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With the rapid technological advancement in the Internet of Things (IoT) and Internet of Vehicles (IoV), we witness exponential growth of Connected and Autonomous Vehicles (CAVs). However, these integrations of IoV with other technologies make the IoV network and its interaction between different network components highly complex. Therefore, ensuring the correct functioning of the firmware and software running on these next-generation vehicles becomes an essential requirement. A feasible method to address the aforementioned security issues is Remote Attestation (RA). However, the advancement in the attackers’ approaches and the increased complexity, large network size, and vehicle mobility allow the attacks to bypass these security solutions, making RA less effective. In this paper, we propose LHASIoV, an attestation and healing protocol for IoV. LHASIoV has many features such as competent-wise (treats different entities of the system differently), geographical location-aware (traces forensics of security breaches and eases healing compromised vehicles), gradual healing (via slicing the healing software) of compromised vehicles, and resistance to single-point-of-failure. We provide proof-of-concept implementation and formal operational and security analysis for LHASIoV. To show its practical feasibility and effectiveness, we provide performance analysis by implementing it on the Omnetpp simulator. The simulation results show that for an IoV system that has 100 vehicles moving with a speed range of 15–25 mph, LHASIoV needed only 5.27 seconds to complete the vehicle’s attestation. For this number of vehicles and compared to the existing protocols, LHASIoV reduced the communication and storage costs on average by 54.46% and 43.92%, respectively.

Digital forensics is crucial to fight crimes around the world. Decentralized Digital Forensics (DDF) promotes it to another level by channeling the power of blockchain into digital investigations. In this work, we focus on the privacy and security of DDF. Our motivations arise from (1) how to track an anonymous-and-malicious data user who leaks only a part of the previously requested data, (2) how to achieve access control while protecting data from untrusted data centers, and (3) how to enable efficient and secure search on the blockchain. To address these issues, we propose Themis: an anonymous and secure DDF scheme with traceable anonymity, private access control, and efficient search. Our framework is boosted by establishing a Trusted Execution Environment in each authority (blockchain node) for securing the uploading, requesting, and searching. To instantiate the framework, we design a secure and robust watermarking scheme in conjunction with decentralized anonymous authentication, a private and fine-grained access control scheme, and an efficient and secure search scheme based on a dynamically updated data structure. We formally define and prove the privacy and security of Themis. We build a prototype with Ethereum and Intel SGX2 to evaluate its performance, which supports processing data from a considerable number of data providers and investigators.

The secure transmission of instructions among vehicles in a platoon is one of the most essential needs for a vehicle platoon. Despite the existence of cryptographic methods to securely share instructions, instruction sharing is still subject to forgery, tampering, and denial-of-service attacks. Therefore, it is urgent to find a solution to perform data source identification to filter out irrelevant information (not instructions) while ensuring the authenticity of encrypted instructions is urgent to address. In addition, immediate revocation of credentials is also a crucial requirement for a vehicle platoon when an authorized vehicle member misbehaves. In this paper, we propose the first Secure Source Identification Scheme for Revocable Instruction Sharing (SI-RIS) to securely simultaneously achieve bilateral fine-grained access control, data source identification, immediate vehicle user revocation, and efficient encryption in vehicle platoons. Specifically, our SI-RIS solution supports fine-grained access control for both the sender and receiver over the encrypted instructions. As a result, only authorized correspondents are able to access the commands. Furthermore, upon identification of malicious members in the platoon, our SI-RIS provides an efficient direct vehicle user revocation mechanism capable of immediate revocation credentials without affecting other vehicles. We prove the security of our SI-RIS via rigorous mathematical security proof. Moreover, performance evaluation and comparisons illustrate the feasibility and practicability of SI-RIS for vehicle platoon.

