Publications
Mission resilience experimentation and evaluation testbed
November 28, 2022
Conference Paper
Published in:
IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.
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Summary
As the complexity of DoD systems increases exponentially, the DoD continues to struggle with understanding and improving the resilience of its mission software. The Applied Resilience for Mission Systems (ARMS) Testbed is an environment that enables resilience improvement by experimentation and assessment of different mission system architectures and approaches. This Testbed consists of components for deploying mission system software for testing, capturing system performance, generating traffic, introducing disruptions into the mission system, orchestrating controlled experiments, and assessing and comparing the performance of mission systems. This paper covers the implementation of this Testbed, analysis for mission resilience comparisons, and their application to an operational terrestrial network architecture. Additionally, we introduce the Distance to Failure metric for comparing the resilience of arbitrary mission systems variations.
Summary
As the complexity of DoD systems increases exponentially, the DoD continues to struggle with understanding and improving the resilience of its mission software. The Applied Resilience for Mission Systems (ARMS) Testbed is an environment that enables resilience improvement by experimentation and assessment of different mission system architectures and approaches. This...
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On randomization in MTD systems
November 7, 2022
Conference Paper
Published in:
Proc. of the 9th ACM Workshop on Moving Target Defense, MTD ’22, 7 November 2022.
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Randomization is one of the main strategies in providing security in moving-target-defense (MTD) systems. However, randomization has an associated cost and estimating this cost and its impact on the overall system is crucial to ensure adoption of the MTD strategy. In this paper we discuss our experience in attempting to estimate the cost of path randomization in a message transmission system that used randomization of paths in the network. Our conclusions are (i) the cost crucially depends on the underlying network control technology, (ii) one can reduce this cost by better implementation, and (iii) reducing one type of cost may result in increased costs of a different type, for example a higher device cost. These suggest that estimating the cost of randomization is a multivariable optimization problem that requires a full understanding of the system components.
Summary
Randomization is one of the main strategies in providing security in moving-target-defense (MTD) systems. However, randomization has an associated cost and estimating this cost and its impact on the overall system is crucial to ensure adoption of the MTD strategy. In this paper we discuss our experience in attempting to...
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The tale of discovering a side channel in secure message transmission systems
August 13, 2022
Conference Paper
Published in:
The Conf. for Failed Approaches and Insightful Losses in Cryptology, CFAIL, 13 August 2022.
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Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life efficiency of the system, and while failing to achieve this initial goal, let to the discovery a side-channel that affects the security of a wide range of SMT implementations.
Summary
Secure message transmission (SMT) systems provide information theoretic security for point-to-point message transmission in networks that are partially controlled by an adversary. This is the story of a research project that aimed to implement a flavour of SMT protocols that uses "path hopping" with the goal of quantifying the real-life...
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Advances in cross-lingual and cross-source audio-visual speaker recognition: The JHU-MIT system for NIST SRE21
June 28, 2022
Conference Paper
Published in:
Speaker and Language Recognition Workshop, Odyssey 2022, pp. 213-220.
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We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This evaluation exposes new challenges, including cross-source–i.e., CTS vs. AfV– and cross-language trials. Each speaker can speak two or three languages among English, Mandarin and Cantonese. For the audio track, we evaluated embeddings based on Res2Net and ECAPA-TDNN, where the former performed the best. We used PLDA based back-ends trained on previous SRE and VoxCeleb and adapted to a subset of Mandarin/Cantonese speakers. Some novel contributions of this submission are: the use of neural bandwidth extension (BWE) to reduce the mismatch between the AFV and CTS conditions; and invariant representation learning (IRL) to make the embeddings from a given speaker invariant to language. Res2Net with neural BWE was the best monolithic system. We used a pre-trained RetinaFace face detector and ArcFace embeddings for the visual track, following our NIST SRE19 work. We also included a new system using a deep pyramid single shot face detector and face embeddings trained on Crystal loss and probabilistic triplet loss, which performed the best. The number of face embeddings in the test video was reduced by agglomerative clustering or weighting the embedding based on the face detection confidence. Cosine scoring was used to compare embeddings. For the multi-modal track, we just added the calibrated likelihood ratios of the audio and visual conditions, assuming independence between modalities. The multi-modal fusion improved Cprimary by 72% w.r.t. audio.
Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This...
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Advances in speaker recognition for multilingual conversational telephone speech: the JHU-MIT system for NIST SRE20 CTS challenge
June 27, 2022
Conference Paper
Published in:
Speaker and Language Recognition Workshop, Odyssey 2022, pp. 338-345.
