Cryptocurrency such as Bitcoin supports anonymous routing (Tor and I2P) because of the application requirements of anonymity and censorship resistance. In permissionless and open networking for cryptocurrency, an adversary can spoof to pretend to use Tor or I2P for anonymity and privacy protection, while in reality it is not using anonymous routing and forwarding its networking directly to the destination peer to reduce the networking overheads. Using profile detection to detect anonymous routing and false claims based on the deterministic features are vulnerable to spoofing, especially in the permissionless cryptocurrency bypassing registration control. We therefore design and build network fingerprinting using the networking behaviors to detect and classify the networking types. We build a network sensor to collect data on an active Bitcoin node connected to the Mainnet and apply supervised machine learning to classify if a peer node is using IP (not anonymous), Tor, or I2P. Our results show that our scheme is effective in accurately detecting the networking types and identifying spoofing attempts through supervised machine learning. Our machine learning model accurately classifies the networking types and detects fake claims of Tor usage with 90% accuracy and false claims of I2P with 87% accuracy in permisionless Bitcoin.

Current cellular networking remains vulnerable to fake base stations due to the lack of base station authentication mechanism or even a key to enable authentication. We design and build a base station certificate (certifying the base station’s public key and location) and a multi-factor authentication (making use of the certificate and the information transmitted in the online radio control communications) to provide authenticity of the source base station and its radio resource control communications. We advance beyond the state-of-the-art research by introducing greater authentication factors and by using blockchain to deliver the base station digital certificate offline, enabling greater key length/security strength and computational/networking efficiency. The multi-factor authentication at the user equipment involves multiple factors verified through the ledger database, the location sensing, and the cryptographic digital signature verification of the cellular radio control communication (SIB1 broadcasting). We analyze our scheme’s security, performance, and the fit to the existing standardized networking protocols. Our work involves the implementation building on X.509 certificate (adapted), smart contract-based blockchain, 5G-standardized radio resource control communications, and software-defined radios. Our analyses show that our scheme effectively defends against more security threats and can enable stronger security, i.e., ECDSA with greater key lengths. Furthermore, our scheme achieves over threefold improvement in computing and energy efficiency on the mobile user equipment compared to previous research.

Blockchain systems and cryptocurrencies rely on the peer-to-peer (P2P) networking to deliver and broadcast the latest blocks and transactions. Such networking is critical, because the delivered information enable the distributed consensus protocol and have direct impacts on the miner's rewards and incomes in the proof-of-work (PoW) cryptocurrencies such as Bitcoin. We study the interplay between the P2P networking and the PoW distributed consensus protocol by controlling the number of P2P connections and analyzing its impacts on the miner's rewards and costs. While the mining reward increases as the connection increases, which is known in the Bitcoin community, the optimal control for maximizing the income (reward minus cost) is bounded and finite in practice due to the networking and mining costs. In this paper, we theoretically model the utility and miner's net-income of the consensus protocol, capturing their dependence on the P2P connectivity control. We apply the model to analyze the optimal control of our active Bitcoin prototypes connected to the Mainnet to demonstrate the model's usefulness. To facilitate and encourage further use of our model and the R&D in cryptocurrency networking in general, we discuss about the model applications and future directions.

5G technology addresses user privacy concerns in cellular networking by encrypting subscriber identifier with ellip-tic curve-based encryption and then transmitting it as ciphertext known as Subscriber Concealed Identifier (SUCI). However, an adversary equipped with a quantum computer can break a discrete logarithm-based elliptic curve algorithm and the user privacy in 5G is at stake against quantum attacks. In this paper, we study the incorporation of the post-quantum ciphers in the SUCI calculation both at the user equipment and at the core network, which involves the shared key exchange and then using the resulting key for the ID encryption. We experiment on different hardware platforms to analyze the PQC key exchange and encryption using NIST-standardized CRYSTALS-Kyber. Our analyses focus on the performances and compare the Kyber-based key exchange and encryption with the current (pre-quantum) Elliptic-curve Diffie-Hellman (ECDH). The performance analyses are critical because mobile networking involves resource-limited and battery-operating mobile devices. We measure and analyze not only the time and CPU-processing performances but also the energy and power performances. Our results show that Kyber-512 is the most efficient and even has better performance (i.e., faster computations, and lower energy consumption) than ECDH.

Cryptocurrency relies on the underlying P2P networking to deliver the up-to-date block and transaction information to synchronize and agree on the processed financial transactions. To maintain healthy connectivity, Bitcoin has implemented a negative reputation scheme based on ban score, which tracks the peer node behavior and results in dropping the peer connection if exceeding a threshold. Previous research in cryptocurrency security showed that such ban score can be both ineffective (easy to bypass to un-ban itself) and vulnerable (against spoofing-based defamation). We design and build a novel positive reputation scheme that addresses such deficiencies of ban score by having the peer behavior only positively impact our score and by disincentivizing switching the network ID for a new reputation score. We provide a general framework for the positive reputation scheme to enable the use of different networking sensing and measurements as well as different aggregate weights for scoring. Then, we provide a concrete instance and build of the positive reputation score scheme focusing on the peer's behavior in relaying unique/new blocks and transactions. We implement, experiment, and analyze our scheme on an active Bitcoin node connected to the Mainnet to show how our scheme provides the reputation and informs the beneficialness of each of its peer connections (delivering new block and transaction information).

Cryptocurrency is designed for anonymous financial transactions to avoid centralized control, censorship, and regulations. To protect anonymity in the underlying P2P networking, Bitcoin adopts and supports anonymous routing of Tor, I2P, and CJDNS. We analyze the networking performances of these anonymous routing with the focus on their impacts on the blockchain consensus protocol. Compared to non-anonymous routing, anonymous routing adds inherent-by-design latency performance costs due to the additions of the artificial P2P relays. However, we discover that the lack of ecosystem plays an even bigger factor in the performances of the anonymous routing for cryptocurrency blockchain. I2P and CJDNS, both advancing the anonymous routing beyond Tor, in particular lack the ecosystem of sizable networking-peer participation. I2P and CJDNS thus result in the Bitcoin experiencing networking partitioning, which has traditionally been researched and studied in cryptocurrency/blockchain security. We focus on I2P and Tor and compare them with the non-anonymous routing because CJDNS has no active public peers resulting in no connectivity. Tor results in slow propagation while I2P yields soft partition, which is a partition effect long enough to have a substantial impact in the PoW mining. To better study and identify the latency and the ecosystem factors of the cryptocurrency networking and consensus costs, we study the behaviors both in the connection manager (directly involved in the P2P networking) and the address manager (informing the connection manager of the peer selections on the backend). This paper presents our analyses results to inform the state of cryptocurrency blockchain with anonymous routing and discusses future work directions and recommendations to resolve the performance and partition issues.

