The Confidential Containers (CoCo) project, as an open-source community initiative, inherits the system architecture of Kata Containers while integrating confidential computing to protect cloud-native container workloads. However, there exists a misalignment in the threat model and trusted computing base (TCB) between Kata Containers and confidential computing. The shifted trust boundaries could potentially expose a range of vulnerabilities, particularly in scenarios where a malicious actor on the host gains access to the CoCo's unprotected control interface. This paper conducts a thorough examination of CoCo's system architecture, exploring the attack surface resulting from the discord in trust boundaries. We have assessed all API endpoints of CoCo's control interface, categorizing them based on their security properties. Drawing from these insights, we have developed a bifurcation approach to splitting CoCo's control interface. This involves establishing an owner-side controller and minimizing the capabilities of the existing host-side controller. Under this framework, the host-side controller is exclusively responsible for allocating and recycling compute resources, while dedicated workload owners can directly manage their containers through alternative secure tunnels. This approach ensures seamless integration with cloud-native orchestration layers and aligns CoCo with the threat model of confidential computing. By doing so, it effectively prevents untrusted hosts from accessing confidential data and interfering with the execution of workloads within protected domains.

Federated learning (FL) relies on a central authority to oversee and aggregate model updates contributed by multiple participating parties in the training process. This centralization of sensitive model updates naturally raises concerns about the trustworthiness of the central aggregation server, as well as the potential risks associated with server failures or breaches, which could result in loss and leaks of model updates. Moreover, recent attacks have demonstrated that, by obtaining the leaked model updates, malicious actors can even reconstruct substantial amounts of private data belonging to training participants. This underscores the critical necessity to rethink the existing FL system architecture to mitigate emerging attacks in the evolving threat landscape. One straightforward approach is to fortify the central aggregator with confidential computing (CC), which offers hardware-assisted protection for runtime computation and can be remotely verified for execution integrity. However, a growing number of security vulnerabilities have surfaced in tandem with the adoption of CC, indicating that depending solely on this singular defense may not provide the requisite resilience to thwart data leaks.

The ability to count and fingerprint devices (optionally, from a given type) that are in a network, and potentially behind a NAT is important not only for network management (e.g., inventory and asset management) but also for business analysis (e.g., product adoption) and security (e.g., to block traffic from a malicious device behind a NAT). As such, researchers have developed a number of solutions to address these questions. However, most existing solutions rely on incidental characteristics of end devices' behaviors. Software updates to end devices or middleboxes (e.g., firewall, NAT) could render existing solutions ineffective. As such, how effective are those solutions in today's settings, e.g., with IoT devices? We propose to answer this answer by evaluating three major approaches that rely on (1) the IP id field, (2) a device's clock skew, and (3) a combination of a device's boot time and the frequency of its TCP timestamp clock, on the network traffic of seventy IoT devices. We show that existing approaches are ineffective with recent IoT devices, and as such the problem of counting devices behind a NAT remains an open problem. Finally, we explore and discuss future potential directions.

This paper addresses the making of security decisions, such as access-control decisions or spam filtering decisions, under uncertainty, when the benefit of doing so outweighs the need to absolutely guarantee these decisions are correct. For instance, when there are limited, costly, or failed communication channels to a policy-decision-point. Previously, local caching of decisions has been proposed, but when a correct decision is not available, either a policy-decision-point must be contacted, or a default decision used. We improve upon this model by using learned classifiers of access control decisions. These classifiers, trained on known decisions, infer decisions when an exact match has not been cached, and uses intuitive notions of utility, damage and uncertainty to determine when an inferred decision is preferred over contacting a remote PDP. Clearly there is uncertainty in the predicted decisions, introducing a degree of risk. Our solution proposes a mechanism to quantify the uncertainty of these decisions and allows administrators to bound the overall risk posture of the system. The learning component continuously refines its models based on inputs from a central policy server in cases where the risk is too high or there is too much uncertainty. We have validated our models by building a prototype system and evaluating it with requests from real access control policies. Our experiments show that over a range of system parameters, it is feasible to use machine learning methods to infer access control policies decisions. Thus our system yields several benefits, including reduced calls to the PDP, reducing latency and communication costs; increased net utility; and increased system survivability.

An understanding of information flow has many applications, including for maximizing marketing impact on social media, limiting malware propagation, and managing undesired disclosure of sensitive information. This paper presents scalable methods for both learning models of information flow in networks from data, based on the Independent Cascade Model, and predicting probabilities of unseen flow from these models. Our approach is based on a principled probabilistic construction and results compare favourably with existing methods in terms of accuracy of prediction and scalable evaluation, with the addition that we are able to evaluate a broader range of queries than previously shown, including probability of joint and/or conditional flow, as well as reflecting model uncertainty. Exact evaluation of flow probabilities is exponential in the number of edges and naive sampling can also be expensive, so we propose sampling in an efficient Markov-Chain Monte-Carlo fashion using the Metropolis-Hastings algorithm -- details described in the paper. We identify two types of data, those where the paths of past flows are known -- attributed data, and those where only the endpoints are known -- unattributed data. Both data types are addressed in this paper, including training methods, example real world data sets, and experimental evaluation. In particular, we investigate flow data from the Twitter microblogging service, exploring the flow of messages through retweets (tweet forwards) for the attributed case, and the propagation of hash tags (metadata tags) and urls for the unattributed case.

