We present Causal Active Inference with Real-time Interventions (CAIRN), a framework that models enterprise telemetry as a probabilistic causal graph and performs real-time Bayesian inference to estimate likely attack causes behind observed anomalies. When evidence is ambiguous, CAIRN triggers targeted interventions (e.g., decoy services, temporary isolation, or focused probes) to produce discriminative observations and reduce uncertainty. This closes the loop between detection and response: CAIRN tests competing hypotheses instead of flooding analysts with correlation-only alerts. In a testbed evaluation, CAIRN reduces false alarms and confirms attacks faster than a passive IDS baseline while maintaining low operational overhead.

In this article, we perform a large-scale study of the Twitter follower network, involving around 0.42 million users who justify DA, to characterize the spreading of DA tweets across the network. Our observations reveal the existence of a very large giant component involving 99% of these users with dense local connectivity that facilitates the spreading of such messages. We further identify active cascades over the network and observe that the cascades of DA tweets get spread over a long distance through the engagement of several closely connected groups of users. Moreover, our observations also reveal a collective phenomenon, involving a large set of active fringe nodes (with a small number of follower and following) along with a small set of well-connected nonfringe nodes that work together toward such spread, thus potentially complicating the process of arresting such cascades. Furthermore, we discovered that the engagement of the users with respect to certain drugs, such as Vicodin, Percocet, and OxyContin, that were observed to be most mentioned in Twitter is instantaneous. On the other hand, for drugs, such as Lortab, that found lesser mentions, the engagement probability becomes high with increasing exposure to such tweets, thereby indicating that drug abusers engaged on Twitter remain vulnerable to adopting newer drugs, aggravating the problem further.

The continuous decrease in the cost of mobile devices and mobile Internet has triggered an exponential growth of overall IP devices, which has consequently increased the demand for network mobility (NEMO), which is required to ensure seamless connectivity of the mobile devices with the Internet on the go. To support this growing demand, the concept of nested-NEMO has evolved over the past few years. However, due to the inherent architecture of nested-NEMO, it is inefficient in terms of hand-off delay and throughput which hinder it to deploy as a highly scalable network. In this paper, we address the scalability issue of nested-NEMO and propose a hierarchical node attachment process for the mobile devices. The performance of the proposed mechanism has been evaluated where we computed the hand-off delay and throughput and observed that the proposed mechanism supports a considerable degree of scalability as compared to the existing nested network mobility architecture.

Quantifying scientific productivity has traditionally been one of the major areas of research. An important component in measuring the scientific productivity of a researcher has been the number and citation count of his publications. However, that citation based measures of scientific productivity are influenced by domain specific factors like the popularity of the research domain, the key topics within the domain, as well as temporal factors like aging, these measures may not suitably reflect the contribution of a researcher uniformly across all domains. In this paper, we introduce social trust on a researcher in a given domain as a measure of his scientific productivity and success. We argue that trust in scientific domain is a social component that indicates the productivity and can influence several parameters like the collaborations of the researchers as well as the citations received by their publications. Unlike citation count of publications, trust is not domain specific and hence can be used uniformly across all domains to measure the scientific productivity. Our proposed measure of trust relies on a trust-based network of authors (nodes), where a link between two nodes is based on social indicators like co-authorship and citation counts. We validate the correctness as well as effectiveness of our proposed approach empirically using the ArnetMiner dataset. Observations indicate that the proposed measure not only mitigates the aging issues prevalent in citation based measures but can also predict the possibility of future success of the researchers in terms of citation count.