Biblio

The rapidly increasing malware threats must be coped with new effective malware detection methodologies. Current malware threats are not limited to daily personal transactions but dowelled deeply within large enterprises and organizations. This paper introduces a new methodology for detecting and discriminating malicious versus normal applications. In this paper, we employed Ant-colony optimization to generate two behavioural graphs that characterize the difference in the execution behavior between malware and normal applications. Our proposed approach relied on the API call sequence generated when an application is executed. We used the API calls as one of the most widely used malware dynamic analysis features. Our proposed method showed distinctive behavioral differences between malicious and non-malicious applications. Our experimental results showed a comparative performance compared to other machine learning methods. Therefore, we can employ our method as an efficient technique in capturing malicious applications.

The 6G wireless communication networks are being studied to build a powerful networking system with global coverage, enhanced spectral/energy/cost efficiency, better intelligent level and security. This paper presents a four-in-one networking paradigm named 3CL-Net that would broaden and strengthen the capabilities of current networking by introducing ubiquitous computing, caching, and intelligence over the communication connection to build 6G-required capabilities. To evaluate the practicability of 3CL-Net, this paper designs a platform based on the 3CL-Net architecture. The platform adopts leader-followers structure that could support all functions of 3CL-Net, but separate missions of 3CL-Net into two parts. Moreover, this paper has implemented part of functions as a prototype, on which some experiments are carried out. The results demonstrate that 3CL-Net is potential to be a practical and effective network paradigm to meet future requirements, meanwhile, 3CL-Net could motivate designs of related platforms as well.

Ubiquitous environment embedded with artificial intelligent consist of heterogenous smart devices communicating each other in several context for the computation of requirements. In such environment the trust among the smart users have taken as the challenge to provide the secure environment during the communication in the ubiquitous region. To provide the secure trusted environment for the users of ubiquitous system proposed approach aims to extract behavior of smart invisible entities by retrieving their behavior of communication in the network and applying the recommendation-based filters using Deep learning (RBF-DL). The proposed model adopts deep learning-based classifier to classify the unfair recommendation with fair ones to have a trustworthy ubiquitous system. The capability of proposed model is analyzed and validated by considering different attacks and additional feature of instances in comparison with generic recommendation systems.

Vehicle Ad-Hoc Networks (VANETs) are a special type of Mobile Ad-Hoc Network (MANETs). In VANETs, a group of vehicles communicates with each other to transfer data without a need for a fixed infrastructure. In this paper, we compare the performance of two routing protocols: Ad-hoc on Demand Distance Vector protocol (AODV) and Destination-Sequenced Distance Vector protocol (DSDV) in VANETs. We measure the reliability of each protocol in the packet delivery.

Steganography is one of the techniques for secure transformation of data which aims at hiding information inside other media in such a way that no one will notice. The cover media that can accommodate secret information include text, audio, image, and video. Images are the most popular covering media in steganography, due to the fact that, they are heavily used in daily applications and have high redundancy in representation. In this paper, we propose an adaptive steganography algorithm for hiding information in RGB images. To minimize visual perceptible distortion, the proposed algorithm uses edge pixels for embedding data. It detects the edge pixels in the image using the Sobel filter. Then, the message is embedded into the LSBs of the blue channel of the edge pixels. To resist statistical attacks, the distribution of the blue channel of the edge pixels is used when embedding data in the cover image. The experimental results showed that the algorithm offers high capacity for hiding data in cover images; it does not distort the quality of the stego image; it is robust enough against statistical attacks; and its execution time is short enough for online data transfer. Also, the results showed that the proposed algorithm outperforms similar approaches in all evaluation metrics.

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1\_scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.

Data security and privacy have become an important problem while big data systems are growing dramatically fast in various application fields. Paillier additive homomorphic cryptosystem is widely used in information security fields such as big data security, communication security, cloud computing security, and artificial intelligence security. However, how to improve its computational performance is one of the most critical problems in practice. In this paper, we propose two modifications to improve the performance of the Paillier cryptosystem. Firstly, we introduce a key generation method to generate the private key with low Hamming weight, and this can be used to accelerate the decryption computation of the Paillier cryptosystem. Secondly, we propose an acceleration method based on Hensel lifting in the Paillier cryptosystem. This method can obtain a faster and improved decryption process by showing the mathematical analysis of the decryption algorithm.

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach.

