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Publication Restricted An information and communication theoretical analysis of the nervous system towards internet of nano-bio things(Koç University, 2018) Abbasi, Naveed Ahmed; Akan, Özgür Barış; 0000-0003-2523-3858; Koç University Graduate School of Sciences and Engineering; Electrical Engineering; 6647
Publication Metadata only Byzantines can also learn from history: fall of centered clipping in federated learning(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Özfatura, Emre; Gündüz, Deniz; Department of Computer Engineering; Özfatura, Ahmet Kerem; Küpçü, Alptekin; Department of Computer Engineering; Koç Üniversitesi İş Bankası Enfeksiyon Hastalıkları Uygulama ve Araştırma Merkezi (EHAM) / Koç University İşbank Center for Infectious Diseases (KU-IS CID); Graduate School of Sciences and Engineering; College of Engineering;The increasing popularity of the federated learning (FL) framework due to its success in a wide range of collaborative learning tasks also induces certain security concerns. Among many vulnerabilities, the risk of Byzantine attacks is of particular concern, which refers to the possibility of malicious clients participating in the learning process. Hence, a crucial objective in FL is to neutralize the potential impact of Byzantine attacks and to ensure that the final model is trustable. It has been observed that the higher the variance among the clients' models/updates, the more space there is for Byzantine attacks to be hidden. As a consequence, by utilizing momentum, and thus, reducing the variance, it is possible to weaken the strength of known Byzantine attacks. The centered clipping (CC) framework has further shown that the momentum term from the previous iteration, besides reducing the variance, can be used as a reference point to neutralize Byzantine attacks better. In this work, we first expose vulnerabilities of the CC framework, and introduce a novel attack strategy that can circumvent the defences of CC and other robust aggregators and reduce their test accuracy up to %33 on best-case scenarios in image classification tasks. Then, we propose a new robust and fast defence mechanism that is effective against the proposed and other existing Byzantine attacks.Publication Metadata only Data-agnostic model poisoning against federated learning: a graph autoencoder approach(IEEE-Inst Electrical Electronics Engineers Inc, 2024) Li, Kai; Zheng, Jingjing; Yuan, Xin; Ni, Wei; Poor, H. Vincent; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Department of Electrical and Electronics Engineering; ; College of Engineering;This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL. © 2005-2012 IEEE.Publication Metadata only Federated learning for pedestrian detection in vehicular networks(Institute of Electrical and Electronics Engineers Inc., 2023) Bennis, Mehdi; Elgabli, Anis; Gündüz, Deniz; Karaağaç, Sercan; Department of Electrical and Electronics Engineering; Kümeç, Feyzi Ege; Reyhanoğlu, Aslıhan; Kar, Emrah; Turan, Buğra; Ergen, Sinem Çöleri; Department of Electrical and Electronics Engineering; Graduate School of Sciences and Engineering; College of Engineering; Koc University Ford Otosan Automotive Technologies Laboratory (KUFOTAL)Vehicular connectivity is foreseen to increase road safety by enabling connected vehicle applications. On the other hand, machine learning (ML) methods are provisioned to increase road safety by supporting object detection and assisted driving. Recently, distributed ML methods, which rely on data transmission between a parameter server and vehicular edge devices, are introduced to develop intelligent transportation systems. In this paper, we investigate the feasibility of the usage of a distributed ML algorithm, federated learning (FL), to detect pedestrians by using vehicular networks. We first provide a comprehensive overview of the proposed scheme, then highlight the methodology to enable FL-based pedestrian detection from the images obtained by vehicle cameras. We further present experimental validation results for communication resource utilization, and pedestrian detection accuracy by using convolutional neural networks (CNNs) and deep neural networks (DNNs) layers in our model architecture for an FL scheme. We obtain 90% pedestrian detection accuracy with our FL scheme. © 2023 IEEE.Publication Metadata only FLAGS simulation framework for federated learning algorithms(Institute of Electrical and Electronics Engineers Inc., 2023) Department of Computer Engineering; Lodhi, Ahnaf Hannan; Shamsizade, Toghrul; Al Asaad, Omar Mohammad; Akgün, Barış; Özkasap, Öznur; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of EngineeringFederated Learning (FL) provides an effective mechanism for distributed learning. However, it is expected to operate in a highly diverse setting with distinct behaviors from the participating nodes as well as dynamic network conditions. The FL performance, therefore, is subject to change due to the highly transitory nature of the overall system. An efficient simulation framework must be flexible to allow a range of participant behaviors, interactions, and environment characteristics. In this demo paper, we present the Federated Learning Algorithm Simulation (FLAGS) framework that we propose as a lightweight FL implementation and testing platform. FLAGS framework allows for a wide range of device behaviors and cooperative mechanisms, enabling rapid testing of multiple FL algorithms. © 2023 IEEE.Publication Metadata only Flexible and cognitive radio access technologies for 5G and beyond(Institution of Engineering and Technology, 2020) Arslan, Hüseyin; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116Standards for 5G and beyond will require communication systems with a much more flexible and cognitive design to support a wide variety of services including smart vehicles, smart cities, smart homes, IoTs, and remote