With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this article is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.

In this article, we propose a threat management system (TMS) for Data-centric Internet-of-Things-based Collaborative Systems (DIoTCSs). In particular, we focus on tampering attacks that target shared databases and can affect the execution of the DIoTCS services. The novelty of the proposed system is to isolate the damage caused by tampering attacks into data partitions. We formulate the partitioning problem as a cost-driven optimization problem, prove its NP-hardness, and propose two polynomial-time heuristics. We evaluate a TMS experimentally and demonstrate that intelligent partitioning of the database improves the overall availability of the DIoTCS.

To ensure integrity, trust, immutability and authenticity of software and information (cyber data, user data and attack event data) in a collaborative environment, research is needed for cross-domain data communication, global software collaboration, sharing, access auditing and accountability. Blockchain technology can significantly automate the software export auditing and tracking processes. It allows to track and control what data or software components are shared between entities across multiple security domains. Our blockchain-based solution relies on role-based and attribute-based access control and prevents unauthorized data accesses. It guarantees integrity of provenance data on who updated what software module and when. Furthermore, our solution detects data leakages, made behind the scene by authorized blockchain network participants, to unauthorized entities. Our approach is used for data forensics/provenance, when the identity of those entities who have accessed/ updated/ transferred the sensitive cyber data or sensitive software is determined. All the transactions in the global collaborative software development environment are recorded in the blockchain public ledger and can be verified any time in the future. Transactions can not be repudiated by invokers. We also propose modified transaction validation procedure to improve performance and to protect permissioned IBM Hyperledger-based blockchains from DoS attacks, caused by bursts of invalid transactions.

B2B (business-to-business) systems often use service-oriented architecture (SOA) with decomposed business services. These services can interact and share data among each other. Service might use a cloud – hosted database, such as a non - relational encrypted key – value store. However, the cloud platform hosting the database can be untrusted. Data owner needs to be sure that each service can access only those segments of a shared database for which the service is authorized. Furthermore, data requests can come from a service also hosted by untrusted cloud. Hence, there is a need for designing a cloud enterprise framework that can ensure privacy-preserving data dissemination in SOA and accurately detect data leakages. We design and prototype a solution that ensures privacy – preserving dissemination of data. The solution is based on (a) role-based access control, (b) cryptographic capabilities of client's browser, (c) authentication method, (d) subject's trust level. The prototype enables privacy – preserving dissemination of Electronic Health Records (EHRs) hosted in an untrusted cloud.

Advances in cloud computing have made it a feasible and cost-effective solution to improve the resiliency of enterprise systems. However, the replication approach taken by cloud computing to provide resiliency leads to an increase in the number of ways an attacker can exploit or penetrate the systems. This calls for designing cloud systems that can accurately detect anomalies and dynamically adapt themselves to keep performing mission-critical functions even under attacks and failures. In this paper, we propose a self-adaptive resiliency approach for cloud enterprise systems that employs a live monitoring and moving target defense based approach to automatically detect deviations from normal behavior and reconfigure critical cloud processes through software-defined networking to mitigate attacks and reduce system downtime. The proposed solution is promising to present a unified framework for resilient cloud systems.