Fitness trackers - wearables that continuously record a wearer's step count and related activity data - are quickly gaining in popularity. Apart from being useful for individuals seeking a more healthy lifestyle, their data is also being used in court and by insurance companies to adjust premiums. For these use cases, it is essential to ensure authenticity and integrity of data. Here we demonstrate a flaw in the way firmware for Withings' Activit 'e is verified, allowing an adversary to compromise the tracker itself. This type of attack has so far not been applied to fitness trackers. Vendors have started mitigating previous attacks, which manipulated data by interfering with wireless channels, or by physically moving the tracker to fool sensors. Hardware similarities amongst different trackers suggest findings can be transferred to other tracker as well.
in 2014 performed a large scale analysis of the security of embedded firmware. They presented a correlation technique that allows for propagation of existing vulnerability information to similar firmware that has previously not been identified as vulnerable. One downside of their approach is that it is rather granular and cannot identify previously unknown firmware modification attacks @cite_28 . present a general discussion of firmware modification attacks and then focus their work on HP's LaserJet printers, identifying an issue allowing an adversary with print permissions to update the affected printer using modified firmware @cite_25 . Coppola in 2013 studied the firmware update procedure of Withings' wireless scale. He combined hardware and software reverse engineering to exploit a critical flaw in the update process, allowing him to upload arbitrary firmware to the scale @cite_27 .
@cite_28 @cite_27 @cite_25
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The ability to update firmware is a feature that is found in nearly all modern embedded systems. We demonstrate how this feature can be exploited to allow attackers to inject malicious firmware modifications into vulnerable embedded devices. We discuss techniques for exploiting such vulnerable functionality and the implementation of a proof of concept printer malware capable of network reconnaissance, data exfiltration and propagation to general purpose computers and other embedded device types. We present a case study of the HP-RFU (Remote Firmware Update) LaserJet printer firmware modification vulnerability, which allows arbitrary injection of malware into the printer’s firmware via standard printed documents. We show vulnerable population data gathered by continuously tracking all publicly accessible printers discovered through an exhaustive scan of IPv4 space. To show that firmware update signing is not the panacea of embedded defense, we present an analysis of known vulnerabilities found in third-party libraries in 373 LaserJet firmware images. Prior research has shown that the design flaws and vulnerabilities presented in this paper are found in other modern embedded systems. Thus, the exploitation techniques presented in this paper can be generalized to compromise other embedded systems. Keywords-Embedded system exploitation; Firmware modification attack; Embedded system rootkit; HP-RFU vulnerability. As embedded systems are more than ever present in our society, their security is becoming an increasingly important issue. However, based on the results of many recent analyses of individual firmware images, embedded systems acquired a reputation of being insecure. Despite these facts, we still lack a global understanding of embedded systems' security as well as the tools and techniques needed to support such general claims. In this paper we present the first public, large-scale analysis of firmware images. In particular, we unpacked 32 thousand firmware images into 1.7 million individual files, which we then statically analyzed. We leverage this large-scale analysis to bring new insights on the security of embedded devices and to underline and detail several important challenges that need to be addressed in future research. We also show the main benefits of looking at many different devices at the same time and of linking our results with other large-scale datasets such as the ZMap's HTTPS survey. In summary, without performing sophisticated static analysis, we discovered a total of 38 previously unknown vulnerabilities in over 693 firmware images. Moreover, by correlating similar files inside apparently unrelated firmware images, we were able to extend some of those vulnerabilities to over 123 different products. We also confirmed that some of these vulnerabilities altogether are affecting at least 140K devices accessible over the Internet. It would not have been possible to achieve these results without an analysis at such wide scale. We believe that this project, which we plan to provide as a firmware unpacking and analysis web service, will help shed some light on the security of embedded devices. Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes. It enables the attacker to turn all the city lights on or off, to permanently brick them, or to exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lamps in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already). To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key (for each device type) that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product. Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device's firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated. Variation in performance and power across manufactured parts and their operating conditions is an accepted reality in aggressive CMOS processes. This paper considers challenges and opportunities in identifying this variation and methods to combat it for improved computing systems. We introduce the notion of instruction-level vulnerability (ILV) to expose variation and its effects to the software stack for use in architectural compiler optimizations. To compute ILV, we quantify the effect of voltage and temperature variations on the performance and power of a 32-bit, RISC, in-order processor in 65nm TSMC technology at the level of individual instructions. Results show 3.4ns (68FO4) delay variation and 26.7x power variation among instructions, and across extreme corners. Our analysis shows that ILV is not uniform across the instruction set. In fact, ILV data partitions instructions into three equivalence classes. Based on this classification, we show how a low-overhead robustness enhancement techniques can be used to enhance performance by a factor of 1.1×-5.5×. Today large software systems (i.e., giants) thrive in commodity markets, but are untrustworthy due to their numerous and inevitable software bugs that can be exploited by the adversary. Thus, the best hope of security is that some small, simple, and trustworthy software components (i.e., wimps) can be protected from attacks launched by adversary-controlled giants. However, wimps in isolation typically give up a variety of basic services (e.g., file system, networking, device I O), trading usefulness and viability with security. Among these basic services, isolated I O channels remained an unmet challenge over the past three decades. Isolated I O is a critical security primitive for a myriad of applications (e.g., secure user interface, remote device control). With this primitive, isolated wimps can transfer I O data to commodity peripheral devices and the data secrecy and authenticity are protected from the co-existing giants. This thesis addresses this challenge by proposing a new security architecture to provide services to isolated wimps. Instead of restructuring the giants or bloating the Trusted Computing Base that underpins wimp-giant isolation (dubbed underlying TCB), this thesis presents a unique on-demand I O isolation model and a trusted add-on component called wimpy kernel to instantiate this model. In our model, the wimpy kernel dynamically takes control of the devices managed by a commodity OS, connects them to the isolated wimps, and relinquishes controls to the OS when done. This model creates ample opportunities for the wimpy kernel to outsource I O subsystem functions to the untrusted OS and verify their results. The wimpy kernel further exports device drivers and I O subsystem code to wimps and mediates the operations of the exported code. These two methodologies help to significantly reduce the size and complexity of the wimpy kernel for high security assurance. Using the popular and complex USB subsystem as a case study, this thesis illustrates the dramatic reduction of the wimpy kernel; i.e., over 99 of the Linux USB code base is removed. In addition, the wimpy kernel also composes with the underlying TCB, by retaining its code size, complexity and security properties. We consider decentralized estimation of a noise-corrupted deterministic signal in a bandwidth-constrained sensor network communicating through an insecure medium. Each sensor collects a noise-corrupted version, performs a local quantization, and transmits a 1-bit message to an ally fusion center through a wireless medium where the sensor outputs are vulnerable to unauthorized observation from enemy third-party fusion centers. In this paper, we introduce an encrypted wireless sensor network (eWSN) concept where stochastic enciphers operating on binary sensor outputs are introduced to disguise the sensor outputs, creating an eWSN scheme. Noting that the plaintext (original) and ciphertext (disguised) messages are constrained to a single bit due to bandwidth constraints, we consider a binary channel-like scheme to probabilistically encipher (i.e., flip) the sensor outputs. We first consider a symmetric key encryption case where the "0" and "1" enciphering probabilities are equal. The key is represented by the bit enciphering probability. Specifically, we derive the optimal estimator of the deterministic signal approached from a maximum-likelihood perspective and the Cramer-Rao lower bound for the estimation problem utilizing the key. Furthermore, we analyze the effect of the considered cryptosystem on enemy fusion centers that are unaware of the fact that the WSN is encrypted (i.e., we derive the bias, variance, and mean square error (MSE) of the enemy fusion center). We then extend the cryptosystem to admit unequal enciphering schemes for "0" and "1", and analyze the estimation problem from both the prospectives of ally (that has access to the enciphering keys) and (third-party) enemy fusion centers. The results show that when designed properly, a significant amount of bias and MSE can be introduced to an enemy fusion center with the cost to the ally fusion center being a marginal increase [factor of (1-Omega1-Omega0 )-2, where 1-Omegaj, j=0, 1 is the "j" enciphering probability in the estimation variance (compared to the variance of a fusion center estimate operating in a vulnerable WSN). In the last years there has been an increasing interest in the security of process control and SCADA systems. Furthermore, recent computer attacks such as the Stuxnet worm, have shown there are parties with the motivation and resources to effectively attack control systems. While previous work has proposed new security mechanisms for control systems, few of them have explored new and fundamentally different research problems