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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

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.

Many chaos-based encryption methods have been presented and discussed in the last two decades, but very few of them are suitable to secure transmission on noisy channels or respect the standard of the National Institute of Standards and Technology (NIST). This paper tackles the problem and presents a novel chaos-based cryptosystem for secure transmitted images. The proposed cryptosystem overcomes the drawbacks of existing chaotic algorithms such as the Socek, Xiang, Yang, and Wong methods. It takes advantage of the increasingly complex behavior of perturbed chaotic signals. The perturbing orbit technique improves the dynamic statistical properties of generated chaotic sequences, permits the proposed algorithm reaching higher performance, and avoids the problem of error propagation. Finally, many standard tools, such as NIST tests, are used to quantify the security level of the proposed cryptosystem, and experimental results prove that the suggested cryptosystem has a high security level, lower correlation coefficients, and improved entropy.

Vehicular Ad Hoc Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. This paper introduces the future emerging technology, i.e., Vehicular Cloud Networking (VCN) where vehicles and adjacent infrastructure merge with traditional internet clouds to offer different applications ranging from low sized applications to very complex applications. VCN is composed of three types of clouds: Vehicular cloud, Infrastructure cloud and traditional Back-End (IT) cloud. We introduced these clouds via a three tier architecture along with their operations and characteristics. We have proposed use cases of each cloud tier that explain how it is practically created and utilised while taking the vehicular mobility in consideration. Moreover, it is critical to ensure security, privacy and trust of VCN network and its assets. Therefore, to describe the security of VCN, we have provided an in-depth analysis of different threats related to each tier of VCN. The threats related to vehicular cloud and infrastructure cloud are categorized according to their assets, i.e., vehicles, adjacent infrastructure, wireless communication, vehicular messages, and vehicular cloud threats. Similarly, the Back-End cloud threats are categorized into data and network threats. The possible implications of these threats and their effects on various components of VCN are also explained in detail.

In this paper, we consider the problem of trust in cloud monitoring systems. We design and develop a novel scheme for trust certification using property based attestation (PBA). The PBA is based on a trusted platform module (TPM) installed on the monitoring system called Cloud Pass. This certification scheme can be applied to any other monitoring system. In our proposal, two security properties are studied and tested, the integrity of the monitoring system and the identity of the platform. To test the proposed scheme, a prototype is developed and the certificates are generated at different security property granularities for the attested system.

Quantum cryptography is a promising solution in the problem of communication security. The BB84 quantum protocol is integrated in the 4-way handshake keys establishment protocol of 802.11 networks to ensure an absolute security in keys transmission; BB84 consists of several steps, but the most important step is the reconciliation which consists of permuting the bits of the sequences randomly using the Bellot and Dang (BD) method to detect and correct the errors that could occur from the quantum system. And yet, it presents weaknesses since it didn't correct all the errors in some cases although it necessitates the most of the time and the resources. In this paper, we propose a new method to permute the bits using the quantum cryptography and the chaos. This method enhances the BD method using a chaotic permutation and leads to a new method called OFBD. The simulated results of the new method show the advantage of detecting and correcting all of the existing errors, increasing the efficiency of this method for more than 25% comparing to the existing permutation.

In this paper, we consider the problem of securing mobile-cloud storage services. We design and develop a novel and secure searchable encryption scheme for mobile devices. In the proposed algorithm, a content based index is constructed and a locality sensitive hashing is used to allow the user to store his files on the cloud in a secure way and then perform a secure query and a fuzzy search. Moreover, the scheme allows to rank the results while maintaining the privacy and confidentiality of the user and to save storage space and computation resources of his mobile phone. To quantify the performance of the proposed scheme, a range of tests are performed and experimental results demonstrate that the proposed system is suitable for a secure, fast and searchable storage mobile-cloud system.