We present the first side-channel attack on full-fledged smartphones that recovers the elliptic curve secret scalar from the electromagnetic signal that corresponds to a single scalar-by-point multiplication in current versions of Libgcrypt, OpenSSL, HACL* and curve25519-donna. To avoid leaking information via side channels, these implementations follow the recommendations of RFC 7748 and use a constant-time conditional swap operation. Our attack targets signal differences created by systematic changes in operand values during this conditional swap operation. We deploy the attack, using low-cost equipment (<$800), against two Android-based mobile phones and against a Linux-based IoT development board. We repeat the attack 100 times, each time with a different scalar, on each device. In all of the implementations considered in this work, our attack successfully recovers the full secret key within seconds. To mitigate the attack we suggest randomizing the exclusive-or mask in the conditional swap operation. We show that this countermeasure is effective in preventing this and similar attacks.

Cyber-physical systems (CPS) are controlling many critical and sensitive aspects of our physical world while being continuously exposed to potential cyber-attacks. These systems typically have limited performance, memory, and energy reserves, which limits their ability to run existing advanced malware protection, and that, in turn, makes securing them very challenging. To tackle these problems, this paper proposes, REMOTE, a new robust framework to detect malware by externally observing Electromagnetic (EM) signals emitted by an electronic computing device (e.g., a microprocessor) while running a known application, in real-time and with a low detection latency, and without any a priori knowledge of the malware. REMOTE does not require any resources or infrastructure on, or any modifications to, the monitored system itself, which makes REMOTE especially suitable for malware detection on resource-constrained devices such as embedded devices, CPSs, and Internet of Things (IoT) devices where hardware and energy resources may be limited. To demonstrate the usability of REMOTE in real-world scenarios, we port two real-world programs (an embedded medical device and an industrial PID controller), each with a meaningful attack (a code-reuse and a code-injection attack), to four different hardware platforms. We also port shellcode-based DDoS and Ransomware attacks to five different standard applications on an embedded system. To further demonstrate the applicability of REMOTE to commercial CPS, we use REMOTE to monitor a Robotic Arm. Our results on all these different hardware platforms show that, for all attacks on each of the platforms, REMOTE successfully detects each instance of an attack and has99.9 percent true positive rates) under all these conditions. We also compare REMOTE to prior work EDDIE [1] and SYNDROME [2], and demonstrate that these prior work are unable to achieve high accuracy under these variations.

Technical Support Scams (TSS), which combine online abuse with social engineering over the phone channel, have persisted despite several law enforcement actions. Although recent research has provided important insights into TSS, these scams have now evolved to exploit ubiquitously used online services such as search and sponsored advertisements served in response to search queries. We use a data-driven approach to understand search-and-ad abuse by TSS to gain visibility into the online infrastructure that facilitates it. By carefully formulating tech support queries with multiple search engines, we collect data about both the support infrastructure and the websites to which TSS victims are directed when they search online for tech support resources. We augment this with a DNS-based amplification technique to further enhance visibility into this abuse infrastructure. By analyzing the collected data, we provide new insights into search-and-ad abuse by TSS and reinforce some of the findings of earlier research. Further, we demonstrate that tech support scammers are (1) successful in getting major as well as custom search engines to return links to websites controlled by them, and (2) they are able to get ad networks to serve malicious advertisements that lead to scam pages. Our study period of approximately eight months uncovered over 9,000 TSS domains, of both passive and aggressive types, with minimal overlap between sets that are reached via organic search results and sponsored ads. Also, we found over 2,400 support domains which aid the TSS domains in manipulating organic search results. Moreover, to our surprise, we found very little overlap with domains that are reached via abuse of domain parking and URL-shortening services which was investigated previously. Thus, investigation of search-and-ad abuse provides new insights into TSS tactics and helps detect previously unknown abuse infrastructure that facilitates these scams.

