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1. DDIFT: Decentralized Dynamic Information Flow Tracking for IoT Privacy and Security

   Nikolaos Sapountzis, Ruimin Sun ( December 2018 , Workshop on Decentralized IoT Systems and Security (DISS))

   By 2018, it is no secret to the global networking community: Internet of Things (IoT) devices, usually controlled by IoT applications and applets, have dominated human lives. It has been shown that popular applet platforms (including If This Then That (IFTTT)) are susceptible to attacks that try to exfiltrate private photos, leak user location, etc. As new attacks might show up very frequently, tracking them fast and in an efficient and scalable manner is a daunting task due to the limited (e.g., memory, energy) resources at the IoT/mobile device and the large network size. Towards that direction, in this paper we propose a decentralized Dynamic Information Flow Tracking (DDIFT) framework that overcomes these challenges, better adapts to the IoT context, and further, is able to illuminate IoT applet attacks. In doing so, we leverage the synergy between: (i) a dynamic information flow tracking module that considers the application of tags with different types along with provenance information and runs in the mobile device at a fast timescale, (ii) a forensics analysis module running in the cloud at a slow timescale, (iii) distributed optimization to optimize various functionalities of the above modules as well as their interaction. We show that our framework is able to detect IoT applet attacks with higher accuracy (on average 81% improvement for different URL upload attack scenarios) and decreases resource wastage (on average 71% less memory usage under different integrity attack scenarios) compared to traditional DIFT, opening new horizons for IoT privacy and security. 

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2. Enabling refinable cross-host attack investigation with efficient data flow tagging and tracking

   Ji, Yang ; Lee, Sangho ; Fazzini, Mattia ; Allen, Joey ; Downing, Evan ; Kim, Taesoo ; Orso, Alessandro ; Lee, Wenke ( August 2018 , Proceeding SEC'18 Proceedings of the 27th USENIX Conference on Security Symposium)

   Investigating attacks across multiple hosts is challenging. The true dependencies between security-sensitive files, network endpoints, or memory objects from different hosts can be easily concealed by dependency explosion or undefined program behavior (e.g., memory corruption). Dynamic information flow tracking (DIFT) is a potential solution to this problem, but, existing DIFT techniques only track information flow within a single host and lack an efficient mechanism to maintain and synchronize the data flow tags globally across multiple hosts. In this paper, we propose RTAG, an efficient data flow tagging and tracking mechanism that enables practical cross-host attack investigations. RTAG is based on three novel techniques. First, by using a record-and-replay technique, it decouples the dependencies between different data flow tags from the analysis, enabling lazy synchronization between independent and parallel DIFT instances of different hosts. Second, it takes advantage of systemcall-level provenance information to calculate and allocate the optimal tag map in terms of memory consumption. Third, it embeds tag information into network packets to track cross-host data flows with less than 0.05% network bandwidth overhead. Evaluation results show that RTAG is able to recover the true data flows of realistic cross-host attack scenarios. Performance wise, RTAG reduces the memory consumption of DIFT-based analysis by up to 90% and decreases the overall analysis time by 60%-90% compared with previous investigation systems. 

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3. MITOS: Optimal Decisioning for the Indirect Flow Propagation Dilemma in Dynamic Information Flow Tracking Systems

   Nikolaos Sapountzis, Ruimin Sun ( April 2020 , 40th IEEE International Conference on Distributed Computing Systems)