Domain Name System Security Extensions (DNSSEC) uses public-key digital signatures to provide integrity and authentication for DNS query responses. The current standardized DNS for reliable UDP delivery limits DNS response (including the message, signature, and public key) to a maximum of 1232 bytes. Incorporating NIST's post-quantum digital signatures into the DNS protocol results in a response size that exceeds the limit set by the Ethernet standardization, making PQC incompatible with the current standardized DNS. To address the incompatibility and enable PQC to protect the authenticity against the quantum-equipped adversaries, previous research proposed fragmenting the DNSSEC messages. Fragmentation however exposes DNSSEC to Fragmentation Mis-Association threat, traditionally studied in the broader IP fragmentation contexts and not applicable in the current DNSSEC with classical/pre-quantum cipher (no fragmentation needed). We distinguish our work from the previous research incorporating PQC to DNSSEC to defend against the Fragmentation Mis-Association Threat by chaining the fragments and applying cryptographic commit-and-reveal. We also advance the previous research and further reduce the number of packet fragments, which can be particularly useful as the DNSSEC based on UDP is prone to packet transmission failure increasing the chance of the DNS response failure when sent in multiple fragments, by using blockchain to offload and enable the offline delivery of the public key. Our scheme thus even allows the Falcon-512 PQC cipher incorporation to forgo the fragmentation, in contrast to the previous research requiring fragmentation for Falcon-512; the other PQC ciphers, i.e., Dilithium ciphers and Falcon-1024, still require fragmentation in our scheme due to the standardized signature sizes. We implement our scheme and analyze the effectiveness and performances through experimentation.

A fake base station is a well-known security issue in mobile networking. The fake base station exploits the vulnerability in the broadcasting message announcing the base station’s presence, which is called SIB1 in telecommunications protocols such as 4G LTE and 5G NR, to get the user equipment to connect to itself. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discover that a fake base station (which engages the user equipment until parts of the connectivity setup and then discontinues with the protocol) can disable the victim user equipment’s connectivity for an indefinitely long time, which we validate using our threat prototype against current 4G/5G practice. We design and build a detection and blacklisting identification of the fake base station so that the user equipment can avoid the base station and move on to connecting to a legitimate base station for the connectivity availability. Our detection and blacklisting scheme builds on the standardized 5G protocol and requires the implementation only on the user equipment (no further protocol changes), facilitating practicality. Our scheme uses the real-time information of both the time duration and the number of request transmissions, which features are directly impacted by the fake base station’s threat and have not been studied in the previous research. We implement both the base station and the user defense on software-defined radio using open-source 5G software (srsRAN and Open5GS) for validations. We vary the base station implementation to simulate legitimate vs. faulty-but-legitimate vs. fake-and-malicious base stations, where the faulty base station notifies the connectivity disruption and releases the session while the fake base station continues to hold the session. We empirically analyze the detection and identification thresholds, which vary with the fake base station’s power and the channel condition. By strategically selecting the threshold parameters, our scheme provides zero errors, including zero false positives to avoid blacklisting the temporarily faulty base stations which can not provide the connectivity at the time.

While unmanned aerial vehicles (UAVs) are increasingly utilized in many domains, there is a growing concern about location integrity for securely deploying and managing the vehicles. A body of studies tackled this problem, e.g., using hardware sensors, cryptographic mechanisms, and machine learning (ML) approaches, but they concentrate primarily on GPS-related information (e.g., jamming and noise). In this study, we take a different approach that performs the checks by analyzing actual movement information. This new approach keeps track of location updates across the flight path (‘trajectory’) rather than relying only on point-wise GPS-specific features to test the validity of the location information. To this end, we develop a deep sequence method that takes a sequence of flight data samples with a minimal set of attributes capturing location movement over time. Our extensive experimental results support the feasibility of our approach, showing up to 98.9% accuracy for ensuring location consistency (even without referring to any of the GPS-specific features).

Tor implements Mixnet to route communications through multiple relay nodes for anonymous routing. The artificial relay additions, while providing anonymity and making the communications difficult to trace, introduce additional communication latency. We advance the current Tor’s source-driven circuit path selection to improve the networking performance. While Tor builds circuits using the Consensus Directory, we design and build new directories (building on the existing Consensus Directory to increase the relay-node selectivity) and the routing algorithm (using the directories). In contrast to the previous research works which had similar goals to enhance the path selection algorithms for low latency performance, we take a systems approach to build on the existing Tor network (introducing new directories and algorithms but using the rest of the existing Tor mechanism for the circuit construction) and implement and experiment on the real-world Tor network to empirically validate our scheme design and the theoretical analyses. Our scheme controls the tradeoff between anonymity vs. performance so that the source node can control the degree of circuit selectivity or even opt to use the current Tor (Vanilla Tor). To inform the source user’s selection, we study how reducing the relay pool impacts anonymity (quantified and measured in entropy). Our prototype experiment results show significant improvements in latency performance. For example, our scheme controlling the immediate relay latency to be less than 30 milliseconds yields the latency cost to become one-third and the throughput to increase by more than 1.4x compared to the current Vanilla Tor performance.

The MAVLink protocol serves as the cornerstone for control communications between ground control systems (GCS) and unmanned aerial vehicles (UAVs), facilitating essential control communication. Despite its widespread adoption, the protocol's security mechanisms have raised significant concerns. This work focuses on the design vulnerabilities of the MAVLink protocol v2.0 including the deficiency in message authentication code (MAC) mechanism and lapses in sequence number verification. Also, this research reveals the implementation loopholes (as findings) in the well-known GCS software (Mission Planner) for updating the secret key and in the widely used UAV emulation (ArduPilot) for examining invalid timestamps. This breach paves the way for the injection of malicious messages, culminating in the potential hijacking of the UAV. In response to these issues, we propose several countermeasures including a solution using the public key-based signature. The efficacy of both the attack methods and the countermeasures is validated through a series of experiments conducted within a controlled testbed environment.

