Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round. This paper proposes DROVE-a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the state-of-The-Art solutions in terms of energy consumption, clustering overhead, throughput, end-To-end delay, flow success rate and packet drop rate.

Current trends forecast that Over-the-Air (OTA) software updates will be highly significant for future connected vehicles. The OTA software updates will enable upgrading vehicle functionalities or bug fixations in embedded software installed on electronic control units remotely. However, in terms of security, OTA updates are highly critical as they need complete access to the in-vehicle communication network. Furthermore, scheduling OTA software updates at a massive scale over a cellular network is highly challenging. This paper proposes STRIDE, a novel technique for secure and scalable software updates using cloud through cellular network. STRIDE ensures end-to-end security using ciphertext-policy attribute-based encryption. To enable fast and reliable distribution of update package, we then propose a software update scheduling algorithm to serve dynamic traffic flow. Particularly, we integrate dynamic traffic flow with the Lyapunov-drift analysis framework, and establish throughput optimality of our proposed scheduling algorithm. We evaluate the performance of STRIDE through extensive experiments. Experimental results show that STRIDE reduces more than 52% computation and storage overheads, 60% propagation delay and increases throughput by 35% than the state-of-the-art solutions, in addition to enjoying the stronger security properties.

The Internet of Things (IoT) is an emerging paradigm and has penetrated deeply into our daily life. Due to the seamless connections of the IoT devices with the physical world through the Internet, the IoT applications use the cloud to store and provide ubiquitous access to collected data. Sharing of data with third party services and other users incurs potential risks and leads to unique security and privacy concerns, e.g., data breaches. Existing cryptographic solutions are inapt for resource-constrained IoT devices, because of their significant computational overhead. To address these concerns, we propose a data protection scheme to store the encrypted IoT data in a cloud, while still allowing query processing over the encrypted data. Our proposed scheme features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities and in-situ key updates. We perform exhaustive experiments on real datasets, to assess the feasibility of the proposed scheme on the resource constrained IoT devices. The results show the feasibility of our scheme, together with the ability to provide a high level of security. The results also show that our scheme significantly reduces the computation, storage and energy overheads than the best performed scheme in the state-of-the-art.

Cloud computing model provides on demand, elastic and fully managed computer system resources and services to organizations. However, attacks on cloud components can cause inestimable losses to cloud service providers and cloud users. One such category of attacks is the Distributed Denial of Service (DDoS), which can have serious consequences including impaired customer experience, service outage and in severe cases, complete shutdown and total economic unsustainability. Advances in Internet of Things (IoT) and network connectivity have inadvertently facilitated launch of DDoS attacks which have increased in volume, frequency and intensity. Recent DDoS attacks involving new attack vectors and strategies, have precipitated the need for this survey. In this survey, we mainly focus on finding the gaps, as well as bridging those gaps between the future potential DDoS attacks and state-of-the-art scientific and commercial DDoS attack defending solutions. It seeks to highlight the need for a comprehensive detection approach by presenting the recent threat landscape and major cloud attack incidents, estimates of future DDoS, illustrative use cases, commercial DDoS solutions, and the laws governing DDoS attacks in different nations. An up-to-date survey of DDoS detection methods, particularly anomaly based detection, available research tools, platforms and datasets, has been given. This paper further explores the use of machine learning methods for detection of DDoS attacks and investigates features, strengths, weaknesses, tools, datasets, and evaluates results of the methods in the context of the cloud. A summary comparison of statistical, machine learning and hybrid methods has been brought forth based on detailed analysis. This paper is intended to serve as a ready reference for the research community to develop effective and innovative detection mechanisms for forthcoming DDoS attacks in the cloud environment. It will also sensitize cloud users and providers to the urgent need to invest in deployment of DDoS detection mechanisms to secure their assets.

Controller Area Network (CAN) is an in-vehicle communication protocol, which provides an efficient and reliable communication link between Electronic Control Units (ECUs) in real time. Recent studies have shown that attackers can take remote control of the targeted vehicle by exploiting the vulnerabilities of the CAN protocol. Motivated by this fact, we propose an Intrusion Detection System (IDS), called Clock Offset-based IDS (COIDS), to monitor in-vehicle network activities to detect any intrusion. Precisely, COIDS measures and then exploits the clock offset of transmitter ECU's clock for fingerprinting ECU. COIDS next leverages the derived fingerprints to construct a baseline of ECU's normal clock behavior using an active learning technique. Based on the baseline of normal behavior, COIDS uses the Cumulative Sum method to detect any abnormal deviation in clock offset. Further, COIDS uses a sequential change-point detection technique to determine the exact time of intrusion. Generally, COIDS has to run on every ECU to monitor the network behavior. This can turn out to be a significant power overhead for a hardware-constrained ECU. Thus, we next develop a cooperative game model to optimize the active time duration of COIDS in an ECU. We performed exhaustive experiments on real world publicly available datasets primarily to assess the effectiveness of COIDS against a wide range of in-vehicle network attacks. Our results show that COIDS detects intrusions faster than the best performed IDS in the state-of-the-art. Further, the results show that our designed cooperative game model significantly reduces the power overhead of the ECU without compromising the performance.

