that CA

      (Read Full Article)

      Comment Mentions:   APM

    2. What’s slowing down your network and how to fix it

      Explore ComputerWeekly.com (Apr 17 2015)

      When using the network feels like wading through molasses, finding the cause can be a difficult process

      (Read Full Article)

      Comment Mentions:   IBM   Microsoft   APM

    3. APM in the New Hybrid World

      Explore APMdigest (Apr 16 2015)

      True Application Performance Monitoring cross-cuts many IT tiers: network infrastructure, physical and virtual infrastructure, databases, mobile devices, etc. An ideal Application Performance Monitoring solution provides visibility over any infrastructure, for any app and any audience ...
      (Read Full Article)

      Comment Mentions:   Application Performance Management   APM   Application Performance Monitoring

    4. CMDB Systems in the Age of Cloud and Agile - Why We Wrote the Book

      Explore APMdigest (Apr 15 2015)

      CMDB Systems — as we have come to understand them — require a way of reconciling multiple sources into a modeled view of physical and logical interdependencies. This is becoming an increasingly automated and dynamic capability in many deployments. Moreover as a federated set of resources, CMDB Systems may in some cases involve mashups as much as data stores for certain use cases.

      (Read Full Article)

      Comment Mentions:   APM   Enterprise Management Associates

  2. Recent Articles for Security Analytics

    1. VCs Pour Money Into Cybersecurity Startups - WSJ

      Explore wsj.com (Apr 20 2015)

      Startups are taking different approaches to detecting and understanding threats. Some, for instance, are using machine learning—computers reacting to and anticipating new situations without reprogramming.

      (Read Full Article)

      Comment Mentions:   Analytics

    2. Businesses are out of excuses on cyber security analytics

      Explore searchbusinessanalytics.techtarget.com (Apr 18 2015)

      Breaches have become a fact of life, and most businesses' response to the problem has been insufficient. It doesn't have to be this way. Cybersecurity analytics has the potential to cut down on data breaches and limit the amount of valuable data hackers are able to abscond with.

      (Read Full Article)

      Comment Mentions:   Analytics

    3. Predictive Analytics: The Future Is Now - Dark Reading

      Explore Dark Reading (Apr 15 2015)

      Vincent Weafer, Senior Vice President of Intel Security who manages more than 350 researchers across 30 countries, says:

      "Is the market ready for these tools? Not quite."

      (Read Full Article)

      Comment Mentions:   Analytics

    4. Prelert’s CEO Mark Jaffe to Share Insights at AGC’s West Coast Technology Conference

      Explore promotedstories.com (Apr 15 2015)

      Prelert, the leading provider of machine learning anomaly detection, today announced that its CEO, Mark Jaffe, will be presenting at AGC Partners’ annual West Coast Technology Conference, taking part April 20-21, 2015, at the Park Central Hotel in San Francisco, CA. Executives from the company, including Mike Paquette , vice president of security products, will be on-site at the event to discuss why companies need to shift their security thinking from one of prevention to one of detection.

      (Read Full Article)

      Comment Mentions:   Prelert   Mark Jaffe   Analytics

  3. Recent Articles for Big Data in IT

    1. ​Emerging Trends in Big Data Analysis for 2015

      Explore dezyre.com (Apr 17 2015)

      Here's a brief summary:

      1) Big Data Analysis to drive Datafication

      Eric Schmidt, Executive Chairman at Google says: “From the dawn of civilization until 2003, humankind generated five Exabyte’s of data. Now we produce five Exabyte’s every two days

      2) Big Data Analytics to gain power of novel Security tools

      Technology information source week recently published an update about Niara, which is building its big data security analytics platform to detect many sophisticated threats that existing security tools cannot detect. 

      3) Deep Learning soon to become the buzz word in Big Data Analysis

      Google’s latest deep ...

      (Read Full Article)

      Comment Mentions:   IBM   Google   Amazon

    2. Microsoft Closes Acquisition of Revolution Analytics - Machine Learning

      Explore TechNet Blogs (Apr 6 2015)

      Joseph Sirosh , Corporate Vice President of Information Management & Machine Learning at Microsoft. said,

      "R is the world’s most popular programming language for statistical computing and predictive analytics, used by more than 2 million people worldwide. Moving forward, we will build R and Revolution’s technology into our data platform products so companies, developers and data scientists can use it across on-premises, hybrid cloud and Azure public cloud environments."

      (Read Full Article)

      Comment Mentions:   Microsoft   Big Data   Azure

    3. Big gaps and Big Data

      Explore World Bank Blogs (Apr 6 2015)

      As someone who works with large data sets within a global development institution, I'm a firm believer in the power of real-time data. But our analytics must be backed by credible hypotheses and sound social science. 

