Intrusion Tolerant Database System Research Articles

Intrusion tolerance model against higher institution database

Privacy and security are the two major concerns of keeping and accessing data on the internet. The rate of intruding organisation’s database by unauthorised users is on the increase. Thus, the affected organisation’s data confidentiality is lost; it can be viewed, modified, deleted and/or make it inaccessible to authorised users. Intrusion detection and tolerance techniques help in recognising malicious attacks as well as supports the websites to survive the attack. A quantitative approach was used in this study even though numerous attempts of quantitative evaluation of the survivability of intrusion tolerant systems, especially in database field have been made. Study on survivability of intrusion tolerant systems has being done, taking behaviour of attack, prediction of scale, speed of database damage propagation and its degree of spreading as facilitators. This paper provides the intrusion tolerant database system as a series of state transition model (Zumkas Model) based on the hidden Markov model. Keywords: Intrusion, survivability, model, patterns

Controlling The False Alarm In An Intrusion Tolerant Database System Using Significance Degrees Of Data Objects

Traditional database security mechanisms focus on either protection or prevention. However, in practice all attacks are not avoidable. To solve this problem, Intrusion Tolerant Database Systems (ITDBs) were introduced. An ITDB uses new generation database security mechanisms to guarantee specified levels of data availability, integrity and confidentiality in the presence of successful attacks. A key part of an ITDB is the intrusion detection (ID) which informs the system about attacks. One of the problems in using ID is the false alarm that will lead to the reduction of the “availability” or “integrity”. This paper presents an intelligent method to control false alarm. In this method, we will use the significance degrees of data objects to determine the anomaly threshold adaptively, as the “availability” and “integrity” required by the data objects are satisfied.

Optimizing an Intrusion Tolerant Database System Using Neural Network

Traditional database security mechanisms focus on either protection or prevention. However, these mechanisms have not any strategy in the presence of successful attacks. To solve this problem, the Intrusion Tolerant Database System (ITDB) was introduced. ITDB uses the new generation of database security mechanisms to guarantee specified levels of data availability, integrity and confidentiality in the presence of successful attacks. These mechanisms include Attack Isolation and Multiphase Damage Confinement. In this paper, we will present a practical model to utilize the combination of intrusion tolerance techniques for managing the ITDB architecture. Using this practical model, we will be able to secure the system’s required integrity and availability levels considering the changes in the environment. We will also introduce an intelligent method for determining the significance degrees of data objects in the optimized attack isolation technique.

Damage assessment and repair in attack resilient distributed database systems

Preventive measures sometimes fail to defect malicious attacks. With attacks on data-intensive applications becoming an ever more serious threat, intrusion tolerant database systems are a significant concern. The main objective of such systems is to detect attacks, and to assess and repair the damage in a timely manner. This paper focuses on efficient damage assessment and repair in distributed database systems. The complexity caused by data partition, distributed transaction processing, and failures makes intrusion recovery much more challenging than in centralized database systems. This paper identifies the key challenges and presents an efficient algorithm for distributed damage assessment and repair.

Evaluating the survivability of Intrusion Tolerant Database systems and the impact of intrusion detection deficiencies

The immaturity of current intrusion detection techniques limits traditional security mechanisms in surviving malicious attacks. Intrusion tolerance approaches have emerged to overcome these limitations. However, to what extent an intrusion tolerant system can mitigate detection deficiencies is still unknown. In this paper, we focus on quantifying the capability of a resilient database system surviving intrusions. An Intrusion Tolerant DataBase system (ITDB) is studied as an example. Our experimental results indicate that ITDB can maintain the desired level of data integrity and availability without being seriously affected by various intrusion detection deficiencies. The impact of intrusion tolerance operations on performance is also evaluated.