A closer look at Intrusion Detection System for web applications

Intrusion Detection System (IDS) is one of the security measures being used as an additional defence mechanism to prevent the security breaches on web. It has been well known methodology for detecting network-based attacks but still immature in the domain of securing web application. The objective of the paper is to thoroughly understand the design methodology of the detection system in respect to web applications. In this paper, we discuss several specific aspects of a web application in detail that makes challenging for a developer to build an efficient web IDS. The paper also provides a comprehensive overview of the existing detection systems exclusively designed to observe web traffic. Furthermore, we identify various dimensions for comparing the IDS from different perspectives based on their design and functionalities. We also provide a conceptual framework of an IDS with prevention mechanism to offer a systematic guidance for the implementation of the system specific to the web applications. We compare its features with five existing detection systems, namely AppSensor, PHPIDS, ModSecurity, Shadow Daemon and AQTRONIX WebKnight. The paper will highly facilitate the interest groups with the cutting edge information to understand the stronger and weaker sections of the web IDS and provide a firm foundation for developing an intelligent and efficient system

Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data

In medicine, one often bases decisions upon a comparative analysis of patient data. In this paper, we build upon this observation and describe similarity-based algorithms to risk stratify patients for major adverse cardiac events. We evolve the traditional approach of comparing patient data in two ways. First, we propose similarity-based algorithms that compare patients in terms of their long-term physiological monitoring data. Symbolic mismatch identifies functional units in long-term signals and measures changes in the morphology and frequency of these units across patients. Second, we describe similarity-based algorithms that are unsupervised and do not require comparisons to patients with known outcomes for risk stratification. This is achieved by using an anomaly detection framework to identify patients who are unlike other patients in a population and may potentially be at an elevated risk. We demonstrate the potential utility of our approach by showing how symbolic mismatch-based algorithms can be used to classify patients as being at high or low risk of major adverse cardiac events by comparing their long-term electrocardiograms to that of a large population. We describe how symbolic mismatch can be used in three different existing methods: one-class support vector machines, nearest neighbor analysis, and hierarchical clustering. When evaluated on a population of 686 patients with available long-term electrocardiographic data, symbolic mismatch-based comparative approaches were able to identify patients at roughly a two-fold increased risk of major adverse cardiac events in the 90 days following acute coronary syndrome. These results were consistent even after adjusting for other clinical risk variables.National Science Foundation (U.S.) (CAREER award 1054419

Exosomes: Salivary Biomarkers?

Saliva is a bio-fluid considered similar to blood in that it contains various DNAs, RNAs and proteins. Therefore, it is a fluid with diagnostic potential. In recent time, exosomes are emerging as nano-vesicles which enhance intra-cellular communication. Exosomal content, which is dependent on the cell of origin, reflects physiological status of cells. Exosomes have potentials for use as biomarkers for variant diseases, based on their stability and availability in various body fluids. Current studies have proposed the role of exosomes as immuno-modulators in the etiology of auto-immune diseases and cancers. The present study focused on the role of exosomes as biomarkers and their therapeutic potentials in particular diseases related to the oral cavity. Keywords: Exosomes, Auto-immune, Biomarkers, Saliva, Diagnosi

Interaction between population and Enviromental Degradation

The present study investigates the long-run interrelationship among the demographic variables and environmental indicators by using the Johansen-Juselius cointegration technique and error correction model to determine the short-run dynamics of the system related to time series data for Pakistan economy, over the period 1972–2001. The paper finds the existence of a cointegrating vector, indicating a valid long-run relationship among the variables. Moreover, demographic variables have a significant effect in the short-run on AL but their short-run coefficients have an insignificant impact on CO2 emission. The empirical evidence clearly support that high population growth rates have a deleterious impact on environment. The policy implication thereof is to enhance the need of lowering population in Pakistan which seems to be a burden on the existing resources and a challenge to the environment.

A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks

The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves efficient association and placement of the backhaul hubs that connects these small-cells with the core network. Terrestrial hubs suffer from an inefficient non line of sight link limitations and unavailability of a proper infrastructure in an urban area. Seeing the popularity of flying platforms, we employ here an idea of using networked flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned balloons flying at different altitudes, as aerial backhaul hubs. The association problem of these NFP-hubs and small-cell base stations is formulated considering backhaul link and NFP related limitations such as maximum number of supported links and bandwidth. Then, this paper presents an efficient and distributed solution of the designed problem, which performs a greedy search in order to maximize the sum rate of the overall network. A favorable performance is observed via a numerical comparison of our proposed method with optimal exhaustive search algorithm in terms of sum rate and run-time speed.Comment: Submitted to IEEE GLOBECOM 2017, 7 pages and 4 figure

Modeling Update Chacing in Weak Consistency Protocols

Computer Science