Biblio

Network Function Virtualization (NFV) is a novel paradigm which enables the deployment of network functions on commodity hardware. As such, it also stands for a deployment en-abler for any novel networking function or networking paradigm such as Named Data Networking (NDN), the most promising solution relying on the Information-Centric Networking (ICN) paradigm. However, dedicated solutions for the security and performance orchestration of such an emerging paradigm are still lacking thus preventing its adoption by network operators. In this paper, we propose a first step toward a content-oriented orchestration whose purpose is to deploy, manage and secure an NDN virtual network. We present the way we leverage the TOSCA standard, using a crafted NDN oriented extension to enable the specification of both deployment and operational behavior requirements of NDN services. We also highlight NDN-related security and performance policies to produce counter-measures against anomalies that can either come from attacks or performance incidents.

This paper presents a multilayer protection approach to guard programs against Return-Oriented Programming (ROP) attacks. Upper layers validate most of a program's control flow at a low computational cost; thus, not compromising runtime. Lower layers provide strong enforcement guarantees to handle more suspicious flows; thus, enhancing security. Our multilayer system combines techniques already described in the literature with verifications that we introduce in this paper. We argue that modern versions of x86 processors already provide the microarchitectural units necessary to implement our technique. We demonstrate the effectiveness of our multilayer protection on a extensive suite of benchmarks, which includes: SPEC CPU2006; the three most popular web browsers; 209 benchmarks distributed with LLVM and four well-known systems shown to be vulnerable to ROP exploits. Our experiments indicate that we can protect programs with almost no overhead in practice, allying the good performance of lightweight security techniques with the high dependability of heavyweight approaches.

Realization of an application using Wireless Sensor Networks (WSNs) using Sensor Nodes (SNs) brings in profound advantages of ad-hoc and flexible network deployments. Implementation of these networks face immense challenges due to short wireless range; along with limited power, storage & computational capabilities of SNs. Also, due to the tiny physical attributes of the SNs in WSNs, they are prone to physical attacks. In the context of WSNs, the physical attacks may range from destroying, lifting, replacing and adding new SNs. The work in this paper addresses the threats induced due to physical attacks and, further proposes a methodology to mitigate it. The methodology incorporates the use of newly proposed secured and efficient symmetric and asymmetric key distribution technique based on the additional commodity hardware Trusted Platform Module (TPM). Further, the paper demonstrates the merits of the proposed methodology. With some additional economical cost for the hardware, the proposed technique can fulfill the security requirement of WSNs, like confidentiality, integrity, authenticity, resilience to attack, key connectivity and data freshness.

Security companies have recently realised that mining massive amounts of security data can help generate actionable intelligence and improve their understanding of Internet attacks. In particular, attack attribution and situational understanding are considered critical aspects to effectively deal with emerging, increasingly sophisticated Internet attacks. This requires highly scalable analysis tools to help analysts classify, correlate and prioritise security events, depending on their likely impact and threat level. However, this security data mining process typically involves a considerable amount of features interacting in a non-obvious way, which makes it inherently complex. To deal with this challenge, we introduce MR-TRIAGE, a set of distributed algorithms built on MapReduce that can perform scalable multi-criteria data clustering on large security data sets and identify complex relationships hidden in massive datasets. The MR-TRIAGE workflow is made of a scalable data summarisation, followed by scalable graph clustering algorithms in which we integrate multi-criteria evaluation techniques. Theoretical computational complexity of the proposed parallel algorithms are discussed and analysed. The experimental results demonstrate that the algorithms can scale well and efficiently process large security datasets on commodity hardware. Our approach can effectively cluster any type of security events (e.g., spam emails, spear-phishing attacks, etc) that are sharing at least some commonalities among a number of predefined features.