The number of cyber-attacks and data breaches has immensely increased across different enterprises, companies, and industries as a result of the exploitation of the weaknesses in securing Internet of Things (IoT) devices. The increasing number of various devices connected to IoT and their different protocols has led to growing volume of zero-day attacks. Deep learning (DL) has demonstrated its superiority in big data fields and cyber-security. Recently, DL has been used in cyber-attacks detection because of its capability of extracting and learning deep features of known attacks and detecting unknown attacks without the need for manual feature engineering. However, DL cannot be implemented on IoT devices with limited resources because it requires extensive computation, strong power and storage capabilities. This paper presents a comprehensive attack detection framework of a distributed, robust, and high detection rate to detect several IoT cyber-attacks using DL. The proposed framework implements an attack detector on fog nodes because of its distributed nature, high computational capacity and proximity to edge devices. Six DL models are compared to identify the DL model with the best performance. All DL models are evaluated using five different datasets, each of which involves various attacks. Experiments show that the long shortterm memory model outperforms the five other DL models. The proposed framework is effective in terms of response time and detection accuracy and can detect several types of cyber-attacks with 99.97% detection rate and 99.96% detection accuracy in binary classification and 99.65% detection accuracy in multi-class classification.

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

An Online Ride Hailing (ORH) system enables a rider to request a taxi through a smartphone app on a short notice. To enjoy ORH services, users have to submit their location information to ORH servers. There are serious privacy concerns for users to reveal location information to ORH servers. In this paper, we propose a lightweight and privacy-preserving ride matching scheme for ORH systems, named lpRide, which can find the taxi with the minimum road distance to serve an incoming rider, while protecting the location privacy of both riders and taxis against the ORH server. In lpRide, we propose an efficient shortest road distance computation approach over encrypted data, which computes approximate road distance by using road network embedding and the modified Paillier cryptosystem. Moreover, we design a secure comparison method to compare two distances over the corresponding blinded ciphertexts, without revealing the actual distances. Theoretical analysis and experimental evaluations show that lpRide is private, accurate and efficient.

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine learning that effectively deals with the privacy leakage problem by utilizing cryptographic primitives. However, how to prevent poisoning attacks in distributed situations has recently become a major FL concern. Indeed, an adversary can manipulate multiple edge nodes and submit malicious gradients to disturb the global model’s availability. Currently, most existing works rely on an Independently Identical Distribution (IID) situation and identify malicious gradients using plaintext. However, we demonstrates that current works cannot handle the data heterogeneity scenario challenges and that publishing unencrypted gradients imposes significant privacy leakage problems. Therefore, we develop APFed, a layered privacy-preserving defense mechanism that significantly mitigates the effects of poisoning attacks in data heterogeneity scenarios. Specifically, we exploit HE as the underlying technique and employ the median coordinate as the benchmark. Subsequently, we propose a secure cosine similarity scheme to identify poisonous gradients, and we innovatively use clustering as part of the defense mechanism and develop a hierarchical aggregation that enhances our scheme’s robustness in IID and non-IID scenarios. Extensive evaluations on two benchmark datasets demonstrate that APFed outperforms existing defense strategies while reducing the communication overhead by replacing the expensive remote communication method with inexpensive intra-cluster communication.

Online Ride Hailing (ORH) has extensively made our trip more convenient. With mobile devices, riders can request taxis through ORH systems in a short time. However, to enjoy ORH services, users need to submit their location information to ORH systems, which raises serious privacy concerns. In this paper, we study the privacy leakage of online ridesharing matching, a more complex and economy ORH service that allows riders to share rides with others, and propose a privacy-preserving shared ride matching scheme, called PSRide. PSRide can find the taxi with the minimum additional travel time to serve a new rider based on its existing schedule, while protecting the location privacy of both riders and taxis. In PSRide, we propose a zone-based minimum road travel time estimation approach and a secure comparison protocol to efficiently optimize the schedules of taxis for a new rider over encrypted data. We implement PSRide and analyze it thoroughly. Theoretical analysis and experimental evaluations show that PSRide is secure and efficient for ORH systems.

An ORH service creates a typical supply-and-demand two-sided market, which enables riders and drivers to establish optimized rides conveniently via mobile applications. Group ridesharing is a novel form of ridesharing, which allows a group of riders to share a vehicle that holds the minimum aggregate distance to the whole group. Accompanied by the advantage of ORH services, there comes some vital privacy concerns. In this paper, we propose a privacy-preserving online group ridesharing matching scheme for ORH services, called PGRide. PGRide can select the nearest driver to serve a group of riders, without leaking the location privacy of both riders and drivers. In PGRide, we propose an encrypted aggregate distance computation approach by using somewhat homomorphic encryption with ciphertexts packing, which efficiently computes the aggregate distances from a group of riders to large-scale dynamic drivers in encrypted form. Meanwhile, we design a secure minimum selection protocol by using ciphertexts packing and blinding, which efficiently finds the minimum element from a set of encrypted integers without leaking any actual element value. Theoretical analysis and performance evaluations prove that PGRide is secure, accurate and efficient.