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1. EchoSafe: Sonar-based Verifiable Interaction with Intelligent Digital Agents

   https://doi.org/10.1145/3137003.3137014  

   Alanwar, Amr; Balaji, Bharathan; Tian, Yuan; Yang, Shuo; Srivastava, Mani (November 2017, Proceedings of the 1st ACM Workshop on the Internet of Safe Things)

   Voice controlled interactive smart speakers, such as Google Home, Amazon Echo, and Apple HomePod are becoming commonplace in today's homes. These devices listen continually for the user commands, that are triggered by special keywords, such as "Alexa" and "Hey Siri". Recent research has shown that these devices are vulnerable to attacks through malicious voice commands from nearby devices. The commands can be sent easily during unoccupied periods, so that the user may be unaware of such attacks. We present EchoSafe, a user-friendly sonar-based defense against these attacks. When the user sends a critical command to the smart speaker, EchoSafe sends an audio pulse followed by post processing to determine if the user is present in the room. We can detect the user's presence during critical commands with 93.13% accuracy, and our solution can be extended to defend against other attack scenarios, as well. 

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2. OS-level Side Channels without Procfs: Exploring Cross-App Information Leakage on iOS

   https://doi.org/10.14722/ndss.2018.23260  

   Zhang, Xiaokuan; Wang, Xueqiang; Bai, Xiaolong; Zhang, Yinqian; Wang, Xiaofeng (January 2018, Proceedings of the Symposium on Network and Distributed System Security)

   It has been demonstrated in numerous previous studies that Android and its underlying Linux operating systems do not properly isolate mobile apps to prevent cross-app side- channel attacks. Cross-app information leakage enables malicious Android apps to infer sensitive user data (e.g., passwords), or private user information (e.g., identity or location) without requiring specific permissions. Nevertheless, no prior work has ever studied these side-channel attacks on iOS-based mobile devices. One reason is that iOS does not implement procfs— the most popular side-channel attack vector; hence the previously known attacks are not feasible. In this paper, we present the first study of OS-level side-channel attacks on iOS. Specifically, we identified several new side-channel attack vectors (i.e., iOS APIs that enable cross-app information leakage); developed machine learning frameworks (i.e., classification and pattern matching) that combine multiple attack vectors to improve the accuracy of the inference attacks; demonstrated three categories of attacks that exploit these vectors and frameworks to exfiltrate sensitive user information. We have reported our findings to Apple and proposed mitigations to the attacks. Apple has incorporated some of our suggested countermeasures into iOS 11 and MacOS High Sierra 10.13 and later versions. 

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3. Towards Vulnerability Analysis of Voice-Driven Interfaces and Countermeasures for Replay Attacks

   https://doi.org/10.1109/MIPR.2019.00106  

   Malik, Khalid Mahmood; Malik, Hafiz; Baumann, Roland (March 2019, IEEE Conference on Multimedia Information Processing and Retrieval (MIPR))

   Fake audio detection is expected to become an important research area in the field of smart speakers such as Google Home, Amazon Echo and chatbots developed for these platforms. This paper presents replay attack vulnerability of voice-driven interfaces and proposes a countermeasure to detect replay attack on these platforms. This paper introduces a novel framework to model replay attack distortion, and then use a non-learning-based method for replay attack detection on smart speakers. The reply attack distortion is modeled as a higher-order nonlinearity in the replay attack audio. Higher-order spectral analysis (HOSA) is used to capture characteristics distortions in the replay audio. The replay attack recordings are successfully injected into the Google Home device via Amazon Alexa using the drop-in conferencing feature. Effectiveness of the proposed HOSA-based scheme is evaluated using original recorded speech as well as corresponding played back recording to the Google Home via the Amazon Alexa using the drop-in conferencing feature. 

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4. Consortium Blockchain-Based Architecture for Cyber-attack Signatures and Features Distribution

   https://doi.org/10.1109/UEMCON47517.2019.8993036  

   Ajayi, O.; Igbe, O.; Sadaawi, T. (October 2019, 10th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON))

   One of the effective ways of detecting malicious traffic in computer networks is intrusion detection systems (IDS). Though IDS identify malicious activities in a network, it might be difficult to detect distributed or coordinated attacks because they only have single vantage point. To combat this problem, cooperative intrusion detection system was proposed. In this detection system, nodes exchange attack features or signatures with a view of detecting an attack that has previously been detected by one of the other nodes in the system. Exchanging of attack features is necessary because a zero-day attacks (attacks without known signature) experienced in different locations are not the same. Although this solution enhanced the ability of a single IDS to respond to attacks that have been previously identified by cooperating nodes, malicious activities such as fake data injection, data manipulation or deletion and data consistency are problems threatening this approach. In this paper, we propose a solution that leverages blockchain’s distributive technology, tamper-proof ability and data immutability to detect and prevent malicious activities and solve data consistency problems facing cooperative intrusion detection. Focusing on extraction, storage and distribution stages of cooperative intrusion detection, we develop a blockchain-based solution that securely extracts features or signatures, adds extra verification step, makes storage of these signatures and features distributive and data sharing secured. Performance evaluation of the system with respect to its response time and resistance to the features/signatures injection is presented. The result shows that the proposed solution prevents stored attack features or signature against malicious data injection, manipulation or deletion and has low latency. 

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5. PERSIA: a PuzzlE-based InteReSt FloodIng Attack Countermeasure

   https://doi.org/10.1145/3405656.3418709  

   Tourani, Reza; Torres, George; Misra, Satyajayant (September 2020, Proceedings of the 7th ACM Conference on Information-Centric Networking)

   null (Ed.)

   With the proliferation of smart and connected mobile, wireless devices at the edge, Distributed Denial of Service (DDoS) attacks are increasing. Weak security, improper commissioning, and the fast, non-standardized growth of the IoT industry are the major contributors to the recent DDoS attacks, e.g., Mirai Botnet attack on Dyn and Memcached attack on GitHub. Similar to UDP/TCP flooding (common DDoS attack vector), request flooding attack is the primary DDoS vulnerability in the Named-Data Networking (NDN) architecture.In this paper, we propose PERSIA, a distributed request flooding prevention and mitigation framework for NDN-enabled ISPs, to ward-off attacks at the edge. PERSIA's edge-centric attack prevention mechanism eliminates the possibility of successful attacks from malicious end hosts. In the presence of compromised infrastructure (routers), PERSIA dynamically deploys an in-network mitigation strategy to minimize the attack's magnitude. Our experimentation demonstrates PERSIA's resiliency and effectiveness in preventing and mitigating DDoS attacks while maintaining legitimate users' quality of experience (> 99.92% successful packet delivery rate). 

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