Over the last three decades, network devices are increasing due to technology like the Internet of Things (IoT) and Bring Your Own Device (BYOD). These rapidly increasing devices open many venues for network attacks whereas modern attacks are more sophisticated and complex to detect. To detect these attacks efficiently, we have used recently available dataset UNSW-NB15. UNSW-NB15 is developed according to the modern flow of network traffic with 49 features including 9 types of network attacks. To analyze the traffic pattern for the intrusion detection system(IDS), we have used multiple classifiers to test the accuracy. From the dataset UNSWNB15, we have used medium and strong correlated features. All the results from different classifiers are compared. Prominent results are achieved by ensemble bagged tree which classifies normal and individual attacks with an accuracy of 79%.

In this paper, we proposed a dispersion flattened photonic crystal fiber (PCF) for having very low dispersion for wide bandwidth as well as low confinement loss. The proposed fiber has been numerically analyzed for Silica core as well as Borosilicate crown glass core with square lattice air holes. In the proposed design we have used elliptical air holes in the inner ring whereas outer rings are circular. Finite Element Method based software tool is used to analyze the proposed design. This comparison of core materials deduces that Borosilicate crown glass PCF produces negative dispersion, making it a good candidate to be used as Dispersion Compensating Fiber (DCF), whereas Silica PCF provides nearly zero dispersion at wavelength range 1.35 µm to 1.70 µm

Multiprotocol label switching is the latest and developing technology in the world of the internet. It speeds up the network by using the technique of label instead of an IP address. It provides the reliable transmission of data with high speed and low delay. For efficient use of network utilization MPLS has a key feature of QoS. Due to the effective utilization of network resources, minimum delay and predictable performance MPLS technology make it more appropriate for implementing multimedia type applications. In this research, the performance of MPLS technology is compared with the traditional IP network for multimedia traffic in node scalable networks. For simulating and comparing the performance of both technologies OPNET modular 14.5 is used. This comparison is done on the basis of network performance parameters such as packet loss/ traffic drop, end-to-end delay, and throughput. Finally, the results have been evaluated which show that MPLS technology provides better performance as compared to IP in node scalable environment

The use of Roman Urdu (in the form of web and user-generated content) is a common mode of communication on social media. Content like comments, reviews, feedbacks and social networking posts have been generated in Roman Urdu in large volumes. But this area is not much worked on in terms of sentiment and opinion analysis. Roman Urdu (the scripting style for Urdu language) is one of the limited resource languages that brings forward the challenges and problems for performing Opinion Mining. Adequate opinion mining is not just about understanding the overall sentiment of a document or a single paragraph, but it is also important to be able to extract sentiments on a very granular level and relate each sentiment to the aspect it corresponds to. On the more advanced level, the analysis can go beyond only positive or negative attitude and identify complex attitude types. We, therefore, developed a model for performing discourse-based opinion mining, so we could also consider the impact that various discourse elements have on the overall sentiment of the text. Our work differs from the existing body of knowledge in that not much work has been carried out on processing of Roman Urdu data for opinion mining considering discourse elements. Since our work focuses on performing discourse-based opinion mining it can be considered as first attempt in this direction as none of the literature surveyed revealed discoursebased analysis of Roman Urdu text. The overall gist of this research work is to have insights of the nature of user-generated content in Roman Urdu and to build necessary resources and devise algorithms to make an advancement in Sentiment Analysis and Classification for Roman Urdu.

Principal component analysis (PCA) and partial least square (PLS) used for fault diagnosis and process monitoring for systems. It is expected that the information to be examined is not self-connected. However, the most largescale chemical industrial plants are nonlinear in nature so these techniques do not cope with them, invalid in nature. To fulfil the gap, there is need to develop an algorithm which can manage these nonlinearities of the process. The demands of industrial products are increasing rapidly so different adaptable techniques are being proposed. Canonical Correlation Analysis (CCA) is multivariate data-driven methodology which takes input-output both process data into consideration. Most industrial systems assumed that the data to be analysed is Gaussian in nature.However, it is not due to the nonlinearity’sreal systems in nature. In this work, an algorithm is developed that can monitor the system process using CCA with control limit that is achieved from the kernel density estimation by estimating probability density function (pdf).