Cryptocurrency software implements the cryptocurrency operations, including the distributed consensus protocol and the peer-to-peer networking. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. Since we focus on Bitcoin (the most popular cryptocurrency) for implementation and integration, we call our scheme Version++, built on and advancing the current Bitcoin handshaking protocol based on the Version message. Our Version++ protocol providing software assurance is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Our scheme is permissionless since it does not require a centralized trusted authority (unlike the remote software attestation techniques from trusted computing); it is distributed since the peer checks the software assurances of its own peer connections; and it is designed for efficiency/lightweight due to the dynamic nature of the peer connections and the large-scale broadcasting in cryptocurrency networking. Utilizing Merkle Tree for the efficiency of the proof verification, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. Our prototype-based performance analyses demonstrate the lightweight design of Version++. The peer-specific verification grows logarithmically with the number of software files in processing time and in storage. In addition, the Version++ verification overhead is small compared to the overall handshaking process; our measured overhead of 2.22% with minimal networking latency between the virtual machines provides an upper bound in the real-world networking with greater handshaking duration, i.e., the relative Version++ overhead in the real world with physically separate machines will be smaller.

Cryptocurrency software implements the cryptocurrency operations. We design a software assurance scheme for cryptocurrency and advance the cryptocurrency handshaking protocol. More specifically, we focus on Bitcoin for implementation and integration and advance its Version-message based hand-shaking and thus call our scheme Version++, The Version++ protocol provides software assurance, which is distinguishable from the previous research because it is permissionless, distributed, and lightweight to fit its cryptocurrency application. Utilizing Merkle Tree for the verification efficiency, we implement and test Version++ on Bitcoin software and conduct experiments in an active Bitcoin node prototype connected to the Bitcoin Mainnet. This paper for the conference demonstration supplements our technical paper at CCNC 2023 for synergy but highlights the prototyping and demonstration components of our research.

Garbage Collection (GC) is a representative automatic memory manager widely deployed in popular programming languages, such as Java, C\#, and Golang (Go). Through GC, these languages provide programmers with flexibility and safety. However, GC leads to non-trivial overhead in compute and memory resources during application runtime. GC threads compete with non-GC threads (mutators) of an application, which particularly impacts latency-critical (LC) applications and causes long tail latency. Existing GC approaches do not efficiently address the interference, as GC is triggered passively without a global insight of the application; or they employ incremental GC to reduce the interference, while the incremental progress is not dynamically tailored during GC process according to runtime characteristics, which leads to significant performance degradation upon bursty requests.

Location integrity is highly crucial in mobile communications. In this regard, the attack attempting to falsify the position of mobile agents (known as location spoofing) is critical, and thus, detecting such spoofing attacks should be a vital function in the mobile communication setting. With its importance, previous studies explored location spoofing attacks. Still, they mainly used classification techniques based on supervised learning, confining the detector's capability to detect known attack patterns. This study evaluates the feasibility of the autoencoder-based scheme that constructs a profile for legitimate data instances to be resilient to intelligent, previously unseen types of attacks (e.g., evading attacks). We examine three types of autoencoder models designed based on different structures and conduct extensive experiments to measure the performance of the autoencoder models with both standard and variation attacks, with a comparison study with conventional supervised learning-based classification techniques. Our experimental results show that the autoencoder models produce comparable or even better performance than supervised learners, which may be limited only to detecting known patterns.

The distributed cryptocurrency networking is critical because the information delivered through it drives the mining consensus protocol and the rest of the operations. However, the cryptocurrency peer-to-peer (P2P) network remains vulnerable, and the existing security approaches are either ineffective or inefficient because of the permissionless requirement and the broadcasting overhead. We design and build a Lightweight and Identifier-Oblivious eNgine (LION) for the anomaly detection of the cryptocurrency networking. LION is not only effective in permissionless networking but is also lightweight and practical for the computation-intensive miners. We build LION for anomaly detection and use traffic analyses so that it minimally affects the mining rate and is substantially superior in its computational efficiency than the previous approaches based on machine learning. We implement a LION prototype on an active Bitcoin node to show that LION yields less than 1% of mining rate reduction subject to our prototype, in contrast to the state-of-the-art machine-learning approaches costing 12% or more depending on the algorithms subject to our prototype as well, while having detection accuracy of greater than 97% F1-score against the attack prototypes and real-world anomalies. LION therefore can be deployed on the existing miners without the need to introduce new entities in the cryptocurrency ecosystem.

While a body of studies has been carried out for malware detection with its significance, they are often limited to known malware patterns due to the reliance on signature-based or supervised learning approaches. The semi-supervised learning approach would be an option for identifying previously unseen patterns (i.e., zero-day detection); however, our preliminary study reveals critical limitations from existing methods, including (i) the profiling-based approach using an autoencoder can provide better detection but is sensitive to the threshold setting, and (ii) one-class (OC) classification does not require a manual threshold discovery but may be limited with low detection rates. In this paper, we present a new detection method incorporating the concept of autoencoding and OC classification, designed to benefit from strong abstraction by neural networks (using an autoencoder) and the removal of the complex threshold selection (using an OC classifier). For this combined architecture, a challenge is concurrent training of the autoencoder and the OC classifier, which may cause an ill-suited learner due to no reference to malware instances. To this end, we introduce a new model selection method that discovers well-optimized models from a variety of combinations. The experimental results performed with public malware datasets (Meraz’18 and Drebin) show the effectiveness of our presented methods with up to 97.1% accuracy, comparable to the supervised learning-based detection. We also examine the impact of evading attacks using adversarial attack tools, the result of which shows resilience to malware variants with over 99% detection rates.

Capture the Flag (CTF) games improve learners’ engagement and diversify pedagogy for education and training. We design and build a novel CTF game that includes coordination and interaction between the (virtually participating) participants to build fellowship and facilitate networking. Our work builds on the existing CTF components with educational benefits but differs from the traditional CTF approach which presents either an individual game with no participant interaction or a team-based game where the members already know each other and have formed teams. More specifically, we incorporate real-time interactions between participants who are new to each other and engage the participants to collectively solve the CTF challenges. We apply our CTF in both a cybersecurity scholarship program and an academic conference. This paper describes and explains the design, implementation, execution, and validation of our CTF, particularly focusing on the novel goal of including coordination and interaction in order to build fellowships with the participants. We validate our CTF design and build using multiple channels, including the real-time data provided by logging during the session, post-CTF survey, and interviews from the beta-testing session. Our evaluation results show that our novel CTF focusing on coordination and interaction aids in building fellowship and a collaborative environment. We envision our CTF design to help with the rapport building and collaboration among participants in classroom/course settings, workshops, conferences, or technical training sessions.

