Abstract: The present disclosure relates to a plus shaped packaging paper [100]. The plus shaped packaging paper [100] includes a plus shaped first flat layer [102] of a first paper material and a plus shaped second fluted layer [104] of a second paper material. The plus shaped second fluted layer [104] is attached to the plus shaped first flat layer [102] to form the plus shaped paper packaging paper [100] having a set of adjacent sides. Further, the plus shaped packaging paper [100] includes a set of diagonal cuts [106], wherein each diagonal cut from the set of diagonal cuts [106] is present in between a corner formed by two adjacent sides of the set of adjacent sides.

Abstract: A payoff matrix based collaborative monitoring model presents a formal framework for defining policies to assign different payoffs for different subjects corresponding to their reporting behavior against different policy violations. An embodiment such as a formal model can be used by security administrators to get better estimates on various factors affecting the required parameters controlling the payoff values, e.g., reporting behavior of users, group dynamics, characteristics of the violations, and likelihood of detection. The proposed model effectively complements the payoff matrix-based approach for enabling the collaborative monitoring of policy violations.

Abstract: A reactive approach to enterprise threat management provides a solution to the problem of prioritizing security violations. In an embodiment, a linear adaptive learning approach is aimed towards a system which could effectively assist security administrators to prioritize reported violations. The approach is adaptive in the sense that the system can change its logic over a course of time controlled only by some specified structural constraints. A learning aspect specifies that any mismatch between a system's response and the response of a security expert is propagated back to the system for adapting the difference such that the responses of the system should increasingly match against the security expert's responses over time. The presented algorithm learns and predicts simultaneously, continually improving its performance as it makes each new prediction and finds out how accurate it is.

Abstract: A computer implemented system and method is used to receive user reports regarding potential security policy violations that describe observations by the user, the type of policy violation, and an identification of another user with potential knowledge of a security policy violation. A payoff matrix may be formed for each user submitting a user report regarding potential as well as actual security violations and for users identified in such reports, wherein the payoff matrix reflects payout data for reported and unreported security policy violations. The payoff matrix may be used to both reward and punish reporting behaviors.