A grouping-proof protocol aims to generate an evidence that two or more RFID (Radio Frequency Identification) tags in a group are coexistent, which has been widely deployed in practical scenarios, such as healthcare, supply-chain management, and so on. However, existing grouping-proof protocols have many issues in security and efficiency, either incompatible with EPCglobal Class-1 Generation-2 (C1G2) standard, or vulnerable to different attacks. In this paper, we propose a lightweight grouping-proof protocol which only utilizes bitwise operations (AND, XOR) and 128-bit pseudorandom number generator (PRNG). 2-round interactions between the reader and the tags allow them to cooperate on fast authentication in parallel mode where the reader broadcasts its round messages rather than hang on for the prior tag and then fabricate apposite output for the next tag consecutively. Our design enables the reader to aggregate the first round proofs (to bind the membership of tags in the same group) generated by the tags to an authenticator of constant size (independent of the number of tags) that can then be used by the tags to generate the second round proofs (and that will be validated by the verifier). Formal security (i.e., PPT adversary cannot counterfeit valid grouping-proof that can be accepted by any verifier) of the proposed protocol relies on the hardness of the learning parity with noise (LPN) problem, which can resist against quantum computing attacks. Other appealing features (e.g., robustness, anonymity, etc.) are also inspected. Performance evaluation shows its applicability to C1G2 RFID.

The concept of usage control goes beyond traditional access control by regulating not only the retrieval but also the processing of data. To be able to remotely enforce usage control policy the processing party requires a trusted execution environ-ment such as Intel SGX which creates so-called enclaves. In this paper we introduce Multi Enclave based Code from Template (MECT), an SGX-based architecture for trusted remote policy enforcement. MECT uses a multi-enclave approach in which an enclave generation service dynamically generates enclaves from pre-defined code and dynamic policy parameters. This approach leads to a small trusted computing base and highly simplified attestation while preserving functionality benefits. Our proof of concept implementation consumes customisable code from templates. We compare the implementation with other architectures regarding the trusted computing base, flexibility, performance, and modularity. This comparison highlights the security benefits for remote attestation of MECT.

Autonomous vehicles increasingly exploit computer-vision-based object detection systems to perceive environments and make critical driving decisions. To increase the quality of images, image stabilizers with inertial sensors are added to alleviate image blurring caused by camera jitters. However, such a trend opens a new attack surface. This paper identifies a system-level vulnerability resulting from the combination of the emerging image stabilizer hardware susceptible to acoustic manipulation and the object detection algorithms subject to adversarial examples. By emitting deliberately designed acoustic signals, an adversary can control the output of an inertial sensor, which triggers unnecessary motion compensation and results in a blurred image, even if the camera is stable. The blurred images can then induce object misclassification affecting safety-critical decision making. We model the feasibility of such acoustic manipulation and design an attack framework that can accomplish three types of attacks, i.e., hiding, creating, and altering objects. Evaluation results demonstrate the effectiveness of our attacks against four academic object detectors (YOLO V3/V4/V5 and Fast R-CNN), and one commercial detector (Apollo). We further introduce the concept of AMpLe attacks, a new class of system-level security vulnerabilities resulting from a combination of adversarial machine learning and physics-based injection of information-carrying signals into hardware.

The goal of the edge computing framework is to solve the problem of management and control in the access of massive 5G terminals in the power Internet of things. Firstly, this paper analyzes the needs of IOT agent in 5G ubiquitous connection, equipment management and control, intelligent computing and other aspects. In order to meet with these needs, paper develops the functions and processes of the edge computing framework, including unified access of heterogeneous devices, protocol adaptation, edge computing, cloud edge collaboration, security control and so on. Finally, the performance of edge computing framework is verified by the pressure test of 5G wireless ubiquitous connection.

With rapid advancement of Smart Grid as well as Internet of Things (IoT), current power distribution communication network faces the challenges of satisfying the emerging data transmission requirements of ubiquitous secure coverage for distributed power services. This paper focuses on secure ubiquitous wireless communication solution for power distribution Internet of Things (PDİoT) in Smart Grid. Detailed secure ubiquitous wireless communication networking topology is presented, and integrated encryption and communication device is developed. The proposed solution supports several State Secret cryptographic algorithm including SM1/SM2/SM3/SM4 as well as forward and reverse isolation functions, thus achieving secure wireless communication for PDİoT services.

Edge computing supports the deployment of ubiquitous, smart services by providing computing and storage closer to terminal devices. However, ensuring the full security and privacy of computations performed at the edge is challenging due to resource limitation. This paper responds to this challenge and proposes an adaptive approach to defense randomization among the edge data centers via a stochastic game, whose solution corresponds to the optimal security deployment at the network's edge. Moreover, security risk is evaluated subjectively based on Prospect Theory to reflect realistic scenarios where the attacker and the edge system do not similarly perceive the status of the infrastructure. The results show that a non-deterministic defense policy yields better security compared to a static defense strategy.

