Behavioral analytics

Abstract: This paper aims to explore the cyber-deception-based approach and to design a novel conceptual model of hybrid threats that includes deception methods. Security programs primarily focus on prevention-based strategies aimed at stopping attackers from getting into the network. These programs attempt to use hardened perimeters and endpoint defenses by recognizing and blocking malicious activities to detect and stop attackers before they can get in. Most organizations implement such a strategy by fortifying their networks with defense-in-depth through layered prevention controls. Detection controls are usually placed to augment prevention at the perimeter, and not as consistently deployed for in-network threat detection. This architecture leaves detection gaps that are difficult to fill with existing security controls not specifically designed for that role. Rather than using prevention alone, a strategy that attackers have consistently succeeded against, defenders are adopting a more balanced strategy that includes detection and response. Most organizations deploy an intrusion detection system (IDS) or next-generation firewall that picks up known attacks or attempts to pattern match for identification. Other detection tools use monitoring, traffic, or behavioral analysis. These reactive defenses are designed to detect once they are attacked yet often fail. They also have some limitations because they are not designed to catch credential harvesting or attacks based on what appears as authorized access. They are also often seen as complex and prone to false positives, adding to analyst alert fatigue. The security industry has focused recent innovation on finding more accurate ways to recognize malicious activity with technologies such as user and entity behavioral analytics (UEBA), big data, artificial intelligence (AI), and deception.

Abstract: Contagious diseases pose significant challenges to public healthcare systems all over the world. The rise in emerging contagious and infectious diseases has led to calls for the use of new techniques and technologies capable of detecting, tracking, mapping and managing behavioral patterns in such diseases. In this study, we used Big Data technologies to analyze two sets of flu (influenza) activity data: Twitter data were used to extract behavioral patterns from a location-based social network and to monitor flu outbreaks (and their locations) in the US, and Cerner HealthFacts data warehouse was used to track real-world clinical encounters. We expected that the integration (mashing) of social media and real-world clinical encounters could be a valuable enhancement to the existing surveillance systems. Our results verified that flu-related traffic on social media is closely related with actual flu outbreaks. However, rather than using simple Pearson correlation, which assumes a zero lag between the online and real-world activities, we used a multi-method data analytics approach to obtain the spatio-temporal cross-correlation between the two flu trends and to explain behavioral patterns during the flu season. We found that clinical flu encounters lag behind online posts. Also, we identified several public locations from which a majority of posts initiated. These findings can help health authorities develop more effective interventions (behavioral and/or otherwise) during the outbreaks to reduce the spread and impact, and to inform individuals about the locations they should avoid during those periods.

Abstract: The present invention relates to business-to-business marketing organizations who participate in lead-generation activities via their company website. More particularly, the invention provides a target lead-generation system and method that targets the right businesses using real-time predictive and behavioral analytics and website traffic data and connects businesses to potential customers and suppliers to drive business revenue. Even more particularly, the invention provides a system and method for real-time searching and matching of data input into website registration forms by website visitors, provides for real-time cleansing and appending of attribute rich company demographic and firmographic data to the website form and to the marketing database. The resulting information is then available for use by other systems such as marketing automation systems and CRM systems.