Online social networking sites are experiencing tremendous user growth with hundreds of millions of active users. As a result, there is a tremendous amount of user profile data online, e.g., name, birthdate, etc. Protecting this data is a challenge. The task of access policy composition is a tedious and confusing effort for the average user having hundreds of friends. We propose an approach that assists users in composing and managing their access control policies. Our approach is based on a supervised learning mechanism that leverages user provided example policy settings as training sets to build classifiers that are the basis for auto-generated policies. Furthermore, we provide mechanisms to enable users to fuse policy decisions that are provided by their friends or others in the social network. These policies then regulate access to user profile objects. We implemented our approach and, through extensive experimentation, prove the accuracy of our proposed mechanisms.

In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is now much more complex. Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. Previous research has demonstrated that Genetic Programming can be used to infer statements of policies from examples of decisions made [1]. This allows a policy that may not formally have been documented to be discovered automatically, or an underlying set of requirements to be extracted by interpreting user decisions to posed ldquowhat ifrdquo scenarios. This study compares the performance of three different approaches in using genetic programming to infer security policies from decision examples made, namely symbolic regression, IF-THEN rules inference and fuzzy membership functions inference. The fuzzy membership functions inference approach is found to have the best performance in terms of accuracy. Also, the fuzzification and de-fuzzification methods are found to be strongly correlated; incompatibility between them can have strong negative impact to the performance.

In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is becoming much more complex. Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. In this paper we investigate how policies can be inferred automatically using Genetic Programming (GP) from examples of decisions made. This allows us to discover a policy that may not formally have been documented, or else extract an underlying set of requirements by interpreting user decisions to posed "what if" scenarios. Three proof of concept experiments on MLS Bell-LaPadula, Budgetised MLS and Fuzzy MLS policies have been carried out. The results show this approach is promising.

With the increasing need to securely share information, current access control systems are proving too in flexible and difficult to adapt. Recent work on risk-based access control systems has shown promise at resolving the inadequacies of traditional access control systems, and promise to increase information sharing and security. We consider some of the core open problems in risk-based access control systems, namely where and how much risk to take. We propose the use of market mechanisms to determine an organization's risk tolerance and allocation. We show that with the correct incentives, an employee will make optimal choices for the organization. We also comment on how the market can be used to ensure employees behave honestly and detect those who are malicious. Through simulations, we empirically show the advantage of risk-based access control systems and market mechanisms at increasing information sharing and security.

This paper presents a new model for, or rather a new way of thinking about adaptive, risk-based access control. Our basic premise is that there is always inherent uncertainty and risk in access control decisions that is best addressed in an explicit way. We illustrate this concept by showing how the rationale of the well-known, Bell-Lapadula model based, multi-level security (MLS) access control model could be used to develop a risk-adaptive access control model. This new model is more like a fuzzy logic control system than a traditional access control system and hence the name "fuzzy MLS". The long version of this paper is published as an IBM Research Report.

In this paper, we develop two mechanisms to detect DoS attacks against CDN-hosted Web sites and CDN infrastructure servers. First, we propose a novel request routing algorithm which allows CDN servers to effectively distinguish attacks from legitimate requests. Our scheme, based on a keyed hash function, significantly improves the resilience of servers to DoS attacks. Second, we introduce several site allocation algorithms based on binary codes which insure that an attack on one hosted Web site has a limited impact on other hosted sites. Our scheme guarantees that a specified minimum number of servers remain available for non-victimized sites. Together, the proposed schemes significantly improve the resilience of CDN-hosted Web sites, and complement other work on countering distributed DoS attacks.

Detecting attacks against systems has, in practice, largely been delegated to sensors, such as network intrustion detection systems. However, due to the inherent limitations of these systems and the increasing use of encryption in communication, intrusion detection and prevention have once again moved back to the host systems themselves. In this paper, we describe our experiences with building BlueBox, a host-based intrusion detection system. Our approach, based on the technique of system call introspection, can be viewed as creating an infrastructure for defining and enforcing very fine-grained process capabilities in the kernel. These capabilities are specified as a set of rules (policies) for regulating access to system resources on a per executable basis. The language for expressing the rules is intuitive and sufficiently expressive to effectively capture security boundaries.We have prototyped our approach on Linux operating system kernel and have built rule templates for popular daemons such as Apache and wu-ftpd. Our design has been validated by testing against a comprehensive database of known attacks. Our system has been designed to minimize the kernel changes and performance impact and thus can be ported easily to new kernels. We describe the motivation and rationale behind BlueBox, its design, implementation on Linux, and how it relates to prior work on detecting and preventing intrusions on host systems.

No abstract available.

The formal specification and verification of the multiparty session protocol discussed by the authors previously (1988) are presented. The notion of intruder processes is introduced to model intruder actions and countermeasures of the trusted computing bases. It is argued that multilevel network security can be achieved and verified formally independent of the specific transport-layer protocols even in the presence of intruders through the use of (1) a multilevel secure session protocol and (2) a key-distribution protocol which helps establish message confidentiality and integrity across an untrusted network. Both the formal security-policy model and the formal top-level specification (FTLS) of the multiparty session protocol are written in Ina Jo. The inference rules of the logic of authentication are incorporated in Ina Jo transforms to demonstrate the correctness of the key-distribution protocol. Two detailed examples of formal proofs are included.>