The exponential growth in the use of Internet of Things (IoT) devices has introduced numerous challenges, in particular dealing with new security threats. In addition, connecting heterogeneous devices with different protocols, large networks need more effective software-based security systems that can defend against such unprecedented attacks that traditional security countermeasures prove to be ineffective. Furthermore, to deal with the ever growing onslaught of data and complexity of the network, new security systems need to utilize novel machine learning mechanisms. This paper proposes a softwarebased architecture that acts as a network function virtualization (NFV) to combat malware spread in IoT based networks.We take advantages of the NFV potential to provide new security solutions for IoT environments. To create a scalable and generalized IDS to incorporate a RNN-LSTM learning model that can predict malware attacks in a time sufficient manner for the NFV to deploy the appropriate countermeasures. In addition, we investigate the scalability of the network and present a generalized IDS that deals with a broad range of malware that can be detected. The analysis utilizes the susceptible (S), exposed (E), infected (I), and resistant (R) (SEIR) model to follow the spread of the malware attack and subsequently provide patching to the system. Our analysis focuses primarily on the feasibility and the performance evaluation of such proposed model.

For managing tasks efficiently in a Distributed Het erogeneous Supercomputing System (DHSS) , we re quire a thorough understanding of applications and their intelligent scheduling within the system. For this purpose, an accurate estimation of the execu tion time of applications on various architectures is needed. In this paper we present a framework to ad dress this issue. We propose two techniques, called augmented code profiling and augmented analytical benchmarking, to characterize applications and archi tectures in a DHSS, respectively. These techniques are based on code profiling and analytical benchmarking, respectively and provide a detailed architectural-dependent characterization of DHSS applications.

The increase in scale of cyber networks and the rise in sophistication of cyber-attacks have introduced several challenges in intrusion detection. The primary challenge is the requirement to detect complex multi-stage attacks in realtime by processing the immense amount of traffic produced by present-day networks. In this paper we present PRISM, a hierarchical intrusion detection architecture that uses a novel attacker behavior model-based sampling technique to minimize the realtime traffic processing overhead. PRISM has a unique multi-layered architecture that monitors network traffic distributedly to provide efficiency in processing and modularity in design. PRISM employs a Hidden Markov Model-based prediction mechanism to identify multi-stage attacks and ascertain the attack progression for a proactive response. Furthermore, PRISM introduces a stream management procedure that rectifies the issue of alert reordering when collected from distributed alert reporting systems. To evaluate the performance of PRISM, multiple metrics have been proposed, and various experiments have been conducted on a multi-stage attack dataset. The results exhibit up to 7.5x improvement in processing overhead as compared to a standard centralized IDS without the loss of prediction accuracy while demonstrating the ability to predict different attack stages promptly.

For economic benefits and efficient management of resources, organizations are increasingly moving towards the paradigm of “cloud computing” by which they are allowed on-demand delivery of hardware, software and data as services. However, there are many security challenges which are particularly exacerbated by the multitenancy and virtualization features of cloud computing that allow sharing of resources among potentially untrusted tenants in access controlled cloud data centers. This can result in increased risk of data leakage. To address this risk vulnerability, we propose an efficient risk-aware virtual resource assignment mechanism for clouds multitenant environment. In particular, we introduce the notion of sensitivity in data centers and the objective is to minimize the risk of data leakage. In addition, the risk should not exceed in high sensitivity data centers in comparison to low sensitivity data centers. We present three assignment heuristics and compare their relative performance.

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, Semantic Webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes-HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. We compute one HMAC value for both the schemes and two other verification objects for rgHMAC scheme that are shared with the verifier. We show that the proposed schemes are provably secure with respect to integrity attacks on the structure and/or content of graphs and query results. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes-computation of HMAC tags is about 10 times faster than the computation of digital signatures.