As an emerging field of communication, Vehicular Social Networks (VSNs) can reduce traffic congestion while enhancing road safety by sharing data among groups of commuters. In VSNs, Vehicular Cloud Server (VCS) based data sharing technology with encrypted primitives allows local users to outsource encrypted data for reducing the storage burden on the user side and sharing data without location restrictions. However, existing data encryption solutions that have been applied in VSNs environments still encounter weaknesses in efficiency, security, or privacy due to the following problems: (1) lack of effective access policies for flexible authorizing ciphertext to multiple data users; (2) data breaches caused by malicious data publishers; (3) necessity in hiding the private information of receivers. To date, no such solution has been available that securely enables one-to-many user authorization with privacy protection, while greatly resisting malicious data publishers. We propose a Sanitizable Access Control System with Policy-protection (SASP) for VSNs in this paper. Our SASP enables a sanitizer to test and sanitize encrypted data to defend against malicious data publishers, ensuring that the plaintext can only be recovered if an authorized user has a valid key. Furthermore, in our SASP system, the access policy is separated into attribute names and attribute values. Wherein, the attribute values contain a lot of private information, which is hidden in the ciphertext to guarantee data users’ privacy. Rigorous security analysis and performance evaluations demonstrate the practicality of SASP for VSNs.

The protection of multicast transmission with optimized secrecy in millimeter-wave (mmWave) communication is still an open problem. In this paper, we propose a low sidelobe secret multicast transmission scheme in mmWave communication based on physical layer security techniques. By utilizing oblique projection, we jointly adopt the directional modulation (DM) and the artificial noise (AN) to achieve secret multicast transmission at low computational costs, without jeopardizing the low sidelobe transmitting pattern. Specifically, considering the constraint of multibeam with the channel knowledge of all target users, a primary transmitting weight vector of the base station (BS) is designed to achieve a low sidelobe transmitting pattern. Then, in each symbol period, the transmitting weight vector of the BS is updated by performing a linear transformation on the primary weight vector with a transformation matrix, which is obtained from the oblique projection matrices of all the target users’ channel vectors. In this way, without deteriorating the primary weight vector’s low sidelobe transmitting pattern, the obtained transmitting weight vector synthesizes the expected symbols at the target users and adds randomness to the eavesdroppers at undesired directions. Finally, the simulations demonstrate that our proposed scheme achieves outstanding communication performance for the target users at low computational costs, while keeping the high symbol error rates at the undesired directions.

We present a device-centric analysis of security and privacy attacks and defenses on extended reality (XR) devices. We present future research directions and propose design considerations to help ensure the security and privacy of XR devices.

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.

Open Radio Access Network (O-RAN) improves the flexibility and programmability of the 5G network by applying the Software-Defined Network (SDN) principles. O-RAN defines a near-real time Radio Intelligent Controller (RIC) to decouple the RAN functionalities into the control and user planes. Although the O-RAN security group offers several countermeasures against threats, RIC is still prone to attacks. In this letter, we introduce a novel attack, named Bearer Migration Poisoning (BMP), that misleads the RIC into triggering a malicious bearer migration procedure. The adversary aims to change the user plane traffic path and causes significant network anomalies such as routing blackholes. BMP has a remarkable feature that even a weak adversary with only two compromised hosts could launch the attack without compromising the RIC, RAN components, or applications. Based on our numerical results, the attack imposes a dramatic increase in signalling cost by approximately 10 times. Our experiment results show that the attack significantly degrades the downlink and uplink throughput to nearly 0 Mbps, seriously impacting the service quality and end-user experience.

Received signal Direction of Arrival (DOA) estimation represents a significant problem with multiple applications, ranging from wireless communications to radars. This problem presents significant challenges, mainly given by a large number of closely located transmitters being difficultly separable. Currently available state of the art approaches fail in providing sufficient resolution to separate and recognize the DOA of closely located transmitters, unless using a large number of antennas and hence increasing the deployment and operation costs. In this paper, we present a deep learning framework for DOA estimation under Line-of-Sight scenarios, which able to distinguish a number of closely located sources higher than the number of receivers' antennas. We first propose a formulation that maps the received signal to a higher dimensional space that allows for better identification of signal sources. Secondly, we introduce a Deep Neural Network that learns the mapping from the receiver antenna space to the extended space to avoid relying on specific receiver antenna array structures. Thanks to our approach, we reduce the hardware complexity compared to state of the art solutions and allow reconfigurability of the receiver channels. Via extensive numerical simulations, we demonstrate the superiority of our proposed method compared to state-of-the-art deep learning-based DOA estimation methods, especially in demanding scenarios with low Signal-to-Noise Ratio and limited number of snapshots.