To realize the computational advantages of quantum mechanics, it is necessary to be able to effectively perform quantum state tomography, and Bayesian methods provide significant opportunities in this regard, being applicable in the uncertainty regimes, where the local approaches based on Cramer-Rao bound become ill-defined. ${\mathcal{P}}{\mathcal{T}}$ -symmetric quantum mechanics provides a unique advantage for Bayesian parameter estimation having an additional degree of freedom, absent in the regular Hermitian case, which significantly enhances the quantum Fisher information and reduces variance of the obtained results. While previous studies have examined ${\mathcal{P}}{\mathcal{T}}$ -symmetric Bayesian parameter estimation in theory, we conduct experimental studies using IBM Quantum Experience and compare them with theoretical calculations. In our approach, the evolution of a qubit controlled by a ${\mathcal{P}}{\mathcal{T}}$-symmetric Hamiltonian is realized by the dilation method using ancilla qubit. We show that our implementation is consistent with the theoretical model and, in particular, we observe a good correspondence between the theoretical and experimental likelihood distributions. Thus we bridge the gap between theory and practical implementations in ${\mathcal{P}}{\mathcal{T}}$-symmetric quantum mechanics and its practical applications in quantum information science.

Bitcoin P2P networking is especially vulnerable to networking threats because it is permissionless and does not have the security protections based on the trust in identities, which enables the attackers to manipulate the identities for Sybil and spoofing attacks. The Bitcoin node keeps track of its peer’s networking misbehaviors through ban scores. In this paper, we investigate the security problems of the ban-score mechanism and discover that the ban score is not only ineffective against the Bitcoin Message-based DoS (BM-DoS) attacks but also vulnerable to the Defamation attack as the network adversary can exploit the ban score to defame innocent peers. To defend against these threats, we design an anomaly detection approach that is effective, lightweight, and tailored to the networking threats exploiting Bitcoin’s ban-score mechanism. We prototype our threat discoveries against a real-world Bitcoin node connected to the Bitcoin Mainnet and conduct experiments based on the prototype implementation. The experimental results show that the attacks have devastating impacts on the targeted victim while being cost-effective on the attacker side. For example, an attacker can ban a peer in two milliseconds and reduce the victim’s mining rate by hundreds of thousands of hash computations per second. Furthermore, to counter the threats, we empirically validate our detection countermeasure’s effectiveness and performances against the BM-DoS and Defamation attacks.

Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the practicality of the PCN multi-path routing by better modeling the system to incorporate the cost of routing fee and the privacy requirement of the channel balance. We design our Auto-Tune algorithm to optimize the routing concerning both the success rate and the routing fee and utilizing the limited channel capacity information (due to the privacy of the PCN user, the channel balance information is withheld). The simulation result shows Auto-Tune outperforms the current PCN implementation based on single-path routing in the success rate. We compare Auto-Tune against the state-of-the-art Flash algorithm, utilizing the channel-balance information, violating the PCN user privacy, and diverging from current implementation practices. Auto-Tune achieves the routing fee close to the optimal fee obtained by Flash, and its success rate is also close to the success rate achieved by Flash.

For resource utilization efficiency, latency critical (LC) services are commonly co-located with best-effort batch jobs in datacenter servers. Many LC services, such as Cassandra and HBase, run in Java Virtual Machine (JVM). We find that LC services often experience heavy-tailed latency due to performance interference of the concurrent garbage collection (GC) as well as multi-tenancy. The root cause is a semantic gap of resource allocation between JVM and the underlying Linux OS in multi-tenant systems. That is, the OS is unaware of the characteristics of different kinds of threads in JVM (i.e., GC threads and LC worker threads), which may lead to GC threads competing for CPUs; JVM is unaware of the resource utilization in the OS, which may trigger CPU-intensive GC operations when CPUs are busy. Furthermore, we find that co-located batch jobs can interfere with LC services due to Simultaneous Multi-Threading (SMT).

Software-defined radio (SDR) and the softwarization of the wireless and mobile systems enable intelligent processing and control in wireless networking. We design and build a lightweight code assurance proof scheme for wireless system software implementations. More specifically, our scheme assures that a wireless user/prover holds the correct software codes, e.g., the correct version, for its wireless networking implementations. In contrast to the previous research for code attestation in trusted computing, our scheme forgoes hardware-based security and real-time networking, thus substantially increasing the application feasibility. We further design our scheme to be efficient in computing by using a Merkle tree for the efficiency of the verification of the assurance proof. We implement our scheme for proof-of-concept on srsRAN (a popular open-source software for cellular technology) and conduct preliminary measurements to demonstrate the lightweight design. We envision our scheme to be orthogonal and supplementary to the previous trustworthy code attestation because it provides different properties (assurance vs. attestation) and because the lightweight aspect yields greater applicability and lower overheads in hardware and networking. Our scheme will therefore be appropriate for the wireless/mobile environment which uses broadcasting (where receiving/verifications occur more frequently than transmitting/generations) and whose devices are resource-constrained.

Post-quantum ciphers (PQC) provide cryptographic algorithms for public-key ciphers which are computationally secure against the threats from quantum-computing adversaries. Because the devices in mobile computing are limited in hardware and power, we analyze the PQC power overheads. We implement the new NIST PQCs across a range of device platforms to simulate varying resource capabilities, including multiple Raspberry Pis with different memories, a laptop, and a desktop computer. We compare the power measurements with the idle cases as our baseline and show the PQCs consume considerable power. Our results show that PQC ciphers can be feasible in the resource-constrained devices (simulated with varying Raspberry Pis in our case); while PQCs consume greater power than the classical cipher of RSA for laptop and desktop, they consume comparable power for the Raspberry Pis.

While blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic, real-time manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results evaluated with real and simulated traffic data support the effectiveness of our security mechanism and design choices based on the AE structure, with the approximate detection performance to the supervised learning methods only through the profiling of normal instances. The measured time complexity is sufficiently cheap to perform real-time analysis, with less than 1.4 msec for per-instance testing on a single core setting.

The impact of malware attacks has been getting more significant, targeting critical infrastructures as well as commodity computing devices. A body of studies has been carried out for detecting malware with its devastating impacts, but they are often limited to known malware attacks due to the nature of the signature-based and supervised machine learning approaches. The semi-supervised learning approach would be an option for identifying previously unseen types of malware attacks (i.e., zero-day detection); however, our preliminary studies suggest two limitations in this avenue: (1) one class (OC) classifiers can be limited with relatively low detection rates, and (2) the profiling-based approach (using an autoencoder) may yield better detection performance but under the assumption of the "ideal" threshold setting. In this paper, we tackle these challenges and present a new detection method, which combines the concepts of autoencoding and OC classification, to benefit from strong abstractions by neural networks (using an autoencoder) but to remove the necessity of the complex threshold selection (using an OC classifier). Our extensive experimental results with a recent malware dataset (Meras’18) show the effectiveness of our method with up to 96% accuracy for zero-day malware detection, which is comparable to the supervised learning-based detection (limited to known types of malware). The proposed method also shows the resilience to adversarial attacks, yielding better performance for identifying synthetic samples generated to evade the detection process than supervised learning algorithms.

Modern vehicles are equipped with vehicular sensors for smart navigation, vehicle state awareness, and other intelligent operations. Despite the previous belief that the sensor operations stay within a vehicle, as it is designed to be, we study information leakage through the tire pressure monitoring system (TPMS) sensors and the corresponding privacy breach. We demonstrate that, using a low-cost and off-the-shelf software defined radio (SDR), an unauthorized attacker can track uniquely-identifiable sensor IDs up to 40 meters away from the vehicle. To address the issue and protect vehicular privacy, we also propose an effective and lightweight TPMS ID randomization scheme and analyze its security and the implementation costs.

Recent trends in the cybersecurity workforce have recognized that effective solutions for complex problems require collective efforts from individuals with diverse sets of knowledge, skills, and abilities. Therefore, the growing need to train students in team collaboration skills propelled educators in computer science and engineering to adopt team-based pedagogical strategies. Team-based pedagogy has shown success in enhancing students' knowledge in course subjects and their motivation in learning. However, it is limited in offering concrete frameworks specifically focusing on how to teach team collaboration skills. As part of an interdisciplinary effort, we draw on Transactive Memory Systems Theory-a communication theory that explains how individuals in groups learn who knows what and organize who does what-in developing a Team Knowledge Sharing Assignment as a tool for student teams to structure their team collaboration processes. This paper reports a result of a case study in designing and facilitating the assignment for cybersecurity students enrolled in a scholarship program. Students' evaluations and the instructor's assessment reveal that the assignment made a positive impact on students' team collaboration skills by helping them successfully identify their team members' expertise and capitalize on their team's knowledge resources when delegating functional roles. Based on this case study, we offer practical suggestions on how the assignment could be used for various classes or cybersecurity projects and how instructors could maximize its benefits.

Random number generators are used in computing, security, and cryptographic applications. The random number outputs retaining the entropy without bias is critical for the integrity of the random number generator and the relying applications. $\mathcal{P}\mathcal{T}$-symmetric quantum mechanics provides a mechanism to improve the detection of the bias in randomness since it can convert two non-orthogonal quantum states into orthogonal ones. We propose a new randomness bias detection and quantification method by using $\mathcal{P}\mathcal{T}$-symmetric quantum mechanics. Previous studies show that, if the bias amount is a priori known, its presence can be detected by, in principle, a single measurement in $\mathcal{P}\mathcal{T}$-symmetric system. Taking advantage of an additional degree of freedom provided by $\mathcal{P}\mathcal{T}$ symmetry, we extend this approach to the case when the bias amount is not a priori known. We provide an algorithm and the analysis using $\mathcal{P}\mathcal{T}$ symmetry for randomness bias detection and quantification.

Blockchain technology is gaining popularity in industries and governments for information monitoring, distribution, and tracking. Thanks to the built-in security properties, blockchain provides security integrity to various decentralized applications (dApps) involving distributed operations including supply chain, healthcare, banking, internet of things (IoT), and networking. In this paper, we propose a generic blockchain framework (GBF) for applying two blockchains to the dApp systems, one to establish trust and the other to use the trust for securing applications. GBF provides a generally applicable framework and addresses the foundational questions of the blockchain objectives, participants, and the underlying distributed consensus protocol in use. We apply GBF to various case studies from the recent blockchain research to show its effectiveness and generality. We also prototype GBF using smart contract and experiment on CloudLab for preliminary evaluations focusing on the application-general metrics. We propose GBF to facilitate blockchain/dApp research and development by providing the initial framework and enable the preliminary analyses so that the decentralized applications with specific aims can build on GBF.

Public Key Infrastructure (PKI) generates and distributes digital certificates to provide the root of trust for securing digital networking systems. To continue securing digital networking in the quantum era, PKI should transition to use quantum-resistant cryptographic algorithms. The cryptography community is developing quantum-resistant primitives/algorithms, studying, and analyzing them for cryptanalysis and improvements. National Institute of Standards and Technology (NIST) selected finalist algorithms for the post-quantum digital signature cipher standardization, which are Dilithium, Falcon, and Rainbow. We study and analyze the feasibility and the processing performance of these algorithms in memory/size and time/speed when used for PKI, including the key generation from the PKI end entities (e.g., a HTTPS/TLS server), the signing, and the certificate generation by the certificate authority within the PKI. The transition to post-quantum from the classical ciphers incur changes in the parameters in the PKI, for example, Rainbow I significantly increases the certificate size by 163 times when compared with RSA 3072. Nevertheless, we learn that the current X.509 supports the NIST post-quantum digital signature ciphers and that the ciphers can be modularly adapted for PKI. According to our empirical implementations-based study, the post-quantum ciphers can increase the certificate verification time cost compared to the current classical cipher and therefore the verification overheads require careful considerations when using the post-quantum-cipher-based certificates.