for securing control systems when compared to securing traditional information technology (IT) systems. In particular, the sophistication of new malware attacking control systems--malware including zero-days attacks, rootkits created for control systems, and software signed by trusted certificate authorities--has shown that it is very difficult to prevent and detect these attacks based solely on IT system information. In this paper we show how, by incorporating knowledge of the physical system under control, we are able to detect computer attacks that change the behavior of the targeted control system. By using knowledge of the physical system we are able to focus on the final objective of the attack, and not on the particular mechanisms of how vulnerabilities are exploited, and how the attack is hidden. We analyze the security and safety of our mechanisms by exploring the effects of stealthy attacks, and by ensuring that automatic attack-response mechanisms will not drive the system to an unsafe state. A secondary goal of this paper is to initiate the discussion between control and security practitioners--two areas that have had little interaction in the past. We believe that control engineers can leverage security engineering to design--based on a combination of their best practices--control algorithms that go beyond safety and fault tolerance, and include considerations to survive targeted attacks. Security-critical products rely on the secrecy and integrity of their cryptographic keys. This is challenging for low-cost resource-constrained embedded devices, with an attacker having physical access to the integrated circuit (IC). Physically, unclonable functions are an emerging technology in this market. They extract bits from unavoidable IC manufacturing variations, remarkably analogous to unique human fingerprints. However, post-processing by helper data algorithms (HDAs) is indispensable to meet the stringent key requirements: reproducibility, high-entropy, and control. The novelty of this paper is threefold. We are the first to provide an in-depth and comprehensive literature overview on HDAs. Second, our analysis does expose new threats regarding helper data leakage and manipulation. Third, we identify several hiatuses open problems in existing literature. Side-channel attacks (SCAs), such as differential power analysis or differential electromagnetic analysis, pose a serious threat to the security of embedded systems. In the literature, few articles address the problem of securing general purpose processors (GPPs) with resourceful countermeasures. However, in many low-cost applications, where security is not critical, cryptographic algorithms are typically implemented in software. Since it has been proved that GPPs are vulnerable to SCAs, it is desirable to develop efficient mechanisms to ensure a certain level of security. In this paper, we extend side-channel countermeasures to the register transfer level description. The challenge is to create a new class of processor that executes embedded software applications, which are intrinsically protected against SCAs. For that purpose, we first investigate how to integrate into the datapath two countermeasures based on masking and hiding approaches. Through an FPGA-based processor, we then evaluate the overhead and the effectiveness of the proposed solutions against time-domain first-order attacks. We finally show that a suitable combination of countermeasures significantly increases the side-channel resistance in a cost-effective way. Security in MPSoC is gaining an increasing attention since several years. Digital convergence is one of the numerous reasons explaining such a focus on embedded systems as much sensitive and secret data are now stored, manipulated and exchanged in these systems. Most solutions are currently built at the software level, we believe hardware enhancements also play a major role in system protection. One strategic point is the communication layer as all data goes through it. Monitoring and controlling communications enable to fend off attacks before system corruption. In this work, we propose an efficient solution with several hardware enhancements to secure data exchanges in a bus-based MPSoC. Our approach relies on low complexity distributed firewalls connected to all critical IPs of the system. Designers can deploy different security policies (access right, data format, authentication, confidentiality) in order to protect the system in a flexible way. To illustrate the benefit of such a solution, implementations are discussed for different MPSoCs implemented on Xilinx Virtex-6 FPGAs. Results demonstrate a reduction up to 33 in terms of latency overhead compared to existing efforts. Many semiconductor chips used in a wide range of applications require protection against physical attacks or tamper resistance. These attacks assume that a direct access to the chip is possible with either establishing electrical connections to signal wires or at least doing some measurements. The importance of protection against physical attacks is dictated by the amount of valuable and sensitive information stored on the chip. This could be secret data or company secrets and intellectual property (IP), electronic money for service access, or banking smartcards. The security in chips serves to deter prospective attackers from performing unauthorized access and benefiting from it. There are many areas that rely on tamper resistance of silicon chips. One of the first was car industry with theft protection and car alarms. Then in the early 1990s service providers such as PayTV, satellite TV, and utility companies realized that their service can