Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Cluster analysis & Wireless sensor network. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Ethics in Behavioural Targeting: Mapping Consumers Perceptions

Abstract: In contemporary development "Internet" is a leading advertising source. Marketing avenues from traditional media are deteriorating and online marketing earnings are growing persistently. With online advertising, behavioural targeting is emerging as a prominent trend which is expected to account for major advertising revenues; as it permits websites to discriminate advertisements according to consumers surfing patterns. This can equally raise privacy issues and can have adverse consumer responses. This study explores the consumer perception regarding the ethics of behavioural targeting when done without user consent and awareness. The study focus on two major aspects the first is about knowledge and awareness about behavioural targeting and second issues related to ethics in behavioural targeting. The result shows that consumers are unaware about behavioural targeting.

An Effective DOS Attack Detection Model in Cloud Using Artificial Bee Colony Optimization

Abstract: Denial of service (DOS) attack is a serious threat in the cloud which causes unavailability of cloud services to genuine users. Firewalls alone are not able to detect DOS attack because of the dynamic nature of the attack and masked identity. This paper proposes a cloud-based DOS attack detection model (CDOSD) which is setup through key feature selection using a new binary version of Artificial bee colony optimization (BABCO) and decision tree (DT) classifier. The DT classifier is utilized because it has superior learning speed than other classification algorithms and BABCO is used for feature selection from the dataset. The real-time DOS attack tools are used to perform the attacks on cloud host. It has been observed that the CDOSD detects DOS attack on cloud host with high accuracy and a very low false positive rate. The features of the dataset are significantly reduced by BABCO which provides a low dimension of computation space for training and classification. The proposed scheme is also compared with the other existing models and found superior in performance. The proposed methodology may help the cloud service providers to design more secure cloud environment.

Financing of PPP Infrastructure Projects in India: Constraints and Recommendations

Abstract: Infrastructure projects are complex, capital intensive and with long gestation period, posing multiple and unique risks to project financiers. Private investment in infrastructure in India over the past decade has not been up to the expectation, and there is a need for Public-Private Partnerships (PPPs) to play a much greater role than before. The paper analyzes the reasons for private financing not making its way into the different infrastructure segments, and examines the key constraints to private financing. It also suggests ways to mitigate these constraints for a sustained private investment in the Indian infrastructure.

Outlier detection: A survey on techniques of WSNs involving event and error based outliers

Abstract: In the recent few years, many wireless sensor networks have been distributed systematically in the real world to collect valuable raw sensed data However, the crucial point of challenge is to extract high level knowledge from this raw sensed data In the application of data analysis, a necessary preprocessing step is anomaly detection, also known as deviation detection or data cleansing Outliers in wireless sensor networks (WSNs) are those measures that deviate from a defined pattern Outlier detection can be used to remove noisy data, detect faulty nodes and discover interesting events Numerous small and low cost nodes loaded with capabilities of integrated sensing and computation are involved in a WSN structure Due to high density WSNs are exposed to faults and nasty attacks causing inaccurate and unreliable sensors reading, making Wireless sensor networks prone to outliers This survey provides an outline of outlier detection techniques and approaches focusing on event and error based outliers