Blockchain and the proof-of-work (PoW) distributed consensus protocol rely on peer-to-peer (P2P) networking. We build a PoW P2P simulator for the modeling and analyses of permissionless blockchain networking. Our simulator utilizes a built-in randomness generator for the simulations, has an easy-to-use interface and intuitive visualization, supports dynamic/programmable control and modifications, and can generate simulation data for further processing. We publish our simulator in open source to facilitate its use for blockchain and P2P networking research and especially recommend it for scalability research or preliminary testing. To highlight its features and capabilities, we demonstrate the simulator use in this paper to analyze the recent blockchain security research, including 51% attack, eclipse, partitioning, and DoS attack.

Blockchain relies on the underlying peer-to-peer (p2p) networking to broadcast and get up-to-date on the blocks and transactions. It is therefore imperative to have high p2p connectivity for the quality of the blockchain system operations. High p2p networking connectivity ensures that a peer node is connected to multiple other peers providing a diverse set of observers of the current state of the blockchain and transactions. However, in a permissionless blockchain network, using the peer identifiers—including the current approach of counting the number of distinct IP addresses and port numbers—can be ineffective in measuring the number of peer connections and estimating the networking connectivity. Such current approach is further challenged by the networking threats manipulating the identifiers. We build a robust estimation engine for the p2p networking connectivity by sensing and processing the p2p networking traffic. We implement a working Bitcoin prototype connected to the Bitcoin Mainnet to validate and improve our engine’s performances and evaluate the estimation accuracy and cost efficiency of our estimation engine.

Peer connectivity plays a significant role in a blockchain network since any poor connectivity may result in the nodes operating on outdated data (e.g., cryptocurrency transactions). Although connectivity information is maintained by individual nodes, such identifier-based information might be unreliable due to the possibility of bogus identifiers. This paper tackles the problem of peer connectivity estimation through data-driven analytics of blockchain traffic for reliable blockchain networking. We define a set of variables to represent traffic characteristics and estimate peer connectivity from the collected data using a machine learning methodology. We also investigate the feasibility of feature prioritization to minimize estimation complexities. Our experimental results show that the presented estimation mechanism makes accurate predictions, with less than 0.1 difference between the measurement and estimation for over 99.7% of predictions. The time complexity measured on a commodity machine shows a microsecond scale for completing a single prediction task, enabling real-time operations.

Software-defined wide-area network (SD-WAN) is an emerging and advanced networking platform extending software-defined networking (SDN) across multiple networking domains. Because SD-WAN manages the data plane in the networking domains separated by the public Internet, SDWAN provides a distinct environment and challenges from SDN, including greater risks for the security threats injecting control plane communications from attackers residing outside of the SDN domain. We design and build blockchain-coordinating controllers (BCC) to secure control communications of the SD-WAN controller network formed by the distributed controllers spread across multiple domains. BCC provides resiliency against the security threats in the control plane where an attacker compromises controller communications to manipulate the coordination and the operations of the other controllers. More specifically, BCC provides secure control communications even when up to n controllers’ networking credentials are compromised. BCC is also designed for modularity so that it applies generally across the controller implementations. We prototype BCC using Ethereum and smart contract on CloudLab to validate its effectiveness and efficiency. We experiment on geographically separate nodes on CloudLab and show that BCC achieves the distributed consensus at sub-second level for certificate/key distribution and for network-wide control communication synchronization.

Set-valued data and social network provide opportunities to mine useful, yet potentially security-sensitive, information. While there are mechanisms to anonymize data and protect the privacy separately in set-valued data and in social network, the existing approaches in data privacy do not address the privacy issue which emerge when publishing set-valued data and its correlative social network simultaneously. In this paper, we propose a privacy attack model based on linking the set-valued data and the social network topology information and a novel technique to defend against such attack to protect the individual privacy. To improve data utility and the practicality of our scheme, we use local generalization and partial suppression to make set-valued data satisfy the grouped ρ-uncertainty model and to reduce the impact on the community structure of the social network when anonymizing the social network. Experiments on real-life data sets show that our method outperforms the existing mechanisms in data privacy and, more specifically, that it provides greater data utility while having less impact on the community structure of social networks.

Distributed banking platforms and services forgo centralized banks to process financial transactions. For example, M-Pesa provides distributed banking service in the developing regions so that the people without a bank account can deposit, withdraw, or transfer money. The current distributed banking systems lack the transparency in monitoring and tracking of distributed banking transactions and thus do not support auditing of distributed banking transactions for accountability. To address this issue, this paper proposes a blockchain-based distributed banking (BDB) scheme, which uses blockchain technology to leverage its built-in properties to record and track immutable transactions. BDB supports distributed financial transaction processing but is significantly different from cryptocurrencies in its design properties, simplicity, and computational efficiency. We implement a prototype of BDB using smart contract and conduct experiments to show BDB’s effectiveness and performance. We further compare our prototype with the Ethereum cryptocurrency to highlight the fundamental differences and demonstrate the BDB’s superior computational efficiency.

Collaborative intrusion detection system (CIDS) shares the critical detection-control information across the nodes for improved and coordinated defense. Software-defined network (SDN) introduces the controllers for the networking control, including for the networks spanning across multiple autonomous systems, and therefore provides a prime platform for CIDS application. Although previous research studies have focused on CIDS in SDN, the real-time secure exchange of the detection-relevant information (e.g., the detection signature) remains a critical challenge. In particular, the CIDS research still lacks robust trust management of the SDN controllers and the integrity protection of the collaborative defense information to resist against the insider attacks transmitting untruthful and malicious detection signatures to other participating controllers. In this paper, we propose a blockchain-enabled collaborative intrusion detection in SDN, taking advantage of the blockchain's security properties. Our scheme achieves three important security goals: to establish the trust of the participating controllers by using the permissioned blockchain to register the controller and manage digital certificates, to protect the integrity of the detection signatures against malicious detection signature injection, and to attest the delivery/update of the detection signature to other controllers. Our experiments in CloudLab based on a prototype built on Ethereum, Smart Contract, and IPFS demonstrates that our approach efficiently shares and distributes detection signatures in real-time through the trustworthy distributed platform.

