/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

Anomaly detection (AD) use within the network intrusion detection field of research, or network intrusion AD (NIAD), is dependent on the proper use of similarity and distance measures, but the measures used are often not documented in published research. As a result, while the body of NIAD research has grown extensively, knowledge of the utility of similarity and distance measures within the field has not grown correspondingly. NIAD research covers a myriad of domains and employs a diverse array of techniques from simple $k$ -means clustering through advanced multiagent distributed AD systems. This review presents an overview of the use of similarity and distance measures within NIAD research. The analysis provides a theoretical background in distance measures and a discussion of various types of distance measures and their uses. Exemplary uses of distance measures in published research are presented, as is the overall state of the distance measure rigor in the field. Finally, areas that require further focus on improving the distance measure rigor in the NIAD field are presented.

A Survey of Distance and Similarity Measures Used Within Network Intrusion Anomaly Detection

Understanding and predicting the determinants of cloud computing adoption

Authentication schemes relying on per-packet signature and per-signature verification introduce heavy cost for computation and communication. Due to its constraint resources, smart grid's authentication requirement cannot be satisfied by this scheme. Most importantly, it is a must to underscore smart grid's demand for high availability. In this paper, we present an efficient and robust approach to authenticate data aggregation in smart grid via deploying signature aggregation, batch verification and signature amortization schemes to less communication overhead, reduce numbers of signing and verification operations, and provide fault tolerance. Corresponding fault diagnosis algorithms are contributed to pinpoint forged or error signatures. Both experimental result and performance evaluation demonstrate our computational and communication gains.

/pdf/efficient-authentication-scheme-for-data-aggregation-in-28pk8vk4c5.pdf

Efficient authentication scheme for data aggregation in smart grid with fault tolerance and fault diagnosis

The mobile commerce (m-commerce) industry has rapidly grown in value in recent years, as has the number of m-commerce service providers and interest in it from consumers and academia alike. In order to ensure customer loyalty, providers must determine which factors influence consumer satisfaction in m-commerce. Therefore, the objective of this study is to determine and rank the significant predictors of satisfaction in m-commerce. The paper also develops a procedure for artificial neural network model design and parameter setting in technology acceptance studies. Data was collected from 224 users of m-commerce services. The results presented are based on a combination of structural equation modeling (SEM) and artificial neural network (ANN) analyses. A multi-layer perceptron was used for ANN modeling. The results show that the optimal ANN model has one hidden layer and a sigmoid as an activation function in both layers, while the number of hidden nodes should be determined using a recommended rule-of-thumb. In addition, mobility and trust were found to be the most significant determinants of consumer satisfaction in m-commerce. The results of the study are significant as they have important implications for both academia and companies, due to the fact that some of the factors investigated in the study, such as mobility, have rarely been explored in previous consumer satisfaction studies, but were proved to be very significant. Another important result of the study is the proposal of a detailed procedure of ANN model design and the recommendations made for the selection of ANN model architecture and parameter settings.

Neural network modeling of consumer satisfaction in mobile commerce: An empirical analysis

Recent developments have caused the expansion of various cloud computing environments and services. Cloud computing environments have led to research in the areas of data processing, virtual environments, and access control. Information security is the most important research area for these environments security. In this study, we analyzed typical example of network testbeds, which have been used for malicious activity data collection and its subsequent analysis. Further, we propose an effective malicious network application testbed, which is based on a cloud system. We also verified the performance of our new testbed by comparing real malicious activity with the cloud-based testbed results.

A Novel Approach to Analyzing for Detecting Malicious Network Activity Using a Cloud Computing Testbed

Machine Learning as network attack detection is one of the popular methods researched. Signature based network attack detection is no longer convinced the efficiency in the diversified intrusions (Limmer and Dressler in 17th ACM Conference on Computer and Communication Security, 2010). Moreover, as the various Zero-day attacks, non notified attacks cannot be detected (Wu and Banzhaf in Appl Soft Comput 10(1):1---35, 2010). This paper suggests an effective update method of data set on Machine Learning to detect non notified attacks. In addition, this paper compares and verifies the effects of Machine Learning Detection with updated data set to the former methods.

Dynamic learning model update of hybrid-classifiers for intrusion detection

A statistical model for network data analysis: KDD CUP 99' data evaluation and its comparing with MIT Lincoln Laboratory network data

Advanced Encryption Standard (AES) has replaced its predecessor, Double Encryption Standard (DES), as the most widely used encryption algorithm in many security applications. Up to today, AES standard has key size variants of 128, 192, and 256-bit, where longer bit keys provide more secure ciphered text output. In the hardware perspective, bigger key size also means bigger area and power consumption due to more operations that need to be done. Some companies that employ ultra-high security in their systems may look for a key size bigger than 256-bit AES. In this paper, 128 and 256-bit AES hardware, as well as two variants of an AES encryption algorithm for 512-bit and 1024-bit key size, are implemented and compared in terms of power consumption and area. The experiment is done in 45 nm CMOS technology at 1.1 V using a Synopys DC Compiler and Modelsim and total power consumption and area results are presented and graphically compared.

Power dissipation and area comparison of 512-bit and 1024-bit key AES

We have come a long way in the information age. Thanks to the advancement of such technologies as the internet, we have discovered new ways to convey information on a broader scope. However, negative aspects exist as is with anything else. These may include invasion of privacy over the web, or identity theft over the internet. What is more alarming is that malwares so called 'maliciouscodes' are rapidly spreading. Its intent is very destructive which can result in hacking, phishing and as aforementioned, one of the most disturbing problems on the net, invasion of privacy. This thesis describes the technology of how you can effectively analyze and detect these kind of malicious codes. We propose sequencial hybrid analysis for API calls that are hooked inside user-mode and kernel-level of Windows. This research explains how we can cope with malicious code more efficiently by abstracting malicious function signature and hiding attribute.

https://www.koreascience.kr:443/article/JAKO200706717291637.pdf

Jae-Ik Cho

Papers

A Novel Approach to Analyzing for Detecting Malicious Network Activity Using a Cloud Computing Testbed

Dynamic learning model update of hybrid-classifiers for intrusion detection

A statistical model for network data analysis: KDD CUP 99' data evaluation and its comparing with MIT Lincoln Laboratory network data

Power dissipation and area comparison of 512-bit and 1024-bit key AES

Malware Detection Via Hybrid Analysis for API Calls