1. Prelert Blog

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

      Explore Anomaly Detection Analytics (22 hours, 38 min ago)

      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

    2. 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

    3. 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

    4. 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

    5. 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

    6. 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...

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      Comment

    7. 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

    8. 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

    9. 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

    10. 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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    11. Anomaly Detection on Large Data Sets via Aggregation

      Explore Anomaly Detection Analytics (Nov 18 2014)

      Anomaly Detection on Large Data Sets via Aggregation

      When dealing with very large data sets, there are various practical obstacles, which aren't present at smaller scale, to getting conventional anomaly detection algorithms to work. A key one is the concept of data inertia. This is simply that it is impractical or even impossible to transport the entire data set to a single process. Often, in this context, we have to process a distributed collection of data streams, and we simply don’t have the bandwidth to copy all these data to one process. Therefore, we would like to be able to perform anomaly detection at the level ...

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    12. IoT Won’t Work Without Artificial Intelligence

      Explore Anomaly Detection Analytics (Nov 14 2014)

      IoT Won’t Work Without Artificial Intelligence

      As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of the year, the discussion has turned from what it is, to how to drive value from it, to the tactical: how to make it work. IoT will produce a treasure trove of big data. This data will hold extremely valuable insight into what’s working well or what’s not – pointing out conflicts that arise and providing high-value insight into new business risks and opportunities as correlations and associations are made. It sounds great. However, the big problem will be finding ways ...

      (Read Full Article)

      Comment Mentions:   Artificial Intelligence

    13. How to Find Anomalies in Splunk's Internal Performance

      Explore Anomaly Detection Analytics (Nov 10 2014)

      How to Find Anomalies in Splunk's Internal Performance

      Splunk does a great job of keeping track of its own internal logs and performance information, and there’s even a very useful and concise app called “S.o.S” (Splunk on Splunk), which tracks and reports on a variety of items culled from Splunk’s “_internal” index and a variety of source logs such as the splunkd.log file. But, just like any visual report or dashboard, there are some fundamental limitations to making this data proactive. Anomaly Detective makes it ridiculously easily to bring machine learning-based anomaly detection to your Splunk on Splunk data!

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    14. Prelert Closes $7.5M Investment from Intel Capital and Existing Investors

      Explore Yahoo! Finance (Nov 4 2014)

      Prelert , the anomaly detection company, today announced it has raised a $7.5 million round of venture capital financing from Intel Capital and existing investors, Fairhaven Capital and Sierra Ventures . This investment will enable Prelert to further expand its field sales and engineering organizations to leverage the growing interest in Anomaly Detective® from enterprises, cloud service providers and IT management providers...

      (Read Full Article)

      Comment Mentions:   Intel   CA Technologies   APM

    15. C++11 mutex implementations

      Explore Anomaly Detection Analytics (Nov 3 2014)

      C++11 mutex implementations

      C++11 brought concurrency to standard C++ for the first time. Prior to this the only choice for writing multi-threaded C++ programs was to use a separate C++ library, such as Boost Thread or Intel Thread Building Blocks , or roll your own wrappers around the low-level operating system facilities, such as POSIX threads or Windows threads . Last year I looked into the performance of different types of locks on different platforms. The variation in performance is surprisingly wide. Prelert’s codebase pre-dates C++11, so we have our own wrappers around the low-level operating system facilities. Here are the ones ...

      (Read Full Article)

      Comment Mentions:   Intel

    16. Machine Data is Different (and Why It Matters)

      Explore Anomaly Detection Analytics (Oct 13 2014)

      Machine Data is Different (and Why It Matters)

      Why is machine data different? To answer this question, let’s start by considering different perspectives on what constitutes unstructured data. In the case of a pre-defined set of allowed classifications, the standard approach for benchmarking a machine-learnt classification against a human-generated correct result is to use a confusion matrix . Each of the allowed classifications corresponds to a row and a column in the matrix, and the cells in the matrix record the number of input messages with corresponding human-generated classification and machine-determined classification. The perfect outcome is for all cells of the matrix to contain zeroes except those on ...

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      Comment Mentions:   Prelert

    17. Ready to Talk Anomaly Detection & Advanced Math at Splunk's User Conference

      Explore Anomaly Detection Analytics (Oct 6 2014)

      Ready to Talk Anomaly Detection & Advanced Math at Splunk's User Conference

      The Prelert team, along with partners and customers, will share insights on using machine-based anomaly detection to find value in Big Data in front of over 4,000 IT and business professionals at Splunk’s fifth annual Worldwide Users’ Conference, .conf2014. The event will take place from October 6-9 at the MGM Grand in Las Vegas, Nevada.

