1. Prelert Blog

    1. Bringing Alert Management into the Present with Advanced Analytics

      Explore Anomaly Detection Analytics (May 12 2015)

      Bringing Alert Management into the Present with Advanced Analytics

      Despite producing huge volumes of alerts, rules and thresholds implementations often miss problems or report them long after the customer has experienced the impact. The fear of generating even more alerts forces monitoring teams to select fewer KPIs, thus decreasing the likelihood of detection. Problems that slowly approach thresholds go unnoticed until user experience is already impacted. Adopting this advanced analytics approach empowers enterprises to not only identify problems that rules and thresholds miss or simply execute against too late, but also provide their troubleshooting teams with pre-correlated causal data.

      (Read Full Article)

      Comment Mentions:   Analytics

    2. Excluding Frequent Occurrences for Smarter Anomaly Detection

      Explore Anomaly Detection Analytics (May 5 2015)

      Excluding Frequent Occurrences for Smarter Anomaly Detection

      In this article, we’ll discuss the rationale behind the ability to automatically exclude frequently observed entities from analysis and it’s applicability as an alternative to “whitelisting.'

      (Read Full Article)


    3. 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

    4. 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

    5. 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)

      (Read Full Article)


    6. 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

    7. 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

    8. 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)


    9. 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

    10. 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

    11. 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

    12. 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

    13. 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

    14. 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

    15. 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

    16. 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)


    17. 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

    18. 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

  1. Recent Articles for IT Ops & APM

    1. APM for Enterprise: How Does It Scale?

      Explore APMdigest (May 18 2015)

      It could be argued that an organization with tens of thousands of JVMs and millions of metrics has a fundamentally different issue than those closer to the base of the pyramid. Certainly these organizations are fewer in number, but that is scant comfort for those with the responsibility of managing their application delivery. Whether in banking/financial trading, FMCG or elsewhere, the issue of effectively analyzing daily transaction flows at high scale is real.

      (Read Full Article)

      Comment Mentions:   Google   Application Performance Management   APM

    2. IoT Ultimate Test for APM

      Explore APMdigest (May 11 2015)

      While there are plenty of industry forecasts projecting hyper-growth of the IoT market, Cisco's forecasts are sufficient to clearly show that APM players and users are going to be severely tested by this unprecedented surge in connected things...


      (Read Full Article)

      Comment Mentions:   Cisco   Application Performance Management   APM

    3. Slow Applications Are Criminal

      Explore APMdigest (May 7 2015)

      Embracing a smart but simple APM Methodology within your environment may be the only thing that exonerates you when the verdict for your slow application is "guilty as charged."

      (Read Full Article)

      Comment Mentions:   Application Performance Management   Forrester   APM

    4. Delivering Value with BizDevOps

      Explore APMdigest (May 5 2015)

      As enterprise applications become more numerous, intertwined and complex, IT organizations are placing more emphasis than ever on finding new approaches to manage applications and optimize their availability and performance. As a result, Application Performance Management (APM) has become an essential part of enterprise IT framework since it directly involves all key stakeholder groups, including application owners, application developers and application users...

      (Read Full Article)

      Comment Mentions:   Application Performance Management   Forrester   APM

  2. Recent Articles for Security Analytics

    1. Security analytics scores high in value, low in penetration

      Explore CIO.com (May 19 2015)

      Security analytics had the highest perceived value compared to its cost, according to a survey of information security professionals released Monday. But it scored next to last in penetration. It's a very new technology, explained David Monahan, research director at Enterprise Management Associates, the research firm that did the study. However, there have been significant advancements in both machine-learning algorithms and analysis techniques over the past two or three years, he said.

      (Read Full Article)

      Comment Mentions:   Enterprise Management Associates   Analytics

    2. Infographic: Value of Security Analytics & Anomaly Detection

      Explore Anomaly Detection Analytics (May 19 2015)

      This infographic features highlights from the EMA research report, Data-Driven Security Reloaded.

      (Read Full Article)

      Comment Mentions:   Analytics

    3. Traditional security approaches produce too many false positives

      Explore Betanews (May 19 2015)

      'Alert blindness' on traditional systems continues to be a major issue, with 62 percent seeing too many false positives or having too many alerts to handle, with the result that they don't feel confident in the security protections they have in place. Another 38 percent say they aren't confident because there's too much uncorroborated data and a lack of context about that data.

      (Read Full Article)


    4. Maturing NoSQL database security is key to big data analytics

      Explore searchsecurity.techtarget.com (May 18 2015)

      Big data security analytics systems based on NoSQL databases and Hadoop processing systems are taking hold in a growing number of enterprises. While to date, big data systems have largely prioritized performance, experts say NoSQL database security is increasingly important, and the technology to support security for these nascent systems is poised to take a big step forward.

      (Read Full Article)

      Comment Mentions:   Big Data   Hadoop   Analytics

  3. Recent Articles for Big Data in IT

    1. The Data Lake Debate: Conclusion

      Explore smartdatacollective.com (May 4 2015)

      On the one hand, the data lake presents a fresh and practical solution for easier data access, loading, cleansing, provisioning, and archiving, freeing companies from the yoke of traditional relational database systems and their accompanying processing and labor-intensive infrastructures.

      But on the other hand, the data lake is still only a component in an overall data ecosystem that includes data management and governance, quality and master data management solutions, and loading and provisioning standards. And, Anne insists, it need not include Hadoop.

      (Read Full Article)


    2. Big Data & The Security Skills Shortage

      Explore Dark Reading (Apr 29 2015)

      One issue is the hundreds or thousands of security incident alerts organizations receive every day -- the vast majority of which are not malicious activity or targeted attacks. Differentiating between true, targeted attacks and non-malicious incidents is extremely difficult unless security analysts are armed with the skills and tools they need to make them entry-level data scientists.

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

    3. Top reasons Big Data security analytics should be on your strategy roadmap

      Explore Network & IT Systems Monitoring (Apr 27 2015)

      In the following we’d like to take a closer look at the implications of Big Data security analytics – what it means for your company and why it matters for the future of your strategy roadmap.

      (Read Full Article)

      Comment Mentions:   Big Data   BYOD   Analytics

    4. ​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

  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)


  5. Recent Articles

    1. Is Big Data Worth Its Weight In Security Gold?

      Explore emc.com (May 18 2015)

      View the infographic...

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

    2. Gartner: Security analytics — a new hope for security, or just hype?

      Explore itworldcanada.com (May 15 2015)

      Analytics systems, on average, tend to do better analyzing lean, or metadata-like, data stores that allow them to quickly, in almost real-time speed, produce interesting findings. The challenge to this approach is that major security events, such as breaches, don’t happen all at once. There may be an early indicator, followed hours later by a minor event, which in turn is followed days or months later by a data leakage event.

      (Read Full Article)

      Comment Mentions:   Analytics

    3. 5 Secrets to DevOps Success

      Explore Information Management (May 12 2015)

      Follow this 5-step guide -- highlighting the right balance of people, processes and tools -- to bring your Dev and Ops teams together.

      (Read Full Article)

      Comment Mentions:   IDC   Analytics

    4. Which to use for security analytics, IPFIX or sFlow?

      Explore Network Visibility, Analytics & Security (May 8 2015)

      Given the rapid evolution in the nature of malware, the ability to customize what fields within each packet to look at and what information to include within the flow metadata records  significantly expands the ability of the cyber security professional to derive more meaningful, more timely and more relevant information.

      (Read Full Article)

      Comment Mentions:   Analytics

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