Biblio

Nowadays, Internet of Things (IoT) has gained considerable significance and concern, consequently, and in particular with widespread usage and adoption of the IoT applications and projects in various industries, the consideration of the IoT Security has increased dramatically too. Therefore, this paper presents a concise and a precise review for the current state of the IoT security models and frameworks. The paper also proposes a new unified criteria and characteristics, namely Formal, Inclusive, Future, Agile, and Compliant with the standards (FIFAC), in order to assure modularity, reliability, and trust for future IoT security models, as well as, to provide an assortment of adaptable controls for protecting the data consistently across all IoT layers.

Realistic state-based discrete-event simulation models are often quite complex. The complexity frequently manifests in models that (a) contain a large number of input variables whose values are difficult to determine precisely, and (b) take a relatively long time to solve. Traditionally, models that have a large number of input variables whose values are not well-known are understood through the use of sensitivity analysis (SA) and uncertainty quantification (UQ). However, it can be prohibitively time consuming to perform SA and UQ. In this work, we present a novel approach we developed for performing fast and thorough SA and UQ on a metamodel composed of a stacked ensemble of regressors that emulates the behavior of the base model. We demonstrate the approach using a previously published botnet model as a test case, showing that the metamodel approach is several orders of magnitude faster than the base model, more accurate than existing approaches, and amenable to SA and UQ.

The majority of security breaches in cloud infrastructure in recent years are caused by human errors and misconfigured resources. Novel security models are imperative to overcome these issues. Such models must be customer-centric, continuous, not focused on traditional security paradigms like intrusion detection and adopt proactive techniques. Thus, this paper proposes CloudStrike, a cloud security system that implements the principles of Chaos Engineering to enable the aforementioned properties. Chaos Engineering is an emerging discipline employed to prevent non-security failures in cloud infrastructure via Fault Injection Testing techniques. CloudStrike employs similar techniques with a focus on injecting failures that impact security i.e. integrity, confidentiality and availability. Essentially, CloudStrike leverages the relationship between dependability and security models. Preliminary experiments provide insightful and prospective results.

It is technically challenging to conduct a security analysis of a dynamic network, due to the lack of methods and techniques to capture different security postures as the network changes. Graphical Security Models (e.g., Attack Graph) are used to assess the security of network systems, but it typically captures a snapshot of a network state to carry out the security analysis. To address this issue, we propose a new Graphical Security Model named Time-independent Hierarchical Attack Representation Model (Ti-HARM) that captures security of multiple network states by taking into account the time duration of each network state and the visibility of network components (e.g., hosts, edges) in each state. By incorporating the changes, we can analyse the security of dynamic networks taking into account all the threats appearing in different network states. Our experimental results show that the Ti-HARM can effectively capture and assess the security of dynamic networks which were not possible using existing graphical security models.

There is no doubt that security issues are on the rise and defense mechanisms are becoming one of the leading subjects for academic and industry experts. In this paper, we focus on the security domain and envision a new way of looking at the security life cycle. We utilize our vision to propose an asset-based approach to countermeasure zero day attacks. To evaluate our proposal, we built a prototype. The initial results are promising and indicate that our prototype will achieve its goal of detecting zero-day attacks.

Information security has rapidly grown to meet the requirements of today services. A solid discipline has been developed as far as technical security is concerned. However, the human layer plays an increasingly decisive role in the managing of Information Technology (IT) systems. The research field that studies the vulnerabilities of the human layer is referred to as Social Engineering, and has not received the same attention of its technical counterpart. We try to partially fill this gap by studying the selection of the Human Attack Vector (HAV), i.e., the path or the means that the attacker uses to compromise the human layer. To this aim, we propose a multilayer model, named Victim Communication Stack (VCS), that provides the key elements to facilitate the choice of the HAV. This work has been carried out under the DOGANA European project.

In cloud computing environment, there are malignant nodes which create a huge problem to transfer data in communication. As there are so many models to prevent the data over the network, here we try to prevent or make secure to the network by avoiding mallicious nodes in between the communication. So the probabiliostic approach what we use here is a coherent tool to supervise the security challenges in the cloud environment. The matter of security for cloud computing is a superficial quality of service from cloud service providers. Even, cloud computing dealing everyday with new challenges, which is in process to well investigate. This research work draws the light on aspect regarding with the cloud data transmission and security by identifying the malignanat nodes in between the communication. Cloud computing network shared the common pool of resources like hardware, framework, platforms and security mechanisms. therefore Cloud Computing cache the information and deliver the secure transaction of data, so privacy and security has become the bone of contention which hampers the process to execute safely. To ensure the security of data in cloud environment, we proposed a method by implementing white box cryptography on RSA algorithm and then we work on the network, and find the malignant nodes which hampering the communication by hitting each other in the network. Several existing security models already have been deployed with security attacks. A probabilistic authentication and authorization approach is introduced to overcome this attack easily. It observes corrupted nodes before hitting with maximum probability. here we use a command table to conquer the malignant nodes. then we do the comparative study and it shows the probabilistic authentication and authorization protocol gives the performance much better than the old ones.

Moving Target Defense (MTD) can enhance the resilience of cyber systems against attacks. Although there have been many MTD techniques, there is no systematic understanding and quantitative characterization of the power of MTD. In this paper, we propose to use a cyber epidemic dynamics approach to characterize the power of MTD. We define and investigate two complementary measures that are applicable when the defender aims to deploy MTD to achieve a certain security goal. One measure emphasizes the maximum portion of time during which the system can afford to stay in an undesired configuration (or posture), without considering the cost of deploying MTD. The other measure emphasizes the minimum cost of deploying MTD, while accommodating that the system has to stay in an undesired configuration (or posture) for a given portion of time. Our analytic studies lead to algorithms for optimally deploying MTD.

Multiple Security Domains Nondeducibility, MSDND, yields results even when the attack hides important information from electronic monitors and human operators. Because MSDND is based upon modal frames, it is able to analyze the event system as it progresses rather than relying on traces of the system. Not only does it provide results as the system evolves, MSDND can point out attacks designed to be missed in other security models. This work examines information flow disruption attacks such as Stuxnet and formally explains the role that implicit trust in the cyber security of a cyber physical system (CPS) plays in the success of the attack. The fact that the attack hides behind MSDND can be used to help secure the system by modifications to break MSDND and leave the attack nowhere to hide. Modal operators are defined to allow the manipulation of belief and trust states within the model. We show how the attack hides and uses the operator's trust to remain undetected. In fact, trust in the CPS is key to the success of the attack.