Computational skewness is a significant challenge in multi-tenant data-parallel clusters that introduce dynamic heterogeneity of machine capacity in distributed data processing. Previous efforts to addressing skewness mostly focus on batch jobs based on the assumption that processing time is linearly dependent on the size of partitioned data. However, they are illsuited for iterative machine learning (ML) jobs, which (1) exhibit a non-linear relationship between the size of partitioned parameters and processing time within each iteration, and (2) show an explicit binding relationship between input data and parameters for parameter update. In this paper, we present FlexPara, a parameter partition approach that leverages the non-linear relationship and provisions adaptive tasks to match the distinct machine capacity so as to address the skewness in iterative ML jobs on data-parallel clusters. FlexPara first predicts task processing time based on a capacity model designed for iterative ML jobs without the linear assumption. It then partitions parameters to parallel tasks through proactive parameter reassignment. Such reassignment can significantly reduce network transmission cost incurred by input data movement due to the binding relationship. We implement FlexPara in Spark and evaluate it with various ML jobs. Experimental results show that compared to hash partition, FlexPara speeds up the execution by up to 54% and 43% in private and NSF Chameleon clusters, respectively.

Moving target defense (MTD) is useful for thwarting network reconnaissance and preventing unauthorized access. While previous research in MTD focuses on protecting the endnodes, we leverage software-defined networking to implement MTD on the data-plane switches, which significantly decreases the controller communication overhead and enables quicker defense response to reduce the attack impact. This paper not only randomizes the IP addresses for MTD but also uses the IP addresses for synchronization across the nodes in the networking path by generating hash-chain-based synchronization signatures. Our scheme is practical as it builds on and encodes the existing IP addresses for randomization to construct a modular solution independent to the routing/flow rule implementation and does not incur additional networking overhead except for the seed distribution (which can occur offline). Our scheme is also effective (the attacker's required cost to achieve timely network reconnaissance increases by more than an order of magnitude than the previous state-of-the-art having the controller actuate the MTD randomization) and scalable (the relative overhead cost of our scheme becomes smaller as the network grows). We analyze our scheme and implement and experiment it on an Open vSwitch-based testbed and on CloudLab to validate these properties.

Energy is required for networking and computation and is a valuable resource for unplugged systems such as mobile, sensor, and embedded systems. Energy denial-of-service (DoS) attack where a remote attacker exhausts the victim’s battery via networking remains a critical challenge for the device availability. While prior literature proposes mitigation- and detection-based solutions, we propose to eliminate the vulnerability entirely by offloading the power requirements to the entity who makes the networking requests. To do so, we build communication channels using wireless charging signals (as opposed to the traditional radio-frequency signals), so that the communication and the power transfer are simultaneous and inseparable, and use the channels to build power-positive networking (PPN). PPN also offloads the computation-based costs to the requester, enabling authentication and other tasks considered too power-hungry for battery-operated devices. In this article, we study the energy DoS attack impacts on off-the-shelf embedded system platforms (Raspberry Pi and the ESP 8266 system-on-chip (SoC) module), present PPN, implement and build a Qi-charging-technology-compatible prototype, and use the prototype for evaluations and analyses. Our prototype, built on the hardware already available for wireless charging, effectively defends against energy DoS and supports simultaneous power and data transfer.

We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.

A recent increase in mobile and IoT devices has led to the advancement of wireless charging. The state-of-the-art wireless charging systems operate at a particular frequency, controlled by the explicit networking from the power-receiving device (which relays the battery status information, useful for the frequency selection), but such control is not designed to cope with the variations in the power receiving device's placements and alignments (which are more significant in near-field and pseudo-tightly coupled charging applications, as more charging pads are being deployed in the public domains and serving heterogeneous clients). In this work, we analyze the impact of the power transfer performance caused by the power receiver's load, distance, and coil alignment/overlap and introduce cognitive wireless charger (CWC), which adaptively controls the operating frequency in real-time using implicit feedback from sensing for optimal operations. In addition to the theoretical and LTSpice-based simulation analysis, we build a prototype compatible to the Qi standard and analyze the performance of CWC with it. Through our analyses, we establish that frequency control achieves performance gains in inductive-coupling charging applications and is sensitive to the variations in the placement and alignment between the power-transmitting and the power-receiving coils. Our prototype, when CWC is turned off, has comparable performance to the commercial-grade Qi wireless chargers and, with CWC enabled, demonstrates significant improvement over modern wireless chargers.

Wireless charging provides a convenient way to charge various mobile and IoT devices. Prior work in state-of-theart wireless charging systems operates at a frequency controlled by explicit networking from the power-receiving devices and is designed for the environment when the participating devices are perfectly aligned with each other. The need for the finer control due to the devices’ misalignment is increasing in near-field and pseudo-tightly coupled charging applications, as more charging pads, are being deployed in the public domains and serving heterogeneous clients. Because inductive-coupled charging applications are sensitive to the placement and alignment variations between the power-transmitting and the power-receiving coils, we design and build Cognitive Wireless Charger (CWC). CWC adaptively controls the operating frequency in real time using implicit feedback for optimal power transfer operations. This demo is to supplement our paper about CWC [1]. In this demo, we showcase the impact on power transfer performance caused by the variations in the placement and alignment between the charging coils of power transmitter and power receiver and demonstrate the performance improvement provided by CWC.

Physical-layer group secret-key (GSK) generation is an effective way of generating secret keys in wireless networks, wherein the nodes exploit inherent randomness in the wireless channels to generate group keys, which are subsequently applied to secure messages while broadcasting, relaying, and other network-level communications. While existing GSK protocols focus on securing the common source of randomness from external eavesdroppers, they assume that the legitimate nodes of the group are trusted. In this paper, we address insider attacks from the legitimate participants of the wireless network during the key generation process. Instead of addressing conspicuous attacks such as switching-off communication, injecting noise, or denying consensus on group keys, we introduce stealth attacks that can go undetected against state-of- the-art GSK schemes. We propose two forms of attacks, namely: (i) different-key attacks, wherein an insider attempts to generate different keys at different nodes, especially across nodes that are out of range so that they fail to recover group messages despite possessing the group key, and (ii) low-rate key attacks, wherein an insider alters the common source of randomness so as to reduce the key-rate. We also discuss various detection techniques, which are based on detecting anomalies and inconsistencies on the channel measurements at the legitimate nodes. Through simulations we show that GSK generation schemes are vulnerable to insider-threats, especially on topologies that cannot support additional secure links between neighbouring nodes to verify the attacks.