Ride-Hailing Service (RHS) has drawn plenty of attention as it provides transportation convenience for riders and financial incentives for drivers. Despite these benefits, riders risk the exposure of sensitive location data during ride requesting to an untrusted Ride-Hailing Service Provider (RHSP). Our motivation arises from repetitive matching, i.e., the same driver is repetitively assigned to the same rider. Meanwhile, we introduce a driver exclusion function to protect riders' location privacy. Existing work on privacy-preserving RHS overlooks this function. While Secure $k$ Nearest Neighbor (S$k$NN) facilitates efficient matching, the state-of-the-art neglects a collusion attack. To solve this problem, we formally define repetitive matching and strong location privacy, and propose Mnemosyne: privacy-preserving ride matching with collusion-resistant driver exclusion. We extend the simple integration of equality checking and item exclusion to a dynamic integration. We concatenate each prefix of an acceptable identity range to each location code when generating a ride request, i.e., secure mix index. We process each prefix of the driver identity to generate a ride response, i.e., a mix token. We build an indistinguishable Bloom-filter as an index to query the token. When matching riders with drivers, the colluding parties cannot distinguish identity prefixes from location codes. We build a prototype of Mnemosyne based on servers, smartphones, and a real-world dataset. Experimental results demonstrate that Mnemosyne outperforms existing work regarding strong location privacy and computational costs.

Security and privacy issues have become a major hindrance to the broad adoption of Ride-Hailing Services (RHSs). In this article, we introduce a new collusion attack initiated by the Ride-Hailing Service Provider (RHSP) and a driver that could easily link the real riders and their anonymous requests (credentials). Besides this attack, existing work requires heavy computations to execute user matching, and it is challenging for riders to verify matching results. Meanwhile, a malicious driver may cancel an assigned ride order due to its short distance. To address these issues, we present a RHS system named Nereus to support collusion resistance, efficiency, verifiability, and accountability. First, we integrate a smart contract into a Software Guard Extensions (SGX) enclave to establish a private smart contract for collusion resistance. We use a Bloom filter to achieve efficient matching. Second, we leverage privacy-preserving range query and Merkle proofs to make matching results verifiable. Meanwhile, we adopt short group signatures to provide anonymous authentication and deposit commitments to hold the runaway driver accountable. We formally state and prove the security and privacy of Nereus. We build a prototype based on Ethereum and SGX to conduct extensive performance analysis in regard to gas costs, computational costs, and communication overhead. Experimental results show that Nereus significantly improves over existing schemes in terms of computational costs.

Many regions are in urgent need of facial masks for slowing down the spread of COVID-19. To fight the pandemic, people are contributing masks through donation systems. Most existing systems are built on a centralized architecture which is prone to the single point of failure and lack of transparency. Blockchain-based solutions neglect fundamental privacy concerns (donation privacy) and security attacks (collusion attack, stealing attack). Moreover, current auditing solutions are not designed to achieve donation privacy, thus not appropriate in our context. In this work, we design a decentralized, anonymous, and secure auditing framework Astraea based on private smart contracts for donation systems. Specifically, we integrate a Distribute Smart Contract (DiSC) with an SGX Enclave to distribute donations, prove the integrity of donation number (intention) and donation sum while preserving donation privacy. With DiSC, we design a Donation Smart Contract to refund deposits and defend against the stealing attack the collusion attack from malicious collector and transponder. We formally define and prove the privacy and security of Astraea by using security reduction. We build a prototype of Astraea to conduct extensive performance analysis. Experimental results demonstrate that Astraea is practically efficient in terms of both computation and communication.