Trucks form a platoon, where they align in a lane on freeways, in order to save fuel. The trucks positioned behind the first truck in the platoon can save fuel because their air drag reduces. In a truck platoon, various technologies such as Adaptive Cruise Control (ACC) system and Vehicle-to-Vehicle (V2V) communication protocol are exploited to control the trucks in the platoon effectively. The camera sensors measure distance between contiguous trucks in the platoon and these sensor measurements are provided to the ACC system. By using these sensor measurement, the ACC system controls the truck speed via in-vehicle network protocols such as the controller area network, which does not encrypt data or authenticate messages. Also, the V2V communication protocol is vulnerable to cyber attacks that disable the wireless channels among the trucks in the platoon. As a result, the ACC system and V2V communication protocol may introduce attack surfaces in a truck platoon. In this survey, we analyze the attack surfaces of a truck platoon under different perspectives. Then, we summarize the defense systems corresponding to the discussed attacks in terms of detection mechanisms, response countermeasures, and proactive defenses. Finally, we provide the research issues that need to be addressed in future.

Current trends forecast that Over-the-Air (OTA) software updates will be highly significant for future connected vehicles. The OTA update will enable upgrading the vehicle functionalities or bug fixations in the embedded software installed on its Electronic Control Units (ECUs) remotely. The introduction of OTA updates in the automotive industry has brought many advantages for both the Original Equipment Manufacturer (OEM) and the driver/owner. However, in terms of security, OTA updates are highly critical as they need complete access to the in-vehicle communication network. This survey highlights and discusses OTA software updates in the automotive sector, mainly from the security perspective. The major objective of this survey is to deliver a comprehensive outline of various research directions and approaches in OTA update technologies in vehicles. At first, we discuss the connected vehicle technology and then integrate the relationship of OTA update features with the connected vehicle. We further discuss both promising and secure OTA update approaches, that have gained a lot of attention recently. Furthermore, we present a comprehensive comparative study of the existing OTA update approaches on the basis of strengths, weaknesses and evaluation setup. The survey also focuses on the existing vehicle features that support OTA updates, and customer satisfaction and usability. Finally, we identify possible future research directions of OTA updates for automobiles, particularly in the area of security.

Current trends forecast that Over-The-Air (OTA) software updates will be highly significant for future autonomous vehicles. The OTA update will enable fast distribution of software updates and fixing bugs in the embedded software installed on its electronic control units. However, in terms of security, OTA updates are highly critical due to complexity, numerous attack surfaces, and unsafe and old techniques. In addition, OTA updates must be scalable to large number of vehicles without incurring additional network bandwidth. In this work, we propose STRIDE, a scheme for OTA software updates over cloud that is secure against malicious attacks while being scalable to concurrent software updates in a large number of vehicles. STRIDE enables fast and secure distribution of update packages from software providers to vehicles. STRIDE ensures end-to-end security using ciphertext-policy attribute-based encryption. We evaluate the performance of our proposal through extensive experiments. Experimental results show that STRIDE significantly reduces the overheads, without compromising the network load and software update retrieval time than the best performed scheme in the state-of-the-art.

Controller Area Network (CAN) is an in-vehicle communication protocol which provides an efficient and reliable communication link between Electronic Control Units (ECUs) in real-time. Recent studies have shown that attackers can take remote control of the targeted car by exploiting the vulnerabilities of the CAN protocol. Motivated by this fact, we propose Clock Offset-based Intrusion Detection System (COIDS) to monitor in-vehicle network and detect any intrusion. Precisely, we first measure and then exploit the clock offset of transmitter ECU's clock for fingerprinting ECU. We next leverage the derived fingerprints to construct a baseline of ECU's normal clock behaviour using an active learning technique. Based on the baseline of normal behaviour, we use Cumulative Sum method to detect any abnormal deviation in clock offset. Particularly, if the deviation in clock offset exceeds an unexpected positive or negative value, COIDS declares this change as an intrusion. Further, we use sequential change-point detection technique to determine the exact time of intrusion. We perform exhaustive experiments on real-world publicly available datasets primarily to assess the effectiveness of COIDS against three most potential attacks on CAN, i.e., DoS, impersonation and fuzzy attacks. The results show that COIDS is highly effective in defending all these three attacks. Further, the results show that COIDS considerably faster in detecting intrusion compared to a state-of-the-art solution.