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What is Security Alarm

Handbook of Research on Securing Cloud-Based Databases with Biometric Applications
Whenever an intrusion is detected within a network an alert is generated which communicated to the computer as well as the user and in some cases the security agencies so that the intrusion attempt can be nullified and the impact of the intrusion can be mitigated.
Published in Chapter:
Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging
Khaleel Ahmad (Maulana Azad National Urdu University, India), Gaurav Kumar (Swami Vivekananda Subharti University, India), Abdul Wahid (Maulana Azad National Urdu University, India), and Mudasir M. Kirmani (Sher-e-Kashmir University of Agricultural Science and Technology of Kashmir, India)
DOI: 10.4018/978-1-4666-6559-0.ch018
Abstract
Rapid connectivity and exchange of information across the globe with extension of computer networks during the past decade has led to security threats in network communication and has become a critical concern for network management. It is necessary to retain high security measures to ensure safe and trusted communication across the network. Diverse soft-computing-based methods have been devised in the past for the perfection of intrusion detection systems on host-based and host-independent systems. This chapter discusses the flow-based anomaly detector for intrusion in network by self-learning process with characteristics of bacterial forging approach. This approach handles the network-flow and attack on network traffic in an automated fashion. This approach works on host-independent systems and on stream of network rather than payload length where data behavior of flow in network is analyzed. This model provides a cataloging of attacks and resistance mechanism techniques to avoid intrusion.
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