The Control Area Network (CAN) represents the standard bus for intra-vehicular networks communication. Unfortunately, CAN was not designed to be a secure protocol. Communications over CAN do not take advantage of any security feature (e.g., cryptography and authentication), raising different vulnerabilities in critical applications. This lack of security is even more emphasized in recent CAN networks, which integrate remote connection capabilities (e.g., Bluetooth and WiFi). This insecurity-by-design led to the development of specific mechanisms to patch CAN vulnerabilities. Many proposed solutions rely on implementing optimized cryptographic primitives and assume that the cryptographic keys were previously shared among the different nodes during the production phase, omitting the issue related to keys distribution and update. We propose SENECAN, a solution that combines watermarking and wired jamming to secure the CAN bus's long-term key distribution. Our solution leverages intentional interference and spread spectrum watermarking to achieve security properties such as confidentiality, integrity, authentication, and anti-replay. Compared to other works, SENECAN does not require any CAN protocol and system architecture modification. Instead, it requires an additional CAN transceiver and an initial transmission overhead. Finally, we tested the effectiveness and functioning of the SENECAN distribution schema in a real CAN environment.

Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and-unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.

Vehicular networks are vulnerable to Distributed Denial of Service (DDoS), an extension of a Denial of Service (DoS) attack. The existing solutions for DDoS detection in vehicular networks use various Machine Learning (ML) algorithms. However, these algorithms are applicable only in a single layer in a vehicular network environment and are incapable of detecting DDoS dynamics for different layers of the network infrastructure. The recently reported attacks on transport networks reveal the fact that a research gap exists between the existing solutions and the multi-layer DDoS detection strategy requirements. Additionally, the majority of the current detection methods fail in the consideration of traffic heterogeneity and are not rate-adaptive, where both the mentioned parameters are important for an effective detection system.

The optimization techniques usually work with the maximization or minimization of the problem to obtain the local loci or cumulative global loci. Two-dimensional bio-inspired optimization techniques face convexing problems towards a global solution and use an increased number of iterations. Besides, the duality principle considers the dual optimization aspects of the problems leading to a large duality gap of uncertain deviations and optimization errors between any prime solution and its dual solution. Moreover, several problems exist where one objective function requires maximization and another objective function requires minimization using the same set of parameters and some chaining of the feedback process. In such cases, we generally use two different optimization problems as per the best suit to the problem environment and obtain the different sub-solutions of the individual problems. This increases the complexity of the system and often deviates from the original optimal solution. We address these problems of dual optimization in our present work.

It is increasingly challenging to deal with the volume,variety, velocity, and veracity of misinformation (e.g., dissemination of fake news contents, spurious posts, and fabricated images/videos) from different online platforms. In this article, we present an overview of existing machine learning and information hiding-based misinformation detection techniques and discuss the current threats and limitations of these approaches. Based on the discussion, we identify a number of potential countermeasures.

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this article, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.

In spite of being just a few years old, ransomwareis quickly becoming a serious threat to our digital infrastructures, data and services. Majority of ransomware families are requesting for a ransom payment to restore a custodian access or decrypt data which were encrypted by the ransomware earlier. Although the ransomware attack strategy seems to be simple, security specialists ranked ransomware as a sophisticated attack vector with many variations and families. Wide range of features which are available in different families and versions of ransomware further complicates their detection and analysis. Though the existing body of research provides significant discussions about ransomware details and capabilities, the all research body is fragmented. Therefore, a ransomware feature taxonomy would advance cyber defenders’ understanding of associated risks of ransomware. In this paper we provide, to the best of our knowledge, the first scientific taxonomy of ransomware features, aligned with Lockheed Martin Cyber Kill Chain(CKC) model. CKC is a well-established model in industry that describes stages of cyber intrusion attempts. To ease the challenge of applying our taxonomy in real world, we also provide the corresponding ransomware defence taxonomy aligned with Courses of Actionmatrix (an intelligence-driven defence model). We believe that this research study is of high value for the cyber security research community, as it provides the researchers with a means of assessing the vulnerabilities and attack vectors towards the intended victims.