be stolen if the access and payment cards are not properly protected. From the late 1990s home entertainment companies realized that their game consoles became the target of dishonest users who wanted to run illegal copies of the games. These days many device manufacturers from computer peripherals and mobile phones to printers and computers are worried about possible IP theft by third parties – either competitors or subcontractors. All the above challenges force hardware engineers to find secure solutions – either better protected off-the-shelf chips or their own custom chips. As in most cases it is impractical to block direct access to the device and its components, protection against physical attacks became the essential part of the system design. These days we have a continuous battle between the manufacturers who invent new security solutions learning their lessons from previous mistakes and the hacker community which is constantly trying to break the protection in various devices. Both sides are also constantly improving their knowledge and experience. In this endless war, the front line shifts forward and backward regularly. Deep down, the problem concerns both economics and law. On the one hand, when dishonest people try to steal property, there will be a demand to increase security. On the other, reverse engineering was always part of technological progress, helping to design compatible products and improve existing ones. The dividing line between legal (reverse engineering) and illegal (piracy) is difficult. Although mobile Java code is frequently executed on many wireless devices, the susceptibility to electromagnetic (EM) attacks is largely unknown. If analysis of EM waves emanating from the wireless device during a cryptographic computation does leak sufficient information, it may be possible for an attacker to reconstruct the secret key. Despite the complexities of a Java-based PDA device, this paper presents a new phase-based technique for aligning EM frames for subsequent time-based DEMA which extracts the secret key. Unlike previous research the new technique does not require perfect alignment of EM frames and demonstrates robustness in the presence of a complex embedded system. This research is important for future wireless embedded systems which will increasingly demand higher levels of security Since the first publication of a successful practical two-fault attack on protected CRT-RSA surprisingly little attention was given by the research community to an ensuing new challenge. The reason for it seems to be two-fold. One is that generic higher order fault attacks are very difficult to model and thus finding robust countermeasures is also difficult. Another reason may be that the published experiment was carried out on an outdated 8 bit microcontroller and thus was not perceived as a serious threat to create a sense of urgency in addressing this new menace. In this paper we describe two-fault attacks on protected CRT-RSA implementations running on an advanced 32 bit ARM Cortex M3 core. To our knowledge, this is the first practical result of two fault laser attacks on a protected cryptographic application. Considering that laser attacks are much more accurate in targeting a particular variable, the significance of our result cannot be overlooked. The prevalence of IoT devices makes them an ideal target for attackers. To reduce the risk of attacks vendors routinely deliver security updates (patches) for their devices. The delivery of security updates becomes challenging due to the issue of scalability as the number of devices may grow much quicker than vendors' distribution systems. Previous studies have suggested a permissionless and decentralized blockchain-based network in which nodes can host and deliver security updates, thus the addition of new nodes scales out the network. However, these studies do not provide an incentive for nodes to join the network, making it unlikely for nodes to freely contribute their hosting space, bandwidth, and computation resources. In this paper, we propose a novel decentralized IoT software update delivery network in which participating nodes referred to as distributors) are compensated by vendors with digital currency for delivering updates to devices. Upon the release of a new security update, a vendor will make a commitment to provide digital currency to distributors that deliver the update; the commitment will be made with the use of smart contracts, and hence will be public, binding, and irreversible. The smart contract promises compensation to any distributor that provides proof-of-distribution, which is unforgeable proof that a single update was delivered to a single device. A distributor acquires the proof-of-distribution by exchanging a security update for a device signature using the Zero-Knowledge Contingent Payment (ZKCP) trustless data exchange protocol. Eliminating the need for trust between the security update distributor and the security consumer (IoT device) by providing fair compensation, can significantly increase the number of distributors, thus facilitating rapid scale out. The vulnerabilities of face-based biometric systems to presentation attacks have been finally recognized but yet we lack generalized software-based face presentation attack detection (PAD) methods performing robustly in practical mobile authentication scenarios. This is mainly due to the fact that the existing public face PAD datasets are beginning to cover a variety of attack scenarios and acquisition conditions but their standard evaluation protocols do not encourage researchers to assess the generalization capabilities of their methods across these variations. In