Biblio

In the computer based solutions of the problems in today's world; if the problem has a high complexity value, different requirements can be addressed such as necessity of simultaneous operation of many computers, the long processing times for the operation of algorithms, and computers with hardware features that can provide high performance. For this reason, it is inevitable to use a computer based on quantum physics in the near future in order to make today's cryptosystems unsafe, search the servers and other information storage centers on internet very quickly, solve optimization problems in the NP-hard category with a very wide solution space and analyze information on large-scale data processing and to process high-resolution image for artificial intelligence applications. In this study, an examination of quantum approaches and quantum computers, which will be widely used in the near future, was carried out and the areas in which such innovation can be used was evaluated. Malicious or non-malicious use of quantum computers with this capacity, the advantages and disadvantages of the high performance which it provides were examined under the head of security, the effect of this recent technology on the existing security systems was investigated.

Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.

Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.