   Dynamic Information Flow Tracking (DIFT), also called Dynamic Taint Analysis (DTA), is a technique for tracking the information as it flows through a program's execution. Specifically, some inputs or data get tainted and then these taint marks (tags) propagate usually at the instruction-level. While DIFT has been a fundamental concept in computer and network security for the past decade, it still faces open challenges that impede its widespread application in practice; one of them being the indirect flow propagation dilemma: should the tags involved in an indirect flow, e.g., in a control or address dependency, be propagated? Propagating all these tags, as is done for direct flows, leads to overtainting (all taintable objects become tainted), while not propagating them leads to undertainting (information flow becomes incomplete). In this paper, we analytically model that decisioning problem for indirect flows, by considering various tradeoffs including undertainting versus overtainting, importance of heterogeneous code semantics and context. Towards tackling this problem, we design MITOS, a distributed-optimization algorithm, that: decides about the propagation of indirect flows by properly weighting all these tradeoffs, is of low-complexity, is scalable, is able to flexibly adapt to different application scenarios and security needs of large distributed systems. Additionally, MITOS is applicable to most DIFT systems that consider an arbitrary number of tag types, and introduces the key properties of fairness and tag-balancing to the DIFT field. To demonstrate MITOS's applicability in practice, we implement and evaluate MITOS on top of an open-source DIFT, and we shed light on the open problem. We also perform a case-study scenario with a real in-memory only attack and show that MITOS improves simultaneously (i) system's spatio-temporal overhead (up to 40%), and (ii) system's fingerprint on suspected bytes (up to 167\%) compared to traditional DIFT, even though these metrics usually conflict. 

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4. DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning

   https://doi.org/10.1109/CVPRW56347.2022.00295  

   Yang, Li' Rakin ; Fan, Deliang ( June 2022 , IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops)

   Nowadays, one practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). It motivates researchers to develop algorithms that can adapt DNN model to multiple domains sequentially, while still performing well on the past domains, which is known as multi-domain learning. Almost all conventional methods only focus on improving accuracy with minimal parameter update, while ignoring high computing and memory cost during training, which makes it difficult to deploy multi-domain learning into more and more widely used resource-limited edge devices, like mobile phone, IoT, embedded system, etc. During our study in multi-domain training process, we observe that large memory used for activation storage is the bottleneck that largely limits the training time and cost on edge devices. To reduce training memory usage, while keeping the domain adaption accuracy performance, we propose Dynamic Additive Attention Adaption (DA3), a novel memory-efficient on-device multi-domain learning method. DA3 learns a novel additive attention adaptor module, while freezing the weights of the pre-trained backbone model for each domain. Differentiating from prior works, such module not only mitigates activation memory buffering for reducing memory usage during training, but also serves as a dynamic gating mechanism to reduce the computation cost for fast inference. We validate DA3 on multiple datasets against state-of-the-art methods, which shows great improvement in both accuracy and training time. Moreover, we deployed DA3 into the popular NIVDIA Jetson Nano edge GPU, where the measured experimental results show our proposed \mldam reduces the on-device training memory consumption by 19x-37x, and training time by 2x, in comparison to the baseline methods (e.g., standard fine-tuning, Parallel and Series Res. adaptor, and Piggyback). 

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5. A Cross-Platform Cache Timing Attack Framework via Deep Learning

   https://doi.org/10.23919/DATE54114.2022.9774612  

   Ding, Ruyi ; Zhang, Ziyue ; Zhang, Xiang ; Gongye, Cheng ; Fei, Yunsi ; Ding, Aidong A. ( March 2022 , IEEE Design Automation and Test in Europe 2-22)

   While deep learning methods have been adopted in power side-channel analysis, they have not been applied to cache timing attacks due to the limited dimension of cache timing data. This paper proposes a persistent cache monitor based on cache line flushing instructions, which runs concurrently to a victim execution and captures detailed memory access patterns in high- dimensional timing traces. We discover a new cache timing side- channel across both inclusive and non-inclusive caches, different from the traditional “Flush+Flush” timing leakage. We then propose a non-profiling differential deep learning analysis strategy to exploit the cache timing traces for key recovery. We further propose a framework for cross-platform cache timing attack via deep learning. Knowledge learned from profiling a common reference device can be transferred to build models to attack many other victim devices, even in different processor families. We take the OpenSSL AES-128 encryption algorithm as an example victim and deploy an asynchronous cache attack. We target three different devices from Intel, AMD, and ARM processors. We examine various scenarios for assigning the teacher role to one device and the student role to other devices, and evaluate the cross- platform deep-learning attack framework. Experimental results show that this new attack is easily extendable to victim devices • and is more effective than attacks without any prior knowledge. 

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