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This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.

It is common place in any organizational environment that data stored internally does not necessarily belong to the company storing the data. In such cases, keeping this data secured is of critical importance. If such data is compromised, it can lead to devastating effects on both the public image of the organization and the relations between said company and its business partners. To combat this surge in malicious activity in recent years, research has focused on using anomaly detection techniques to detect possible malicious activity on a network. This paper proposes an evolution of the MCOD (Micro-Clustering Outlier Detection) machine learning algorithm. Designed to implement a time-series approach along with using both distance based outlier detection and cluster density analysis, we analysis the results of this algorithm on real-world data.

The growing number of malicious network attacks has resulted in the need for a fast, reliable method to identify possible malicious activity. For any organization, it is critical that confidential and proprietary data is sufficiently secured to address both legal and contractual obligations. The changing nature of security attacks has caused a surge of interest in anomaly detection mechanisms. Such mechanisms are suitable as they can dynamically adapt to changed network conditions and threats without security personnel intervention. While anomaly detection mechanisms have significant potential, they are technically limited. Many anomaly detection approaches are unsuitable for real time environments. The approaches also typically operate based on “what is common is normal”. Mechanisms are typically singular in focus analysing data on one specific type. This paper proposes a novel framework to detect anomalies previously hidden within current detection techniques. The approach is easily extensible taking input from many security assessment applications; network traffic, asset criticality. Using time based correlations with historic data; a method for generating a normalized view of activity on the network is achieved. Once normality has been established for specific time intervals an extensible environment is implemented which allows for the active monitoring of anomalies in real-time. Anomalies which had sufficient “commonality” to remain undetected by other mechanisms are identified and analysed. The proposed solution is completely autonomous, capable of acting independently with no previous knowledge required. The presented results describe NetFlow activity of the NPD Groups' network over a 24-hour period and outline real world anomalies that were detected.

This paper presents a new combined neural network and chaos based pseudorandom sequence generator and a DNA-rules based chaotic encryption algorithm for secure transmission and storage of images. The proposed scheme uses a new heterogeneous chaotic neural network generator controlling the operations of the encryption algorithm: pixel position permutation, DNA-based bit substitution and a new proposed DNA-based bit permutation method. The randomness of the generated chaotic sequence is improved by dynamically updating the control parameters as well as the number of iterations of the chaotic functions in the neural network. Several tests including auto correlation, 0/1 balance and NIST tests are performed to show high degree of randomness of the proposed chaotic generator. Experimental results such as pixel correlation coefficients, entropy, NPCR and UACI etc. as well as security analyses are given to demonstrate the security and efficiency of the proposed chaos based genetic encryption method. yes

The increase in malicious network based attacks has resulted in a growing interest in network anomaly detection. The ability to detect unauthorized or malicious activity on a network is of importance to any organization. With the increase in novel attacks, anomaly detection techniques can be more successful in detecting unknown malicious activity in comparison to traditional signature based methods. However, in a real-world environment, there are many variables that cannot be simulated. This paper proposes an architecture where parallel clustering algorithms work concurrently in order to detect abnormalities that may be lost while traversing over time-series windows. The presented results describe the NetFlow activity of the NPD Group, Inc. over a 24-hour period. The presented results contain real-world anomalies that were detected.

Data leakage has become a problem of epidemic proportions with very serious consequences for businesses and their customers. Experts warn that it is very difficult for organisations to avoid infiltration and that they should be prepared for such events. Proactive detection of ongoing attacks is therefore critically important. In this paper we describe the design and implementation of Peeper, a policy based system for data leakage detection that utilizes operating system call provenance. The implementation of our scheme shows that it enables real-time data detection of data leakage. It tracks the operations performed on sensitive files and issues alerts if suspicious activities are detected.

In this paper, we propose a new integrity assurance system in the cloud by using both hard and soft attestations. By hard attestation, we mean the usage of trusted computing to certify the machines in the cloud or to detect any tamper on the system level including boot, bios and operating system. The soft assurance mechanism is based on the analysis of the cloud provenance data which are collected by logging kernel system calls. This mechanism is data centric which means that it allows the detection of any changes to data artifacts in the cloud. The results of our integrity assurance system is communicated to the user using Cloud Trust Protocol (CTP) developed by the Cloud Security Alliance (CSA). Our proposed scheme is tested on an OpenStack cloud to show a proof of concept of our integrity assurance system.

International audience; This paper presents a novel and robust chaos-based cryptosystem for secure transmitted images and four other versions. In the proposed block encryption/decryption algorithm, a 2D chaotic map is used to shuffle the image pixel positions. Then, substitution (confusion) and permutation (diffusion) operations on every block, with multiple rounds, are combined using two perturbed chaotic PWLCM maps. The perturbing orbit technique improves the statistical properties of encrypted images. The obtained error propagation in various standard cipher block modes demonstrates that the proposed cryptosystem is suitable to transmit cipher data over a corrupted digital channel. Finally, to quantify the security level of the proposed cryptosystem, many tests are performed and experimental results show that the suggested cryptosystem has a high security level.