health. Although future 6G technologies may look like an extension of their 5G counterparts, new user requirements, completely new applications and use-cases, and networking trends will bring more challenging communication engineering problems. New communication paradigms in different layers will be required, in particular in the physical layer of future wireless communication systems. This comprehensive book is intended to be both a tutorial on flexible and cognitive radio access technologies for 5G and beyond and an advanced overview for technical professionals and managers in the communications industry, as well as researchers in academia and industry. The authors cover enabling radio access technologies for 5G and beyond, not only from a standard specific angle (like 5G) but also by considering future trends beyond 5G. Rather than specific standard implementations, the book covers a wide variety of technologies and their uses. The presentations are both descriptive and mathematical in nature to cater to readers who need mathematical description as well as readers who do not. The book is written at a level suited to readers who already have a background in electrical engineering and basic wireless communications.Publication Metadata only Intelligent edge computing: state-of-the-art techniques and applications(Institute of Electrical and Electronics Engineers Inc., 2020) Department of Computer Engineering; Department of Computer Engineering; N/A; Gürsoy, Attila; Özkasap, Öznur; Gill, Waris; Faculty Member; Faculty Member; PhD Student; Department of Computer Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 8745; 113507; N/ATo enable intelligent decisions at the network edge, supervised and unsupervised machine learning techniques and their variations are highly utilized in recent research studies. These include techniques and the corresponding applications such as detecting manufacturing faults in a smart factory setting, monitoring patient activities and health problems in smart health systems, detecting security attacks on the Internet of Things devices, and finding the rare events in the audio signals. In this paper, we present an extensive review of state-of-the-art techniques and applications of intelligent edge computing and provide classification and discussion of various approaches in this field.Publication Open Access M-stability: threshold security meets transferable utility(Association for Computing Machinery (ACM), 2021) Department of Computer Engineering; Biçer, Osman; Küpçü, Alptekin; Yıldız, Burcu; Faculty Member; Department of Computer Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 168060; N/AUse of game theory and mechanism design in cloud security is a well-studied topic. When applicable, it has the advantages of being efficient and simple compared to cryptography alone. Most analyses consider two-party settings, or multi-party settings where coalitions are not allowed. However, many cloud security problems that we face are in the multi-party setting and the involved parties can almost freely collaborate with each other. To formalize the study of disincentivizing coalitions from deviating strategies, a well-known definition named k-resiliency has been proposed by Abraham et al. (ACM PODC '06). Since its proposal, k-resiliency and related definitions are used extensively for mechanism design. However, in this work we observe the shortcoming of k-resiliency. That is, although this definition is secure, it is too strict to use for many cases and rule out secure mechanisms as insecure. To overcome this issue, we propose a new definition named ?.,""-repellence against the presence of a single coalition to replace k-resiliency. Our definition incorporates transferable utility in game theory as it is realistic in many distributed and multi-party computing settings. We also propose m-stability definition against the presence of multiple coalitions, which is inspired by threshold security in cryptography. We then show the advantages of our novel definitions on three mechanisms, none of which were previously analyzed against coalitions: incentivized cloud computation, forwarding data packages in ad hoc networks, and connectivity in ad hoc networks. Regarding the former, our concepts improve the proposal by Küpçü (IEEE TDSC '17), by ensuring a coalition-proof mechanism.Publication Metadata only Moderated redactable blockchains: a definitional framework with an efficient construct(Springer Science and Business Media Deutschland GmbH, 2020) Dousti, M. S.; Department of Computer Engineering; Küpçü, Alptekin; Faculty Member; Department of Computer Engineering; College of Engineering; 168060Blockchain is a multiparty protocol to reach agreement on the order of events, and to record them consistently and immutably without centralized trust. In some cases, however, the blockchain can benefit from some controlled mutability. Examples include removing private information or unlawful content, and correcting protocol vulnerabilities which would otherwise require a hard fork. Two approaches to control the mutability are: moderation, where one or more designated administrators can use their private keys to approve a redaction, and voting, where miners can vote to endorse a suggested redaction. In this paper, we first present several attacks against existing redactable blockchain solutions. Next, we provide a definitional framework for moderated redactable blockchains. Finally, we propose a provable and efficient construct, which applies a single digital signature per redaction, achieving a much simpler and secure result compared to the prior art in the moderated setting.Publication Restricted Nanoscale and bio-inspired communicatin techniques for the internet of bio-nano things(Koç University, 2017) Kuşçu, Murat; Akan, Özgür Barış; 0000-0003-2523-3858; Koç University Graduate School of Sciences and Engineering; Electrical and Electronics Engineering; 6647