      (Read Full Article)

      Comment Mentions:   Prelert   Big Data   Dr. Steve

    18. Anomaly Detection to Reduce the Noise

      Explore Anomaly Detection Analytics (Oct 2 2014)

      Anomaly Detection to Reduce the Noise

      If you have followed some of my other recent blogs, you’ll have noticed that automated anomaly detection is a great technique to find anomalous behaviors in data by effectively contrasting the difference between “normal” and “abnormal. " Most people equate this with contrasting between “good” and “bad,” but that isn’t always necessarily true. What if the data set you’re looking at are “all bad things,” such as Intrusion Detection (IDS) alerts?

      (Read Full Article)

      Comment Mentions:   IT Security

  1. Recent Articles for IT Ops & APM

    1. Gartner Gives Dynatrace the Highest Scores for Application Performance Monitoring in 3 of 5 Use Cases

      Explore Press Release Distribution (Jan 21 2015)

      Dynatrace , (formerly Compuware APM) and the market leader in the new generation of application performance management (APM)...

      (Read Full Article)

      Comment Mentions:   Google   Application Performance Management   APM

    2. Five steps for sustaining rigorous network security and performance

      Explore Independent Banker (Jan 9 2015)

      Systems can generate a large number of alerts [causing] staffers [to] be flooded with too many alerts to deal with. In addition to zero-day attacks, [some] systems can also detect unusual behavior by employees. Kevin Conklin, vice president of marketing and product strategy at Prelert Inc., an anomaly detection software company said:

      “It’s called unsupervised machine learning. It will learn what are normal patterns of behavior and focus on the things that are abnormal”...

      (Read Full Article)

      Comment Mentions:   Prelert   Kevin Conklin   IT Security

    3. The Art of War: Military lessons for IT security

      Explore Business Spectator (Jan 8 2015)

      The Chief Security Officer of Oracle, Mary Ann Davidson, talks about securing the Internet of Things, M2M and understanding the difference between capability and intent...

      (Read Full Article)

      Comment Mentions:   Oracle   CIO   IT Security

    4. Top 47 Log Management Tools

      Explore ProfitBricks Blog (Jan 2 2015)

      The following 47 log management tools are listed in random order, not in order of performance or capabilities. The numbers are provided as a simple reference tool, but are not meant to imply a quality-ranking system. Every enterprise and organization is different, and while some tools are more flexible than others, each organization will have distinct requirements and preferences. There are log management tools spanning practically every use scenario and configuration, so this list is intended to be a central resource for comparing various appliances against your unique specifications...

      (Read Full Article)

      Comment Mentions:   Cisco   Cloud Computing

  2. Recent Articles for Security Analytics

    1. Prelert Expands Executive Team with Key Security Veterans in Response to Increased Demand for Advanced Analytics in the Fight against Cybercrime

      Explore Yahoo! Finance (Jan 20 2015)

      Prelert, the leading provider of machine learning anomaly detection, today announced that it has added two executives to its team in response to the strong demand for its advanced analytics products in the IT security market. Mike Paquette, vice president of security products, and Oleg Kolesnikov, senior director of cyber security and head of security analytics, will reinforce Prelert’s leadership in providing machine learning anomaly detection solutions that can identify advanced threats and data breaches before they result in large data losses.

      (Read Full Article)

      Comment Mentions:   Mark Jaffe   IT Security   Analytics

    2. Gartner: Security Analytics – Finally Emerging For Real?

      Explore Gartner Blog Network (Jan 12 2015)

      So, there is still no market called “security analytics” , but there are some areas where specificity is finally emerging (yay!). Below you will see two areas where the label of “security analytics” may actually apply in real life, and not in the realm of marketing wet dreams

      (Read Full Article)

      Comment Mentions:   Analytics

    3. Big Data Security Analytics Infographic

      Explore finland.emc.com (Jan 12 2015)

      How organizations seeking to maximize their security intelligence use big data security analytics...

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

    4. Big data security analytics still immature, say security experts

      Explore Linkis.com (Jan 9 2015)

      The US Computer Emergency Response Team (US-CERT) has yet to achieve its vision for big data security analytics , said Peter Fonash, CTO for the cyber security office at the US Department of Homeland Security. But suppliers said big data security analytics is already delivering value by enabling organisations to analyse data from previously disconnected security data sources...

