The Internet of Things (IoT) has become ubiquitous and greatly affected peoples‘ daily lives. With the increasing development of IoT devices, the corresponding security issues are becoming more and more challenging. Such a severe security situation raises the following questions that need urgent attention: What are the primary security threats that IoT devices face currently‘ How do vendors and users deal with these threats‘ In this paper, we aim to answer these critical questions through a large-scale systematic study. Specifically, we perform a ten-month-long empirical study on the vulnerability of 1,362,906 IoT devices varying from six types. The results show sufficient evidence that N-days vulnerability is seriously endangering the IoT devices: 385,060 (28.25%) devices suffer from at least one N-days vulnerability. Moreover, 2,669 of these vulnerable devices may have been compromised by botnets. We further reveal the massive differences among five popular IoT search engines: Shodan, Censys, Zoomeye, Fofa and NTI. Besides, we measure the security of MQTT servers and identify that 12,740 (88%) MQTT servers have no password protection. Our analysis can serve as an important guideline for investigating the security of IoT devices, as well as advancing the development of a more secure environment for IoT systems.

Recent advances in composing Cloud applications have been driven by deployments of inter-networking heterogeneous microservices across multiple Cloud datacenters. System dependability has been of the upmost importance and criticality to both service vendors and customers. Security, a measurable attribute, is increasingly regarded as the representative example of dependability. With the increment of microservice types and dynamicity, applications are exposed to aggravated internal security threats and externally environmental uncertainties. Still, orchestrating a set of microservices across datacenters under those constraints remains computationally intractable. This paper describes a new dependable microservice orchestration framework GA-Par to effectively select and deploy microservices. We adopt a hybrid (both whitebox and blackbox based) approach to measure the satisfaction of security requirement and the environmental impact of network QoS on system dependability. Due to the exponential grow of solution space, we develop a parallel Genetic Algorithm framework based on Spark to accelerate operations for calculating the optimal or near-optimal solution. Real world datasets are utilized to validate models and orchestration approach. Experiments show our solution outperforms the greedy-based security aware method with 42.34% improvement. GA-Par is roughly 4x faster than a Hadoop-based genetic algorithm solver and the effectiveness can be constantly guaranteed under different application scales.

Currently, the development of IoT firmware heavily depends on third-party components (TPCs) to improve development efficiency. Nevertheless, TPCs are not secure, and the vulnerabilities in TPCs will influence the security of IoT firmware. Existing works pay less attention to the vulnerabilities caused by TPCs, and we still lack a comprehensive understanding of the security impact of TPC vulnerability against firmware. To fill in the knowledge gap, we design and implement FirmSec , which leverages syntactical features and control-flow graph features to detect the TPCs in firmware, and then recognizes the corresponding vulnerabilities. Based on FirmSec , we present the first large-scale analysis of the security risks raised by TPCs on 34,136 firmware images. We successfully detect 584 TPCs and identify 128,757 vulnerabilities caused by 429 CVEs. Our in-depth analysis reveals the diversity of security risks in firmware and discovers some well-known vulnerabilities are still rooted in firmware. Besides, we explore the geographical distribution of vulnerable devices and confirm that the security situation of devices in different regions varies. Our analysis also indicates that vulnerabilities caused by TPCs in firmware keep growing with the boom of the IoT ecosystem. Further analysis shows 2,478 commercial firmware images have potentially violated GPL/AGPL licensing terms.

A Cyber-Physical System (CPS) is a networked system with communication protocols and technologies that are susceptible to potential vulnerabilities and security threats. For example, an adversary may try to uncover the Source Location Privacy (SLP) of a target to perform an attack by analyzing network traffic in a random manner. However, this approach is usually inefficient. Considering the inefficiency of traditional random walk methods, we incorporate an active learning mechanism to equip with the backtracking framework to effectively distinguish between the fake and real network traffic with a limited number of learning samples. In this paper, we present an Active Learning-based Backtracking Attack (ALBA) which leverages muti-layer sampling technique to discover SLP. ALBA offers a more intelligent and faster approach in discovering the SLP. A new muti-layer sampling strategy is also proposed to enhance the performance of the backtracking attack. The effectiveness of our proposed ALBA is evaluated using the NSL-KDD dataset, which simulates the communication environment of CPS. The experimental results show that ALBA outperforms existing state-of-the-art schemes.