Biblio

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

This paper showcases multiclass classification baselines using different machine learning algorithms and neural networks for distinguishing legitimate network traffic from direct and obfuscated network intrusions. This research derives its baselines from Advanced Security Network Metrics & Tunneling Obfuscations dataset. The dataset captured legitimate and obfuscated malicious TCP communications on selected vulnerable network services. The multiclass classification NIDS is able to distinguish obfuscated and direct network intrusion with up to 95% accuracy.

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem.