Master of Science in Computer Science

thesisIn today's IP networks, any host can send packets to any other host irrespective of whether the recipient is interested in communicating with the sender or not. The downside of this openness is that every host is vulnerable to an attack by any other host. We ob- serve that this unrestricted network access (network ambient authority) from compromised systems is also a main reason for data exfiltration attacks within corporate networks. We address this issue using the network version of capability based access control. We bring the idea of capabilities and capability-based access control to the domain of networking. CeNet provides policy driven, fine-grained network level access control enforced in the core of the network (and not at the end-hosts) thereby removing network ambient authority. Thus CeNet is able to limit the scope of spread of an attack from a compromised host to other hosts in the network. We built a capability-enabled SDN network where communication privileges of an endpoint are limited according to its function in the network. Network capabilities can be passed between hosts, thereby allowing a delegation-oriented security policy to be realized. We believe that this base functionality can pave the way for the realization of sophisticated security policies within an enterprise network. Further we built a policy manager that is able to realize Role-Based Access Control (RBAC) policy based network access control using capability operations. We also look at some of the results of formal analysis of capability propagation models in the context of networks

Code Renewability for Native Software Protection

Software protection aims at safeguarding assets embedded in software by preventing and delaying reverse engineering and tampering attacks. This paper presents an architecture and supporting tool flow to renew parts of native applications dynamically. Renewed and diversified code and data belonging to either the original application or to linked-in protections are delivered from a secure server to a client on demand. This results in frequent changes to the software components when they are under attack, thus making attacks harder. By supporting various forms of diversification and renewability, novel protection combinations become available, and existing combinations become stronger. The prototype implementation is evaluated on a number of industrial use cases

Autonomic Rejuvenation of Cloud Applications as a Countermeasure to Software Anomalies

Failures in computer systems can be often tracked down to software anomalies of various kinds. In many scenarios, it could be difficult, unfeasible, or unprofitable to carry out extensive debugging activity to spot the causes of anomalies and remove them. In other cases, taking corrective actions may led to undesirable service downtime. In this article we propose an alternative approach to cope with the problem of software anomalies in cloud-based applications, and we present the design of a distributed autonomic framework that implements our approach. It exploits the elastic capabilities of cloud infrastructures, and relies on machine learning models, proactive rejuvenation techniques and a new load balancing approach. By putting together all these elements, we show that it is possible to improve both availability and performance of applications deployed over heterogeneous cloud regions and subject to frequent failures. Overall, our study demonstrates the viability of our approach, thus opening the way towards it adoption, and encouraging further studies and practical experiences to evaluate and improve it

Systematic Classification of Side-Channel Attacks: A Case Study for Mobile Devices

Contains fulltext : 187230.pdf (preprint version ) (Open Access

Secure entity authentication

According to Wikipedia, authentication is the act of confirming the truth of an attribute of a single piece of a datum claimed true by an entity. Specifically, entity authentication is the process by which an agent in a distributed system gains confidence in the identity of a communicating partner (Bellare et al.). Legacy password authentication is still the most popular one, however, it suffers from many limitations, such as hacking through social engineering techniques, dictionary attack or database leak. To address the security concerns in legacy password-based authentication, many new authentication factors are introduced, such as PINs (Personal Identification Numbers) delivered through out-of-band channels, human biometrics and hardware tokens. However, each of these authentication factors has its own inherent weaknesses and security limitations. For example, phishing is still effective even when using out-of-band-channels to deliver PINs (Personal Identification Numbers). In this dissertation, three types of secure entity authentication schemes are developed to alleviate the weaknesses and limitations of existing authentication mechanisms: (1) End user authentication scheme based on Network Round-Trip Time (NRTT) to complement location based authentication mechanisms; (2) Apache Hadoop authentication mechanism based on Trusted Platform Module (TPM) technology; and (3) Web server authentication mechanism for phishing detection with a new detection factor NRTT. In the first work, a new authentication factor based on NRTT is presented. Two research challenges (i.e., the secure measurement of NRTT and the network instabilities) are addressed to show that NRTT can be used to uniquely and securely identify login locations and hence can support location-based web authentication mechanisms. The experiments and analysis show that NRTT has superior usability, deploy-ability, security, and performance properties compared to the state-of-the-art web authentication factors. In the second work, departing from the Kerb eros-centric approach, an authentication framework for Hadoop that utilizes Trusted Platform Module (TPM) technology is proposed. It is proven that pushing the security down to the hardware level in conjunction with software techniques provides better protection over software only solutions. The proposed approach provides significant security guarantees against insider threats, which manipulate the execution environment without the consent of legitimate clients. Extensive experiments are conducted to validate the performance and the security properties of the proposed approach. Moreover, the correctness and the security guarantees are formally proved via Burrows-Abadi-Needham (BAN) logic. In the third work, together with a phishing victim identification algorithm, NRTT is used as a new phishing detection feature to improve the detection accuracy of existing phishing detection approaches. The state-of-art phishing detection methods fall into two categories: heuristics and blacklist. The experiments show that the combination of NRTT with existing heuristics can improve the overall detection accuracy while maintaining a low false positive rate. In the future, to develop a more robust and efficient phishing detection scheme, it is paramount for phishing detection approaches to carefully select the features that strike the right balance between detection accuracy and robustness in the face of potential manipulations. In addition, leveraging Deep Learning (DL) algorithms to improve the performance of phishing detection schemes could be a viable alternative to traditional machine learning algorithms (e.g., SVM, LR), especially when handling complex and large scale datasets