Advertising is the most crucial part of all social networking sites. The phenomenal rise of social media has resulted in a general increase in the availability of customer tastes and preferences, which is a positive development. This information may be used to improve the service that is offered to users as well as target advertisements for customers who already utilize the service. It is essential while delivering relevant advertisements to consumers, to take into account the geographic location of the consumers. Customers will be ecstatic if the offerings displayed to them are merely available in their immediate vicinity. As the user’s requirements will vary from place to place, location-based services are necessary for gathering this essential data. To get users to stop thinking about where they are and instead focus on an ad, location-based advertising (LBA) uses their mobile device’s GPS to pinpoint nearby businesses and provide useful information. Due to the increased two-way communication between the marketer and the user, mobile consumers’ privacy concerns and personalization issues are becoming more of a barrier. In this research, we developed a collaborative filtering-based hybrid CNN-LSTM model for recommending geographically relevant online services using deep neural networks. The proposed hybrid model is made using two neural networks, i.e., CNN and LSTM. Geographical information systems (GIS) are used to acquire initial location data to collect precise locational details. The proposed LBA for GIS is built in a Python simulation environment for evaluation. Hybrid CNN-LSTM recommendation performance beats existing location-aware service recommender systems in large simulations based on the WS dream dataset.

The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.

Advances of implantable medical devices (IMD) are transforming the traditional method of providing medical treatment, especially those patients under the most challenging condition. Accordingly, the IMD-enabled artificial pancreas system (APS) has now reached global market. It helped many patients suffering from chronic disease, called diabetes mellitus, in monitoring and maintaining blood glucose level conveniently. However, this advancement is accompanied by various security threats that place the life of patients at risk. Hence, protective measures, especially against yet unknown threats, are of paramount importance. This paper proposes a specification-based misbehavior detection system (SMDS) as an alternative solution to effectively mitigate security threats. Moreover, an outlier detection algorithm is also introduced to validate integrity of unprotected data transmitted by the different components. The monitor agent applies a smoothened-trust-based scheme to assess the trustworthiness of the APS. To demonstrate effectiveness of the proposed method, we first extend the UVA/Padova simulator for glucose-insulin data collection and subsequently simulate scenario with well-behave and malicious APS in MATLAB. The results show that there exists an optimal trust threshold that can achieve high specificity and sensitivity rate. Moreover, the proposed technique was compared to contemporary machine learning classifier including decision tree, support vector machine, k-nearest neighbor, and the SMDS called SMDAps. It is shown that our approach can dominate detection performance, especially to malicious behavior that manifests habitually (hidden mode).

We propose a lightweight specification-based misbehavior detection management technique to efficiently and effectively detect misbehavior of an IoT device embedded in a medical cyber physical system through automatic model checking and formal verification. We verify our specification-based misbehavior detection technique with a patient-controlled analgesia (PCA) device embedded in a medical health monitoring system. Through extensive ns3 simulation, we verify its superior performance over popular machine learning anomaly detection methods based on support vector machine (SVM) and k-nearest neighbors (KNN) techniques in both effectiveness and efficiency performance metrics.

Unmanned Aerial Vehicles (UAV)-based civilian or military applications become more critical to serving civilian and/or military missions. The significantly increased attention on UAV applications also has led to security concerns particularly in the context of networked UAVs. Networked UAVs are vulnerable to malicious attacks over open-air radio space and accordingly intrusion detection systems (IDSs) have been naturally derived to deal with the vulnerabilities and/or attacks. In this paper, we briefly survey the state-of-the-art IDS mechanisms that deal with vulnerabilities and attacks under networked UAV environments. In particular, we classify the existing IDS mechanisms according to information gathering sources, deployment strategies, detection methods, detection states, IDS acknowledgment, and intrusion types. We conclude this paper with research challenges, insights, and future research directions to propose a networked UAVIDS system which meets required standards of effectiveness and efficiency in terms of the goals of both security and performance.

This article discusses a lightweight behavior rule specification-based monitoring solution for identifying misbehavior of an embedded IoT device. These unusual activities are exhibited because of attacks exploiting the vulnerability exposed through automatic model checking and formal verification. It is conclusive in the presented research that rule specification-based misbehavior detection technique outperforms contemporary anomaly-based misbehavior detection techniques for an unmanned aerial vehicle (UAV) cyber-physical system.

Plagiarism denotes the act of copying someone else's idea (or, works) and claiming it as his/her own. Plagiarism detection is the procedure to detect the texts of a given document which are plagiarized, i.e. copied from from some other documents. Potential challenges are due to the facts that plagiarists often obfuscate the copied texts; might shuffle, remove, insert, or replace words or short phrases; might also restructure the sentences replacing words with synonyms; and changing the order of appearances of words in a sentence. In this paper we propose a technique based on textual similarity for external plagiarism detection. For a given suspicious document we have to identify the set of source documents from which the suspicious document is copied. The method we propose comprises of four phases. In the first phase, we process all the documents to generate tokens, lemmas, finding Part-of-Speech (PoS) classes, character-offsets, sentence numbers and named-entity (NE) classes. In the second phase we select a subset of documents that may possibly be the sources of plagiarism. We use an approach based on the traditional Vector Space Model (VSM) for this candidate selection. In the third phase we use a graph-based approach to find out the similar passages in suspicious document and selected source documents. Finally we filter out the false detections1.