this present work, we introduce a new public face PAD database, OULU-NPU, aiming at evaluating the generalization of PAD methods in more realistic mobile authentication scenarios across three covariates: unknown environmental conditions (namely illumination and background scene), acquisition devices and presentation attack instruments (PAI). This publicly available database consists of 5940 videos corresponding to 55 subjects recorded in three different environments using high-resolution frontal cameras of six different smartphones. The high-quality print and videoreplay attacks were created using two different printers and two different display devices. Each of the four unambiguously defined evaluation protocols introduces at least one previously unseen condition to the test set, which enables a fair comparison on the generalization capabilities between new and existing approaches. The baseline results using color texture analysis based face PAD method demonstrate the challenging nature of the database. Forming secure associations between wireless devices that do not share a prior trust relationship is an important problem. This paper presents ProxiMate, a system that allows wireless devices in proximity to securely pair with one another autonomously by generating a common cryptographic key directly from their shared time-varying wireless environment. The shared key synthesized by ProxiMate can be used by the devices to authenticate each others' physical proximity and then to communicate confidentially. Unlike traditional pairing approaches such as Diffie-Hellman, ProxiMate is secure against a computationally unbounded adversary and its computational complexity is linear in the size of the key. We evaluate ProxiMate using an experimental prototype built using an open-source software-defined platform and demonstrate its effectiveness in generating common secret bits. We further show that it is possible to speed up secret key synthesis by monitoring multiple RF sources simultaneously or by shaking together the devices that need to be paired. Finally, we show that ProxiMate is resistant to even the most powerful attacker who controls the public RF source used by the legitimate devices for pairing. Secure and fast distribution of software updates and patches is essential for improving functionality and security of computer systems. Today, each device downloads updates individually from a software provider distribution server. Unfortunately, this approach does not scale to large systems with billions of devices where the network bandwidth of the server and the local Internet gateway become bottlenecks. Cache-enabled Network (CN) services (either proprietary, as Akamai, or open Content-Distribution Networks) can reduce these bottlenecks. However, they do not offer security guarantees against potentially untrusted CN providers that try to threaten the confidentiality of the updates or the privacy of the users. In this paper, we propose Updaticator, the first protocol for software updates over Cache-enabled Networks that is scalable to billions of concurrent device updates while being secure against malicious networks. We evaluate our proposal considering Named-Data Networking, a novel instance of Cache-enabled overlay Networks. Our analysis and experimental evaluation show that Updaticator removes the bottlenecks of individual device-update distribution, by reducing the network load at the distribution server: from linear in the number of devices to a constant, even if billions of devices are requesting updates. Furthermore, when compared to the state-of-the-art individual device-update mechanisms, the download time with Updaticator is negligible, due to local caching. Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9 accuracy, our method achieves 55.7 ; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6 accuracy. Our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 --- it achieved 50.6 classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by 10 . Code is available at this https URL. A critical aspect of malware forensics is authorship analysis. The successful outcome of such analysis is usually determined by the reverse engineer’s skills and by the volume and complexity of the code under analysis. To assist reverse engineers in such a tedious and error-prone task, it is desirable to develop reliable and automated tools for supporting the practice of malware authorship attribution. In a recent work, machine learning was used to rank and select syntax-based features such as n-grams and flow graphs. The experimental results showed that the top ranked features were unique for each author, which was regarded as an evidence that those features capture the author’s programming styles. In this paper, however, we show that the uniqueness of features does not necessarily correspond to authorship. Specifically, our analysis demonstrates that many “unique” features selected using this method are clearly unrelated to the authors’ programming styles, for example, unique IDs or random but unique function names generated by the compiler; furthermore, the overall accuracy is generally unsatisfactory. Motivated by this discovery, we propose a layered Onion Approach for Binary Authorship Attribution called OBA2. The novelty of our approach lies in the three complementary layers: preprocessing, syntax-based attribution, and semantic-based attribution. Experiments show that our method produces results that not only are more accurate but have a meaningful connection to the authors’ styles. a 2014 The Author. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http: creativecommons.org licenses by-nc-nd 3.0 ).