This paper describes EM-Based Detection of Deviations in Program Execution (EDDIE), a new method for detecting anomalies in program execution, such as malware and other code injections, without introducing any overheads, adding any hardware support, changing any software, or using any resources on the monitored system itself. Monitoring with EDDIE involves receiving electromagnetic (EM) emanations that are emitted as a side effect of execution on the monitored system, and it relies on spikes in the EM spectrum that are produced as a result of periodic (e.g. loop) activity in the monitored execution. During training, EDDIE characterizes normal execution behavior in terms of peaks in the EM spectrum that are observed at various points in the program execution, but it does not need any characterization of the malware or other code that might later be injected. During monitoring, EDDIE identifies peaks in the observed EM spectrum, and compares these peaks to those learned during training. Since EDDIE requires no resources on the monitored machine and no changes to the monitored software, it is especially well suited for security monitoring of embedded and IoT devices. We evaluate EDDIE on a real IoT system and in a cycle-accurate simulator, and find that even relatively brief injected bursts of activity (a few milliseconds) are detected by EDDIE with high accuracy, and that it also accurately detects when even a few instructions are injected into an existing loop within the application.

Memory corruption vulnerabilities are the root cause of many modern attacks. Existing defense mechanisms are inadequate; in general, the software-based approaches are not efficient and the hardware-based approaches are not flexible. In this paper, we present hardware-assisted data-flow isolation, or, HDFI, a new fine-grained data isolation mechanism that is broadly applicable and very efficient. HDFI enforces isolation at the machine word granularity by virtually extending each memory unit with an additional tag that is defined by dataflow. This capability allows HDFI to enforce a variety of security models such as the Biba Integrity Model and the Bell -- LaPadula Model. We implemented HDFI by extending the RISC-V instruction set architecture (ISA) and instantiating it on the Xilinx Zynq ZC706 evaluation board. We ran several benchmarks including the SPEC CINT 2000 benchmark suite. Evaluation results show that the performance overhead caused by our modification to the hardware is low (<; 2%). We also developed or ported several security mechanisms to leverage HDFI, including stack protection, standard library enhancement, virtual function table protection, code pointer protection, kernel data protection, and information leak prevention. Our results show that HDFI is easy to use, imposes low performance overhead, and allows us to create more elegant and more secure solutions.

It is a great challenge in human health clinic to find parsimonious set of genes responsible for depression disease. Many prioritization approaches have been developed for depression candidate genes. However, most of the methods primarily rank depression candidate genes based on the similarities with known depression genes. Those approaches do not effectively consider relativeness of depression candidate genes with non-disease genes for precise ranking. In this paper, we propose a Biomarker Ensemble Ranking Framework (BERF) for depression candidate gene prioritization, which applies 2-ranking models scheme by considering both candidate genes and non-disease genes. We first clusterize training genes which consists of both known depression and non-disease genes. Then, we introduce a global loss function in the 2-ranking learning model. Finally, we propose a modified SVM-based learning strategy with minimizing the global loss. An ensemble technique is applied to generate the ranking results for depression candidate genes. The experimental results show that our BERF outperforms existing approach in terms of ROC and AUC.

Masking of gates is one of the most popular techniques to prevent differential power analysis (DPA) of AES algorithm. It has been shown that the logic circuits used in the implementation of cryptographic algorithms leak side-channel information inspite of masking, which can be exploited, in differential power attacks. The phenomenon in CMOS circuits responsible for the leakage of masked circuits is known as glitching. Motivated by this fact, the authors analyse the effect of glitches in CMOS circuits against masked implementation of the AES S-box. The authors explicitly demonstrate that glitches do not affect always. There exists a relation between combinational path delay of the circuit and timing difference of input vectors to the circuit, which has a bearance on the amount of information leaked by the masked gates. A balanced masked S-box circuit is proposed where the inputs are synchronised by sequential components. Detailed SPICE results are shown to support the claim that the modifications indeed reduce the vulnerability of the masked AES S-box against DPA attacks.

This paper deals with FPGA and ASIC implementations of side-channel attack resistant elliptic curve cryptosystems defined over GF(p). The elegance of the design lies in the fact that all operations are performed in binary number system, thus reducing conversion overheads of existing architectures. In our implementation, point addition and point doubling operations are performed in affine coordinates. They are performed using same amount of computation, which provides a secure design against timing and power analysis attacks. Implementation and side-channel analysis results are compared with related existing designs.

This paper presents the architecture and FPGA implementation of a robust GF(p) parallel arithmetic unit. The most efficient modular multiplication, inversion and division units greatly reduce the clock cycles requirement for point operations applicable to elliptic curve cryptography. The parallel arithmetic unit helps to achieve a high speed up in cryptographic applications. The architecture can resist the cryptographic timing attack. Integrated input and output interface units provide lower bandwidth requirement to plug in the architecture with automated cryptographic systems. The design exhibits its elegance among competitive architecture with respect to throughput and robustness.