Deep learning which is also known as Deep Neural Network includes machine learning techniques that enable the network to learn from unsupervised data and solve complex problems. It can be extensively used for cybersecurity to protect companies from threats like phishing, spear-phishing, drive-by attack, a password attack, denial of service, etc. Learn about the top 10 applications of deep learning in cybersecurity. Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can be applied to create smarter ID/IP systems by analyzing the traffic with better accuracy, reducing the number of false alerts, and helping security teams differentiate bad and good network activities. Notable solutions include Next-Generation Firewall (NGFW), Web Application Firewall (WAF), and User Entity and Behavior Analytics (UEBA). Traditional malware solutions such as regular firewalls detect malware by using a signature-based detection system.

Artificial Intelligence (AI) is revolutionizing almost every industry. Deep Learning (DL), an AI methodology, is propelling the high-tech industry to the future with a seemingly endless list of applications ranging from object recognition for systems in autonomous vehicles to potentially saving lives -- helping doctors detect and diagnose cancer with greater accuracy. In this article, we'll outline some interesting applications of deep learning in cybersecurity and how you can use deep learning to improve security measures within your organization. Deep learning is a subtype of Machine Learning (ML) and belongs to the broader category of artificial intelligence. Deep learning uses Artificial Neural Networks (ANNs), which are designed to mimic the functionality and connectivity of neurons in the human brain. Deep learning gets its name because it uses deeper networks compared to other AI methods like ML.

Video surveillance is commonly associated with security. But in most cases, it's used to record incidents and assist in investigations after the fact rather than prevent undesirable events. Artificial intelligence–powered video analytics is a highly promising trend that fundamentally changes the way things work. Extracting manageable data from a video stream can help recognize risky situations early on, minimizing damage and, ideally, completely avoid emergencies. At the same time, AI significantly expands the areas of application of video surveillance beyond security systems.

Artificial intelligence is the new golden ring for cybersecurity developers, thanks to its potential to not just automate functions at scale but also to make contextual decisions based on what it learns over time. This can have big implications for security personnel--all too often, companies simply don't have the resources to search through the haystack of anomalies for the proverbial malicious needle. For instance, if a worker normally based in New York suddenly one morning logs in from Pittsburgh, that's an anomaly -- and the AI can tell that's an anomaly because it has learned to expect that user to be logging in from New York. Similarly, if a log-in in Pittsburgh is followed within a few minutes of another log-in by the same user from, say, California, that's likely a malicious red flag. So, at its simplest level, AI and "machine learning" is oriented around understanding behavioral norms.

Around 2010, security analytics technologies started to integrate big data science and open-source technologies like Hadoop (and HDFS), Pig, Mahout, etc. The goal? Ingest, process, and apply new types of algorithms to security data to supplement human intelligence for finding needles in growing haystacks of security data. The U.S. Department of Energy was an early pioneer in this area with a project called Orca from the Oak Ridge National Lab. Since then, big data security analytics sort of morphed into machine learning, which led to the creation of a new security technology category: user and entity behavior analytics (UEBA). UEBA was designed to monitor user behaviors such as logins, remote access, network connections, etc., model "normal" behavior, and then detect anomalies that may indicate an attack in progress.

As wealth managers look to AI for cost or UX benefits, what do they need to know about robo-advice, behavioral analytics or the value of clean data? Artificial intelligence (AI) is increasingly being used in wealth management as the catch-all term for next generation capabilities to attract and retain customers. From personalized portfolio management to customer behavior analytics, the potential for significant UX enhancements and cost reduction is vast. But progress is only possible if there's clean, well-organized data to start with. We take a look at the technological initiatives behind AI and how they apply to the wealth management industry.

Security analytics may hold promise. The reality is a ways off. One area ahead of the curve, however, is tracking inside-user behavior. User behavior analytics (UBA) relies on statistical modeling, machine learning and data science to identify patterns of behavior and compare them against other human or machine activities. These technologies develop normal versus abnormal behavior profiles by collecting information on users' activities across IP addresses, accounts and devices.

I've been involved in the data analytics and high-tech industries long enough to have seen plenty of new technologies subjected to a degree of hype so great they could never ever measure up. Some of these (fuzzy logic or Google Glass, anyone?) flamed out quickly; others, like artificial intelligence (AI), have had seesawing fortunes spanning decades -- here subject to the loftiest expectations only to be followed there by a'trough of disillusionment' (one of Gartner's hype-cycle stages, and a term I like) as physical, technical and other limitations became evident. Within the sub-domain of AI for security, a collection of technologies known as user behavior analytics (UBA) is now enjoying its own moment of high expectations, much as security information and event management (SIEM) systems did about a decade ago. UBA differs from SIEM in not just aggregating and correlating alerts from different network events but by using a combination of AI and analytical approaches -- including rules-based, pattern-matching and statistical methods, plus supervised and unsupervised machine learning -- to establish baselines of how systems, networks and devices typically behave, and then to detect significant anomalies in their behavior and send alerts to security teams for further investigation. Gartner industry analysts in particular have spent lots of time thinking about UBA.

Big data platform vendors are increasingly focusing on churning through unstructured data, especially for text, audio and even security applications like insider threat analysis. Among the companies emerging in this industry segment is Digital Reasoning, a well-connected cognitive computing company that has helped the U.S. military track terrorists online while working with financial markets to spot insider trading. The company, which recently expanded beyond the Capital Beltway to Nashville, rolled out the latest version of its Synthesys cognitive computing platform this week that combines machine learning, natural language processing, computer vision and pattern recognition. The combination is intended to boost the quality of unstructured data analysis while reducing the amount of time needed to get the desired results. Version 4 of the Digital Reasoning platform released on Tuesday (June 21) is based on proprietary analytics tools that apply deep learning neural network techniques across text, audio and images.