Information-Centric Networking (ICN) is an emerging networking paradigm where named and routable data (content) is the focal point. Users send explicit requests (interests) which specify content by name, and the network handles routing these interests to some entity capable of satisfying them with the appropriate data response (producer). One key feature of ICN is opportunistic in-network content caching. This property facilitates efficient content distribution by reducing bandwidth consumption, lessening network congestion, and improving the content retrieval latency by users (consumers). Unfortunately, the same feature is also detrimental to privacy of content consumers and producers. Simple to implement, and difficult to detect, timing attacks can exploit ICN routers as “oracles” and allow an adversary to learn whether a nearby consumer recently requested certain content. The attack leverages a timing side channel that relies on router caches and is implemented by requesting a few packets from each piece of content being probed. Similarly, probing attacks that target content producers can be used to discover whether certain content has been recently distributed. After analyzing the scope and feasibility of such attacks, we propose and evaluate some efficient countermeasures that offer quantifiable privacy guarantees while retaining the benefits of ICN.

The distributed denial of service (DDoS) attacks in cloud computing requires quick absorption of attack data. DDoS attack mitigation is usually achieved by dynamically scaling the cloud resources so as to quickly identify the onslaught features to combat the attack. The resource scaling comes with an additional cost which may prove to be a huge disruptive cost in the cases of longer, sophisticated, and repetitive attacks. In this work, we address an important problem, whether the resource scaling during attack, always result in rapid DDoS mitigation? For this purpose, we conduct real-time DDoS attack experiments to study the attack absorption and attack mitigation for various target services in the presence of dynamic cloud resource scaling. We found that the activities such as attack absorption which provide timely attack data input to attack analytics, are adversely compromised by the heavy resource usage generated by the attack. We show that the operating system level local resource contention, if reduced during attacks, can expedite the overall attack mitigation. The attack mitigation would otherwise not be completed by the dynamic scaling of resources alone. We conceived a novel relation which terms “Resource Utilization Factor” for each incoming request as the major component in forming the resource contention. To overcome these issues, we propose a new “Scale Inside-out” approach which during attacks, reduces the “Resource Utilization Factor” to a minimal value for quick absorption of the attack. The proposed approach sacrifices victim service resources and provides those resources to mitigation service in addition to other co-located services to ensure resource availability during the attack. Experimental evaluation shows up to 95 percent reduction in total attack downtime of the victim service in addition to considerable improvement in attack detection time, service reporting time, and downtime of co-located services.

Nowadays, people generate and share massive amounts of content on online platforms (e.g., social networks, blogs). In 2021, the 1.9 billion daily active Facebook users posted around 150 thousand photos every minute. Content moderators constantly monitor these online platforms to prevent the spreading of inappropriate content (e.g., hate speech, nudity images). Based on deep learning (DL) advances, Automatic Content Moderators (ACM) help human moderators handle high data volume. Despite their advantages, attackers can exploit weaknesses of DL components (e.g., preprocessing, model) to affect their performance. Therefore, an attacker can leverage such techniques to spread inappropriate content by evading ACM.

IoT-enabled Smart Grid (IoT-SG) is an emerging paradigm that enables the bi-direction communication of IoT devices and hardware to efficiently collect and transmit the consumer’s information over the Internet. However, the underlying open communication network and resource-constrained capabilities of devices invite manifold challenges and threats in terms of security and privacy. To alleviate such issues, we designed a private blockchain-based access control protocol for IoT-SG using Physically Unclonable Function (PUF). Our protocol allows the Service Provider (SP) and smart meters to transfer the data in an efficient and secure manner. The participating SPs form the Peer-to-Peer (P2P) network, and each peer node is responsible for securely creating the blocks from the gathered data. Thereafter, all the peer nodes employ a voting-based consensus mechanism to verify and add the recently created block into the blockchain network. We have rigorously validated the security of our protocol under the Random or Real (RoR) model. The testbed results and security feature analysis show that our protocol is more efficient than competing ones while it also provides significant security properties.

