Biblio

Budding technologies like IoT requires minimum latency for performing real-time applications. The IoT devices collect a huge amount of big data and stores in the cloud environment, because of its on-demand services and scalability. But processing the needed information of the IoT devices from the cloud computing environment is found to be time-sensitive one. To eradicate this issue fog computing environment was created which acts an intermediate between the IoT devices and cloud computing environment. The fog computing performs intermediate computation and storage which is needed by IoT devices and it eliminates the drawbacks of latency and bandwidth limitation faced by directly using cloud computing for storage and accessing. The fog computing even though more advantageous it is more exposed to security issues by its architecture. This paper concentrates more on the security issues met by fog computing and the present methods used by the researchers to secure fog with their pros and cons.

In the past air-gapped systems that are isolated from networks have been considered to be very secure. Yet there have been reports of such systems being breached. These breaches have shown to use unconventional means for communication also known as covert channels such as Acoustic, Electromagnetic, Magnetic, Electric, Optical, and Thermal to transfer data. In this paper, a review of various attack methods that can compromise an air-gapped system is presented along with a summary of how efficient and dangerous a particular method could be. The capabilities of each covert channel are listed to better understand the threat it poses and also some countermeasures to safeguard against such attack methods are mentioned. These attack methods have already been proven to work and awareness of such covert channels for data exfiltration is crucial in various industries.

A security breach often makes companies react by changing their attitude and approach to security within the organization. This paper presents an in-depth case study of post-breach security changes made by a company and the consequences of those changes. We employ the principles of participatory action research and humble inquiry to conduct a long-term study with employee interviews while embedded in the organization's security division. Despite an extremely high level of financial investment in security, and consistent attention and involvement from the board, the interviews indicate a significant level of friction between employees and security. In the main themes that emerged from our data analysis, a number of factors shed light on the friction: fear of another breach leading to zero risk appetite, impossible security controls making non-compliance a norm, security theatre underminining the purpose of security policies, employees often trading-off security with productivity, and as such being treated as children in detention rather than employees trying to finish their paid jobs. This paper shows that post-breach security changes can be complex and sometimes risky due to emotions often being involved. Without an approach considerate of how humans and security interact, even with high financial investment, attempts to change an organization's security behaviour may be ineffective.

The mass integration and deployment of intelligent technologies within critical commercial, industrial and public environments have a significant impact on business operations and society as a whole. Though integration of these critical intelligent technologies pose serious embedded security challenges for technology manufacturers which are required to be systematically approached, in-line with international security regulations.This paper establish security foundation for such intelligent technologies by deriving embedded security requirements to realise the core security functions laid out by international security authorities, and proposing microarchitectural characteristics to establish cyber resilience in embedded systems. To bridge the research gap between embedded and operational security domains, a detailed review of existing embedded security methods, microarchitectures and design practises is presented. The existing embedded security methods have been found ad-hoc, passive and strongly rely on building and maintaining trust. To the best of our knowledge to date, no existing embedded security microarchitecture or defence mechanism provides continuity of data stream or security once trust has broken. This functionality is critical for embedded technologies deployed in critical infrastructure to enhance and maintain security, and to gain evidence of the security breach to effectively evaluate, improve and deploy active response and mitigation strategies. To this end, the paper proposes three microarchitectural characteristics that shall be designed and integrated into embedded architectures to establish, maintain and improve cyber resilience in embedded systems for next-generation critical infrastructure.

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Honeypot systems are an effective method for defending production systems from security breaches and to gain detailed information about attackers' motivation, tactics, software and infrastructure. In this paper we present how different types of honeypots can be employed to gain valuable information about attacks and attackers, and also outline new and innovative possibilities for future research.