Strengthening Data Security: an Holistic Approach

In the light of heightened concern around data security, this paper highlights some of the measures that can be used to develop and strengthen security in data archiving. The paper includes discussion of the different approaches that can be taken towards the construction of firm and resilient data and information security policies within the social science data archiving communities. While international standards can provide theoretical guidelines for the construction of such a policy, procedures need to be informed by more practical considerations. Attention is drawn to the necessity of following a holistic approach to data security, which includes the education of data creators in the reduction of disclosure risk, the integration of robust and appropriate data processing, handling and management procedures, the value of emerging technological solutions, the training of data users in data security, and the importance of management control, as well as the need to be informed by emerging government security and digital preservation standards

A proposed case for the cloud software engineering in security

This paper presents Cloud Software Engineering in Security (CSES) proposal that combines the benefits from each of good software engineering process and security. While other literature does not provide a proposal for Cloud security as yet, we use Business Process Modeling Notation (BPMN) to illustrate the concept of CSES from its design, implementation and test phases. BPMN can be used to raise alarm for protecting Cloud security in a real case scenario in real-time. Results from BPMN simulations show that a long execution time of 60 hours is required to protect real-time security of 2 petabytes (PB). When data is not in use, BPMN simulations show that the execution time for all data security rapidly falls off. We demonstrate a proposal to deal with Cloud security and aim to improve its current performance for Big Data

Introduction to Security Onion

Security Onion is a Network Security Manager (NSM) platform that provides multiple Intrusion Detection Systems (IDS) including Host IDS (HIDS) and Network IDS (NIDS). Many types of data can be acquired using Security Onion for analysis. This includes data related to: Host, Network, Session, Asset, Alert and Protocols. Security Onion can be implemented as a standalone deployment with server and sensor included or with a master server and multiple sensors allowing for the system to be scaled as required. Many interfaces and tools are available for management of the system and analysis of data such as Sguil, Snorby, Squert and Enterprise Log Search and Archive (ELSA). These interfaces can be used for analysis of alerts and captured events and then can be further exported for analysis in Network Forensic Analysis Tools (NFAT) such as NetworkMiner, CapME or Xplico. The Security Onion platform also provides various methods of management such as Secure SHell (SSH) for management of server and sensors and Web client remote access. All of this with the ability to replay and analyse example malicious traffic makes the Security Onion a suitable low cost alternative for Network Security Management. In this paper, we have a feature and functionality review for the Security Onion in terms of: types of data, configuration, interface, tools and system management

Chaotic iterations versus Spread-spectrum: chaos and stego security

A new framework for information hiding security, called chaos-security, has been proposed in a previous study. It is based on the evaluation of unpredictability of the scheme, whereas existing notions of security, as stego-security, are more linked to information leaks. It has been proven that spread-spectrum techniques, a well-known stego-secure scheme, are chaos-secure too. In this paper, the links between the two notions of security is deepened and the usability of chaos-security is clarified, by presenting a novel data hiding scheme that is twice stego and chaos-secure. This last scheme has better scores than spread-spectrum when evaluating qualitative and quantitative chaos-security properties. Incidentally, this result shows that the new framework for security tends to improve the ability to compare data hiding scheme

IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN

Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks (LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of IoT networks in general