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Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures based on ResNet, squeeze-excitation ResNets, Transformers and EfficientNets. Though squeeze-excitation ResNets and EfficientNets provide superior performance in in-domain tasks like VoxCeleb, regular ResNet34 was more robust in the challenge scenario. On the contrary, squeeze-excitation networks over-fitted to the training data, mostly in English. We also proposed a novel PLDA mixture and k-NN PLDA back-ends to handle the multilingual trials. The former clusters the x-vector space expecting that each cluster will correspond to a language family. The latter trains a PLDA model adapted to each enrollment speaker using the nearest speakers–i.e., those with similar language/channel. The k-NN back-end improved Act. Cprimary (Cp) by 68% in SRE16-19 and 22% in SRE20 Progress w.r.t. a single adapted PLDA back-end. Our best single system achieved Act. Cp=0.110 in SRE20 progress. Meanwhile, our best fusion obtained Act. Cp=0.110 in the progress–8% better than single– and Cp=0.087 in the eval set.
Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures...
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Toward improving EN adoption: Bridging the gap between stated intention and actual use
June 3, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-7
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact Tracing (PACT) protocol and related projects to assist the public health objective of slowing the spread of SARS-CoV-2 through digital contact tracing. The joint Google and Apple deployed protocol (Google-Apple Exposure Notifications, also known as GAEN or EN), which became the de facto standard in the U.S., employs the same features as detailed by PACT. The protocol leverages smartphone Bluetooth communications to alert users of potential contact with those carrying the COVID-19 virus in a way that preserves the privacy of both the known-infected individual, and the users receiving the alert. Contact tracing and subsequent personal precautions are more effective at reducing disease spread when more of the population participates, but there are known difficulties with the adoption of novel technology. In order to help the U.S. Centers for Disease Control and Prevention (CDC) and U.S. state-level public health teams address these difficulties, a team of staff from MIT's Lincoln Laboratory (MIT LL) and Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) focused on studying user perception and information needs.
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact...
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The thundering herd: Amplifying kernel interference to attack response times
May 4, 2022
Conference Paper
Published in:
2022 IEEE 28th Real-Time and Embedded Technology and Applications Symp., RTAS, 4-6 May 2022.
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Embedded and real-time systems are increasingly attached to networks. This enables broader coordination beyond the physical system, but also opens the system to attacks. The increasingly complex workloads of these systems include software of varying assurance levels, including that which might be susceptible to compromise by remote attackers. To limit the impact of compromise, u-kernels focus on maintaining strong memory protection domains between different bodies of software, including system services. They enable limited coordination between processes through Inter-Process Communication (IPC). Real-time systems also require strong temporal guarantees for tasks, and thus need temporal isolation to limit the impact of malicious software. This is challenging as multiple client threads that use IPC to request service from a shared server will impact each other's response times. To constrain the temporal interference between threads, modern u-kernels often build priority and budget awareness into the system. Unfortunately, this paper demonstrates that this is more challenging than previously thought. Adding priority awareness to IPC processing can lead to significant interference due to the kernel's prioritization logic. Adding budget awareness similarly creates opportunities for interference due to the budget tracking and management operations. In both situations, a Thundering Herd of malicious threads can significantly delay the activation of mission-critical tasks. The Thundering Herd effects are evaluated on seL4 and results demonstrate that high-priority threads can be delayed by over 100,000 cycles per malicious thread. This paper reveals a challenging dilemma: the temporal protections u-kernels add can, themselves, provide means of threatening temporal isolation. Finally, to defend the system, we identify and empirically evaluate possible mitigations, and propose an admission-control test based upon an interference-aware analysis.
Summary
Embedded and real-time systems are increasingly attached to networks. This enables broader coordination beyond the physical system, but also opens the system to attacks. The increasingly complex workloads of these systems include software of varying assurance levels, including that which might be susceptible to compromise by remote attackers. To limit...
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Cross-language attacks
April 22, 2022
Conference Paper
Published in:
Network and Distributed System Security (NDSS) Symposium 2022.
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Memory corruption attacks against unsafe programming languages like C/C++ have been a major threat to computer systems for multiple decades. Various sanitizers and runtime exploit mitigation techniques have been shown to only provide partial protection at best. Recently developed ‘safe’ programming languages such as Rust and Go hold the promise to change this paradigm by preventing memory corruption bugs using a strong type system and proper compile-time and runtime checks. Gradual deployment of these languages has been touted as a way of improving the security of existing applications before entire applications can be developed in safe languages. This is notable in popular applications such as Firefox and Tor. In this paper, we systematically analyze the security of multi-language applications. We show that because language safety checks in safe languages and exploit mitigation techniques applied to unsafe languages (e.g., Control-Flow Integrity) break different stages of an exploit to prevent control hijacking attacks, an attacker can carefully maneuver between the languages to mount a successful attack. In essence, we illustrate that the incompatible set of assumptions made in various languages enables attacks that are not possible in each language alone. We study different variants of these attacks and analyze Firefox to illustrate the feasibility and extent of this problem. Our findings show that gradual deployment of safe programming languages, if not done with extreme care, can indeed be detrimental to security.