Energy is required for networking and computation and is a valuable resource for unplugged embedded systems. Energy DoS attack where a remote attacker exhausts the victim's battery by sending networking requests remains a critical challenge for the device availability. While prior literature proposes mitigation- and detection-based solutions, we propose to eliminate the vulnerability entirely by offloading the power requirements to the entity who makes the networking requests. To do so, we build communication channels using wireless charging signals, so that the communication and the power transfer are simultaneous and inseparable, and use the channels to build power-positive networking (PPN). PPN also offloads the computation-based costs to the requester, enabling authentication and other tasks considered too power-hungry for battery-operated devices. Furthermore, because we use the charging signal for bidirectional networking, the design requires no additional hardware beyond that for wireless charging. In this paper, we present PPN, implement a Qi-compatible prototype, and use the prototype to analyze the performance.

Wireless network dynamically allocates channel resources to improve spectral efficiency and, to avoid collisions, has its users cooperate with each other using a medium access control (MAC) protocol. However, MAC assumes user compliance and can be detrimental when a user misbehaves. An attacker who compromised the network can launch more devastating denial-of-service (DoS) attacks than a network outsider by sending excessive reservation requests to waste bandwidth, by listening to the control messages and conducting power-efficient jamming, by falsifying information to manipulate the network control, and so on. We build SecureMAC to defend against such insider threats while retaining the benefits of coordination between the cooperative users. SecureMAC is comprised of four components: channelization to prevent excessive reservations, randomization to thwart reactive targeted jamming, coordination to counter control-message aware jamming and resolve over-reserved and under-reserved spectrum, and power attribution to determine each node's contribution to the received power. Our theoretical analyses and implementation evaluations demonstrate superior performance over previous approaches, which either ignore security issues or give up the benefit of cooperation when under attack by disabling user coordination (such as the Nash equilibrium of continuous wideband transmission). In realistic scenarios, our SecureMAC implementation outperforms such schemes by 76-159 percent.

To defend against network reconnaissance for unauthorized access of the packet forwarding path, we leverage software-defined networking (SDN) and build moving target defense (MTD) by randomizing network addresses. We distinguish our work from prior research by implementing MTD at the data plane and on all nodes along the forwarding path. Thus, our scheme is fast and lightweight in operation (significantly decreasing the controller communication overhead) and enables quicker security response to reduce the attack impact (as opposed to having the attack impact all the way to the endhost destination). We validate our work on an Open vSwitch-based testbed and show that the attacker's cost to achieve timely network reconnaissance increases by more than an order of magnitude than having the controller actuate the MTD.

Recent findings have shown that network and system attacks in Software-Defined Networks (SDNs) have been caused by malicious network applications that misuse APIs in an SDN controller. Such attacks can both crash the controller and change the internal data structure in the controller, causing serious damage to the infrastructure of SDN-based networks. To address this critical security issue, we introduce a security framework called AEGIS to prevent controller APIs from being misused by malicious network applications. Through the run-time verification of API calls, AEGIS performs a fine-grained access control for important controller APIs that can be misused by malicious applications. The usage of API calls is verified in real time by sophisticated security access rules that are defined based on the relationships between applications and data in the SDN controller. We also present a prototypical implementation of AEGIS and demonstrate its effectiveness and efficiency by performing six different controller attacks including new attacks we have recently discovered.

In wireless networks, users share a transmission medium. For efficient channel use, wireless systems often use a Medium Access Control (MAC) protocol to perform channel coordination by having each node announce its usage intentions and other nodes avoid making conflicting transmissions. Traditionally, such announcements are made on a common control channel. However, this control channel is vulnerable to jamming because its location is pre-assigned and known to attackers. Furthermore, the announcements themselves provide information useful for jamming. We focus on a situation where transmitters share spectrum in the presence of intelligent and insider jammers capable of adaptively changing their jamming patterns. Despite the complex threat model, we propose a simple MAC scheme, called SimpleMAC, that effectively counters network compromise and MAC-aware jamming attacks. We then study the optimal adversarial behavior and analyze the performance of the proposed scheme theoretically, through Monte Carlo simulations, and by implementation on the WARP software-defined radio platform. In comparison to the Nash equilibrium alternative of disabling the MAC protocol, SimpleMAC quickly attains vastly improved performance and converges to the optimal solution (over six-fold improvement in SINR and 50% gains in channel capacity in a realistic mobile scenario).

Modern-day train operations rely on wireless communications. Unlike other mobile systems, the train vehicle operations are tightly interwound with and remain physically close to the railway and the trackside infrastructure, providing a suitable platform to deploy leaky-waveguide-based communication. Due to the train system's safety-critical application and its exposure to the public, it is critical to address security in train communications. To investigate the availability of leaky waveguide communications, we first study prior leaky waveguide implementations in train systems and, based on those studies, construct a model to characterize the path loss of inside-waveguide propagation and the repeater implementations. Using our model, we analyze the jamming impact and contrast with jamming in free space without a waveguide. As a result, we establish that jamming the waveguide takes advantage of the waveguide infrastructure to extend its impact beyond the traditional jamming range and breaks the spatial dependence on the jamming source.

In wireless networks, users share a transmission medium. To increase the efficiency of channel usage, wireless systems often use a Medium Access Control (MAC) protocol to perform channel coordination by having each node announce its usage intentions; other nodes avoid making conflicting transmissions minimizing interference both to the node that has announced its intentions and to a node that cooperates by avoiding transmissions during the reserved slot. Traditionally, in a multi-channel environment, such announcements are made on a common control channel. However, this control channel is vulnerable to jamming because its location is pre-assigned and known to attackers. Furthermore, the announcements themselves provide information useful for jamming. In this paper, we focus on a situation where multiple wireless transmitters share spectrum in the presence of intelligent and possibly insider jammers capable of dynamically and adaptively changing their jamming patterns.

Wireless access points largely fall into three categories: home and small business networks, enterprise networks, and hotspots. Wi-Fi Protected Access (WPA) provides solutions to home, small business, and enterprise networks, but hotspots typically are not secured at the Medium Access Control (MAC) layer because they are open to the public. In this paper, we present a scheme that establishes a secure wireless connection between a client device and an access point in these open environments. In our approach, we use hierarchical identity-based cryptography, and each user uses its MAC address as its public key. Our scheme ensures confidentiality and integrity even in the presence of colluding attackers.