With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IOT SENTINEL, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IOT SENTINEL is effective in identifying device types and has minimal performance overhead.

Today, embedded, mobile, and cyberphysical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as Industrie 4.0 and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. These systems generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. Cyberattacks on IoT systems are very critical since they may cause physical damage and even threaten human lives. The complexity of these systems and the potential impact of cyberattacks bring upon new threats. This paper gives an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems

Fine-grained address space layout randomization (ASLR) has recently been proposed as a method of efficiently mitigating runtime attacks. In this paper, we introduce the design and implementation of a framework based on a novel attack strategy, dubbed just-in-time code reuse, that undermines the benefits of fine-grained ASLR. Specifically, we derail the assumptions embodied in fine-grained ASLR by exploiting the ability to repeatedly abuse a memory disclosure to map an application's memory layout on-the-fly, dynamically discover API functions and gadgets, and JIT-compile a target program using those gadgets-all within a script environment at the time an exploit is launched. We demonstrate the power of our framework by using it in conjunction with a real-world exploit against Internet Explorer, and also provide extensive evaluations that demonstrate the practicality of just-in-time code reuse attacks. Our findings suggest that fine-grained ASLR may not be as promising as first thought.

IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved.In this paper, we present DÏOT, an autonomous self-learning distributed system for detecting compromised IoT devices. DÏOT builds effectively on device-type-specific communication profiles without human intervention nor labeled data that are subsequently used to detect anomalous deviations in devices' communication behavior, potentially caused by malicious adversaries. DÏOT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, DÏOT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-theshelf IoT devices over a long term and show that DÏOT is highly effective (95.6 % detection rate) and fast (≈ 257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. DÏOT reported no false alarms when evaluated in a real-world smart home deployment setting.

In recent years, there has been explosive growth in smartphone sales, which is accompanied with the availability of a huge number of smartphone applications (or simply apps). End users or consumers are attracted by the many interesting features offered by these devices and the associated apps. The developers of these apps benefit financially, either by selling their apps directly or by embedding one of the many ad libraries available on smartphone platforms. In this paper, we focus on potential privacy and security risks posed by these embedded or in-app advertisement libraries (henceforth "ad libraries," for brevity). To this end, we study the popular Android platform and collect 100,000 apps from the official Android Market in March-May, 2011. Among these apps, we identify 100 representative in-app ad libraries (embedded in 52.1% of the apps) and further develop a system called AdRisk to systematically identify potential risks. In particular, we first decouple the embedded ad libraries from their host apps and then apply our system to statically examine the ad libraries for risks, ranging from uploading sensitive information to remote (ad) servers to executing untrusted code from Internet sources. Our results show that most existing ad libraries collect private information: some of this data may be used for legitimate targeting purposes (i.e., the user's location) while other data is harder to justify, such as the user's call logs, phone number, browser bookmarks, or even the list of apps installed on the phone. Moreover, some libraries make use of an unsafe mechanism to directly fetch and run code from the Internet, which immediately leads to serious security risks. Our investigation indicates the symbiotic relationship between embedded ad libraries and host apps is one main reason behind these exposed risks. These results clearly show the need for better regulating the way ad libraries are integrated in Android apps.