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

  3. Recent Articles for Big Data in IT

    1. Navigating the maze of Cyber Security Intelligence and Analytics

      Explore Anything Connected (Jan 2 2015)

      Information compiled in a relatively recent Verizon Data Breach Investigations Report points out that data is stolen within hours in 60% of breaches, but goes undetected for months in 54% of overall breaches. With the mounting pace of attacks, and increasing IT attack surface due to constant influx of new technologies such as cloud computing, BYOD and virtualization, CISOs are looking for real-time data analytics to improve threat defense through better controls, reliably detect an incident and quickly contain the breach before it inflicts an inordinate amount of damage, and also provide insight into extent of data exfiltration to quantify ...

      (Read Full Article)

      Comment Mentions:   IBM   Google   Amazon

    2. 5 big data trends to watch out for in 2015

      Explore Global Big Data Conference (Jan 1 2015)

      At the end of 2013, the industry was still debating whether Hadoop and related big data technologies were going to become mainstream or were just niche technologies for Internet companies. A year later, the answer is clear — Hadoop is without question the foundation of the new data stack, the first of the Hadoop distributions (Hortonworks) is now a public company, with others sure to follow. This is putting a spotlight on the next layer up the stack — big data analytics — and the use cases that will be unlocked and transformed by collecting and connecting vast quantities of raw data and ...

      (Read Full Article)

      Comment Mentions:   Gartner   Big Data   Hadoop

    3. Big Data Analytics: Time For New Tools - InformationWeek

      Explore DragPlus Social media trends follower (Dec 18 2014)

      Doug Henschen, Executive Editor of InformationWeek said:

      "The top driver, cited by 48% of respondents using or planning to deploy data analytics, BI, or statistical analysis software, is finding correlations across multiple, disparate data sources, like Internet clickstreams, geospatial data, and customer-transaction data...Other motivations include predicting fraud and financial risks, analyzing social network comments for customer sentiment, and identifying security risks."

      (Read Full Article)

      Comment Mentions:   InformationWeek   Big Data   Analytics

    4. Big Data analytics to IT's rescue

      Explore Help Net Security (Dec 11 2014)

      How can an IT security organization ensure they are not the next target (excuse the pun)? It turns out there are common characteristics of successful attacks and that finding anomalies [caused by these breaches ] in huge volumes of data is exactly what Big Data analytics and unsupervised machine learning, are good at.

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

  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)

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  5. Recent Articles

    1. Five Steps for Better Security Analytics in 2015

      Explore Security Intelligence (Jan 6 2015)

      A large utility is typically “pinged” 1 million times every day by malicious parties. That sounds like a lot, but these attacks are rarely noticed because the same utility processes millions of events per second, offering plenty of cover. Current approaches are best suited to combat known threats. The challenge is finding new associations and uncovering patterns to identify clues about attacks such as advanced persistent threats , spear phishing and hacktivism. The following are five tips to help you protect your “city” in 2015:..

      (Read Full Article)

      Comment Mentions:   Big Data   Analytics

    2. Security Predictions for 2015 - Dataquest

      Explore Dataquest (Dec 26 2014)

      These 8 predictions are based on the latest IT trends that closely impact all of us since they co-exist in our personal as well as professional spaces like the Internet of Things, Geo-political interferences, attacks on iOS, newer authentication methods replacing traditional passwords, and more. New threat trends like malvertising, exploitation of legacy protocols, et al, also share the spotlight among the predictions for 2015...

      (Read Full Article)

      Comment Mentions:   Google   Big Data   Analytics

    3. 9 Tips on Using Big Data Analytics to Improve Security

      Explore Enterprise Apps Today (Dec 24 2014)

      In marketing, you use Big Data to identify trends or unmet needs that you can leverage to retain existing customers and acquire new ones. With security, it is the opposite. It is the unusual that matters...

      (Read Full Article)

      Comment Mentions:   EMC   Big Data   Hadoop

    4. Are you a Geek or a Nerd?

      Explore Anomaly Detection Analytics (Dec 23 2014)

      Are you a Geek or a Nerd?

      The holidays are a great time to ponder the bigger questions in life. And so it was on the last days before Christmas that Heather (my marketing cohort) and I recently pondered the serious issue of renaming the blog section dedicated to the deep issues of mathematics and coding techniques. Should it be 'For Math & Code Geeks' or 'For Math & Code Nerds?'.

      (Read Full Article)

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