Summary
Memory corruption attacks against unsafe programming languages like C/C++ have been a major threat to computer systems for multiple decades. Various sanitizers and runtime exploit mitigation techniques have been shown to only provide partial protection at best. Recently developed ‘safe’ programming languages such as Rust and Go hold the promise...
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Preventing Kernel Hacks with HAKCs
April 22, 2022
Conference Paper
Published in:
Network and Distributed System Security (NDSS) Symposium 2022.
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Commodity operating system kernels remain monolithic for practical and historical reasons. All kernel code shares a single address space, executes with elevated processor privileges, and has largely unhindered access to all data, including data irrelevant to the completion of a specific task. Applying the principle of least privilege, which limits available resources only to those needed to perform a particular task, to compartmentalize the kernel would realize major security gains, similar to microkernels yet without the major redesign effort. Here, we introduce a compartmentalization design, called a Hardware-Assisted Kernel Compartmentalization (HAKC), that approximates least privilege separation, while minimizing both developer effort and performance overhead. HAKC divides code and data into separate partitions, and specifies an access policy for each partition. Data is owned by a single partition, and a partition’s access-control policy is enforced at runtime, preventing unauthorized data access. When a partition needs to transfer control flow to outside itself, data ownership is transferred to the target, and transferred back upon return. The HAKC design allows for isolating code and data from the rest of the kernel, without utilizing any additional Trusted Computing Base while compartmentalized code is executing. Instead, HAKC relies on hardware for enforcement. Loadable kernel modules (LKMs), which dynamically load kernel code and data providing specialized functionality, are the single largest part of the Linux source base. Unfortunately, their collective size and complexity makes LKMs the cause of the majority of CVEs issued for the Linux kernel. The combination of a large attack surface in kernel modules, and the monolithic design of the Linux kernel, make LKMs ideal candidates for compartmentalization. To demonstrate the effectiveness of our approach, we implement HAKC in Linux v5.10 using extensions to the Arm v8.5-A ISA, and compartmentalize the ipv6.ko LKM, which consists of over 55k LOC. The average overhead measured in Apachebench tests was just 1.6%–24%. Additionally, we compartmentalize the nf_tables.ko packet filtering LKM, and measure the combined impact of using both LKMs. We find a reasonable linear growth in overhead when both compartmentalized LKMs are used. Finally, we measure no significant difference in performance when using the compartmentalized ipv6.ko LKM over the unmodified LKM during real-world web browsing experiments on the Alexa Top 50 websites.
Summary
Commodity operating system kernels remain monolithic for practical and historical reasons. All kernel code shares a single address space, executes with elevated processor privileges, and has largely unhindered access to all data, including data irrelevant to the completion of a specific task. Applying the principle of least privilege, which limits...
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Quantifying bias in face verification system
April 1, 2022
Journal Article
Published in:
Comput. Sci. Math. Forum 2022, 3(1), 6
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Machine learning models perform face verification (FV) for a variety of highly consequential applications, such as biometric authentication, face identification, and surveillance. Many state-of-the-art FV systems suffer from unequal performance across demographic groups, which is commonly overlooked by evaluation measures that do not assess population-specific performance. Deployed systems with bias may result in serious harm against individuals or groups who experience underperformance. We explore several fairness definitions and metrics, attempting to quantify bias in Google’s FaceNet model. In addition to statistical fairness metrics, we analyze clustered face embeddings produced by the FV model. We link well-clustered embeddings (well-defined, dense clusters) for a demographic group to biased model performance against that group. We present the intuition that FV systems underperform on protected demographic groups because they are less sensitive to differences between features within those groups, as evidenced by clustered embeddings. We show how this performance discrepancy results from a combination of representation and aggregation bias.
Summary
Machine learning models perform face verification (FV) for a variety of highly consequential applications, such as biometric authentication, face identification, and surveillance. Many state-of-the-art FV systems suffer from unequal performance across demographic groups, which is commonly overlooked by evaluation measures that do not assess population-specific performance. Deployed systems with bias...
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