      (Read Full Article)

      Comment Mentions:   Google   Big Data   Analytics

    4. The Data Lake Debate, Part 4

      Explore smartdatacollective.com (Apr 6 2015)

      While the idea of a data lake sounds like fun, don’t go jumping in just yet. There are critical factors to consider before taking the plunge and saying that A data lake is essential for any organization to take full advantage of its data. In presenting the following arguments, I not only contend that a data lake is not essential for any organization, I also argue that creating a data lake will in fact be detrimental for those who do so prematurely.

      (Read Full Article)

      Comment Mentions:   Google   Hadoop

  4. Recent Articles for Machine Learning Analytics

    1. IoT Won’t Work Without Artificial Intelligence

      Explore Wired.com (Nov 13 2014)

      In an IoT situation, machine learning can help companies take the billions of data points they have and boil them down to what’s really meaningful. The general premise is the same as in the retail applications – review and analyze the data you’ve collected to find patterns or similarities that can be learned from, so that better decisions can be made.

      (Read Full Article)

      Comment Mentions:   Artificial Intelligence   Mark Jaffe

    2. Making Sense of IoT Data With Machine Learning Technologies - Forbes

      Explore forbes.com (Sep 4 2014)

      As companies embark on the long journey of harvesting large amounts of data from connected devices and sensors, the valuable insights hidden in the data are driving up costs and not adding to the bottom line. How can these companies get these insights to market faster while reducing the risk of project failure? One way is to leverage the expertise of companies whose core competency is machine learning. One interesting use case comes from Prelert, a self-described anomaly detection company...

      (Read Full Article)

      Comment Mentions:   Prelert

    3. Sophie Chang Named VP of Engineering at Preler

      Explore businesswire.com (Aug 12 2014)

      Prelert , the anomaly detection company, today announced that it has hired Sophie Chang as Vice President of Engineering to lead its U.K.-based engineering team. In this role, Chang will be responsible for product development and managing all aspects of the team’s activities, helping to enhance Prelert’s machine learning-based anomaly detection engine. Chang brings more than ten years of senior executive experience to her new role, most notably through her time as VP Software at 1E, a fast-growing and successful B2B IT efficiency software company. She was responsible for growing its technology team from two people to ...

      (Read Full Article)

      Comment Mentions:   Prelert   Mark Jaffe   Dr. Steve

    4. How Machine Learning Is Improving Computer Security

      Explore smartdatacollective.com (Jul 27 2014)

      The machine learning approach has a major advantage over the more traditional way of threat detection. With the traditional way, systems had to look for signatures that had already been determined to be a threat. Once these signatures were identified within a network, the system would have to either stop it from further infiltration, or eliminate it. This method has some rather obvious weaknesses, the main one being its non-predictive nature. Machine learning is able to address this major weakness by looking through data for certain patterns and signals, thus predicting future attacks and preventing them, letting the system stay ...

      (Read Full Article)

      Comment

  5. Recent Articles

    1. Frustrated security pros try a new approach: Let the bad guys in ...

      Explore SiliconANGLE (Apr 10 2015)

      The approach is certainly promising, but there’s no guarantee that more sophisticated attackers won’t be able to find a way to slip past this new line of defense. After all, computer security is a never-ending game of cat-and-mouse, with cybercriminals constantly coming up with new ways to beat the latest security systems, and forcing vendors to come up with ever more sopshisticated solutions in response ...

      (Read Full Article)

      Comment Mentions:   Google   Computerworld   Network World

    2. Analytics Drive New Approaches to IT Security

      Explore AllAnalytics (Apr 9 2015)

      Anyone working in corporate information security today knows the landscape has changed greatly. The laser-like focus on traditional security schemes such as incident response and multi-factor authentication has given way to a growing emphasis being placed on better understanding threat profiles, prioritizing assets and deducing behavioral patterns.

      (Read Full Article)

      Comment Mentions:   IT Security   Analytics

    3. Botnet activity inside organisations predicts likelihood of future data breach

      Explore techworld.com (Apr 9 2015)

      The 1,536 organisations with the lowest grade of botnet activity (grade A) turned out to have suffered breaches on 26 occasions (1.7 percent of the total) while the 4,536 organisations showing higher levels of botnets (grade B) had suffered breaches on 172 occasions (a 3.7 percent incidence). The figures suggest that firms with higher botnet activity were on the basis of this sample 2.2 times more likely to have suffered a data breach, a statistically significant contrast.

      (Read Full Article)

      Comment Mentions:   Analytics   Breach

    4. Three reasons to deploy security analytics software in the enterprise

      Explore searchsecurity.techtarget.com (Mar 25 2015)

      Before deploying a security analytics tool, it helps to understand how such a product will fit within an organization's other security controls and the gaps it will help fill in typical IT security use cases.

      (Read Full Article)

      Comment Mentions:   Analytics

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