An Internet of Things (IoT) architecture generally consists of a wide range of Internet-connected devices or things such as Android devices, and devices that have more computational capabilities (e.g., storage capacities) are likely to be targeted by ransomware authors. In this paper, we present a machine learning based approach to detect ransomware attacks by monitoring power consumption of Android devices. Specifically, our proposed method monitors the energy consumption patterns of different processes to classify ransomware from non-malicious applications. We then demonstrate that our proposed approach outperforms K-Nearest Neighbors, Neural Networks, Support Vector Machine and Random Forest, in terms of accuracy rate, recall rate, precision rate and F-measure.

Online Social Networks (OSNs), such as Facebook, provide users with tools to share information along with a set of privacy controls preferences to regulate the spread of information. Current privacy controls are efficient to protect content data. However, the complexity of tuning them undermine their efficiency when shielding contextual information (such as the social network structure) that many users believe being kept private.

In RFID systems addressing security issues, many authentication techniques require the tag to keep some sort of synchronization with the reader. In particular, this is true in those proposals that leverage hash chains. When the reader and the tag get de‐synchronized, possibly by an attacker, this paves the way to several denial of service (DoS) attacks, as well as threatening privacy (e.g., viathe timing attack). Even if de‐synchronization happens for non‐malicious causes, this event has a negative effect on performances (for instance, slowing down the authentication process).

In‐network data aggregationin wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead—sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure.

Recent technological innovations lead to the rise of a plethora of portable electronic devices such as smartphones, small household appliances, and other IoT devices. To power or recharge the battery of such devices, manufacturers identified in the ubiquitous Universal Serial Bus (USB) standard a convenient solution, as it enables both communication and energy supply. Unfortunately, the default trust on USB ports has been exploited by hackers to extract highly sensitive user data on such devices. Despite the efforts by security experts and manufacturers to detect and block this threat, an even more stealthy approach to undermine users privacy relies on side-channel attacks on the USB interface, such as electromagnetic emissions and power consumption.

Cerebellar granule cells, differentiated in vitro, were parallelly fed with [Sph- 3H]GM1 and [stearoyl- 14C]GM1, under identical conditions (10 −6M ganglioside; pulse, from 1–4 h; chase, up to 24 h after 4 h pulse) and the salvage pathways of sphingosine and stearic acid were investigated. It was observed that both sphingosine and stearic acid, liberated during the intralysosomal degradation of ganglioside, are metabolically recycled, along distinct pathways. Sphingosine is used for the biosynthesis of a number of sphingolipids, particularly ceramide, glucosyl-ceramide, gangliosides and sphingomyelin; stearic acid is utilized for the biosynthesis of sphingolipids, and to a greater extent, glycero-phospholipids, especially those endogenously richer in stearic acid (phosphatidyl-ethanolamine and phosphatidyl-choline). No evidence was provided for a salvage pathway for ceramide.

In recent years, the scale and diversity of malicious software on mobile networks have grown significantly, thereby causing considerable danger to users' property and personal privacy. In this study, we propose a malware detection method that uses the URLs visited by apps to identify malware. A multi-view neural network is used to create a malware detection model that emphasizes depth and width. This neural network can create multiple views of inputs automatically and distribute soft attention weights to focus on different features of inputs. Multiple views preserve rich semantic information from inputs for classification without requiring complicated feature engineering. In addition, we conduct comprehensive experiments to compare the proposed method with others and verify the validity of the detection model. The experimental results show that our method achieves robust and timely malware detection. It can not only effectively detect malware discovered in different months of a certain year, but also detect potentially malicious apps in the third-party app market. We also compare the detection results of the proposed method on wild apps with 10 popular anti-virus scanners, and the final result shows that our approach ranks second in terms of detection performance. [ABSTRACT FROM AUTHOR]