Studies on metal resistant bacteria in cochin estuary and its response towards antibiotics and silver nanoparticles

The Cochin estuary (CE), which is one of the largest wetland ecosystems, extends from Thanneermukkam bund in the south to Azhikode in the north. It functions as an effluent repository for more than 240 industries, the characteristics of which includes fertilizer, pesticide, radioactive mineral processing, chemical and allied industries, petroleum refining and heavy metal processing industries (Thyagarajan, 2004). Studies in the CE have been mostly on the spatial and temporal variations in the physical, chemical and biological characteristics of the estuary (Balachandran et al., 2006; Madhu et al., 2007; Menon et al., 2000; Qasim 2003;Qasim and Gopinathan 1969) . Although several monitoring programs have been initiated in the CE to understand the level of heavy metal pollution, these were restricted to trace metals distribution (Balachandran et al., 2005) or the influence of anthropogenic inputs on the benthos and phytoplankton (Madhu et al., 2007;Jayaraj, 2006). Recently, few studies were carried out on microbial ecology in the CE(Thottathil et al 2008a and b;Parvathi et al., 2009and 2011; Thomas et al., 2006;Chandran and Hatha, 2003). However, studies on metal - microbe interaction are hitherto not undertaken in this estuary. Hence, a study was undertaken at 3 sites with different level of heavy metal concentration tounderstand the abundance, diversity and mechanisms of resistance in metal resistant bacteria and its impact on the nutrient regeneration. The present work has also focused on the response of heavy metal resistant bacteria towards antibacterial agent’s antibiotics and silver nanoparticles

An Alternative Approach To Computer System Security Monitoring And Enhancement Through System Call Sequence Analysis

Modern computer systems are plagued with stability and security problems: applications lose data, web servers are hacked, and systems crash under heavy load. Many of these problems or anomalies arise from rare program behavior caused by attacks or errors. A substantial percentage of the web-based attacks are due to buffer overflows. Many methods have been devised to detect and prevent anomalous situations that arise from buffer overflows. The current state-of-art of anomaly detection systems is relatively primitive and mainly depend on static code checking to take care of buffer overflow attacks. For protection, Stack Guards and I-leap Guards are also used in wide varieties.This dissertation proposes an anomaly detection system, based on frequencies of system calls in the system call trace. System call traces represented as frequency sequences are profiled using sequence sets. A sequence set is identified by the starting sequence and frequencies of specific system calls. The deviations of the current input sequence from the corresponding normal profile in the frequency pattern of system calls is computed and expressed as an anomaly score. A simple Bayesian model is used for an accurate detection.Experimental results are reported which show that frequency of system calls represented using sequence sets, captures the normal behavior of programs under normal conditions of usage. This captured behavior allows the system to detect anomalies with a low rate of false positives. Data are presented which show that Bayesian Network on frequency variations responds effectively to induced buffer overflows. It can also help administrators to detect deviations in program flow introduced due to errors.