In new technological world pervasive computing plays the important role in data computing and communication. The pervasive computing provides the mobile environment for decentralized computational services at anywhere, anytime at any context and location. Pervasive computing is flexible and makes portable devices and computing surrounded us as part of our daily life. Devices like Laptop, Smartphones, PDAs, and any other portable devices can constitute the pervasive environment. These devices in pervasive environments are worldwide and can receive various communications including audio visual services. The users and the system in this pervasive environment face the challenges of user trust, data privacy and user and device node identity. To give the feasible determination for these challenges. This paper aims to propose a dynamic learning in pervasive computing environment refer the challenges proposed efficient security model (ESM) for trustworthy and untrustworthy attackers. ESM model also compared with existing generic models; it also provides better accuracy rate than existing models.

Deep learning has been used in a wide range of applications like computer vision, natural language processing and image detection. The advancement in deep learning algorithms in image detection and manipulation has led to the creation of deepfakes, deepfakes use deep learning algorithms to create fake images that are at times very hard to distinguish from real images. With the rising concern around personal privacy and security, Many methods to detect deepfake images have emerged, in this paper the use of deep learning for creating as well as detecting deepfakes is explored, this paper also propose the use of deep learning image enhancement method to improve the quality of deepfakes created.

With the development of IT technology and the generalization of the Internet of Things, smart grid systems combining IoT for efficient power grid construction are being widely deployed. As a form of development for this, edge computing and blockchain technology are being combined with the smart grid. Wang et al. proposed a user authentication scheme to strengthen security in this environment. In this paper, we describe the scheme proposed by Wang et al. and security faults. The first is that it is vulnerable to a side-channel attack, an impersonation attack, and a key material change attack. In addition, their scheme does not guarantee the anonymity of a participant in the smart grid system.

Modern learning systems, say a tutoring platform, has many characteristics like digital data presentation with interactivity, mobility, which provides information about the study-content as per the learners understanding levels, intelligent learners behavior, etc. A sophisticated ubiquitous learner system maintains security and monitors the mischievous behavior of the learner, and authenticates and authorizes every learner, which is quintessential. Some of the existing security schemes aim only at single entry-point authentication, which may not suit to ubiquitous tutor platform. We propose a secured authentication scheme which is based on the information utility of the learner. Whenever a learner moves into a tutor platform, which has ubiquitous learner system technology, the system at first-begins with learners' identity authentication, and then it initiates trust evaluation after the successful authentication of the learner. Periodic credential verification of the learner will be carried out, which intensifies the authentication scheme of the system proposed. BAN logic has been used to prove the authentication in this system. The proposed authentication scheme has been simulated and analyzed for the indoor tutor platform environment.

Tunnel approach to the security and privacy aspects of communication networks has been an issue since the inception of networking technologies. Neither the technology nor the regulatory and legal frame works proactively play a significant role towards addressing the ever escalating security challenges. As we have move to ubiquitous computing paradigm where information secrecy and privacy is coupled with new challenges of human to machine and machine to machine interfaces, a transformational model for security should be visited. This research is attempted to highlight the peripheral view of IoT based miniature device security paradigm with focus on standardization, regulations, user adaptation, software and applications, low computing resources and power consumption, human to machine interface and privacy.

Computing capabilities such as real time data, unlimited connection, data from sensors, environmental analysis, automated decisions (machine learning) are demanded by many areas like industry for example decision making, machine learning, by research and military, for example GPS, sensor data collection. The possibility to make these features compatible with each domain that demands them is known as ubiquitous computing. Ubiquitous computing includes network topologies such as wireless sensor networks (WSN) which can help further improving the existing communication, for example the Internet. Also, ubiquitous computing is included in the Internet of Things (IoT) applications. In this article, it is discussed the mobility of WSN and its advantages and innovations, which make possible implementations for smart home and office. Knowing the growing number of mobile users, we place the mobile phone as the key factor of the future ubiquitous wireless networks. With secure computing, communicating, and storage capacities of mobile devices, they can be taken advantage of in terms of architecture in the sense of scalability, energy efficiency, packet delay, etc. Our work targets to present a structure from a ubiquitous computing point of view for researchers who have an interest in ubiquitous computing and want to research on the analysis, to implement a novel method structure for the ubiquitous computing system in military sectors. Also, this paper presents security and privacy issues in ubiquitous sensor networks (USN).

The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.

The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.

A recurring principle in consideration of the future of systems engineering is continual dynamic adaptation. Context drives change whether it be from potential loss (threats, vulnerabilities) or from potential gain (opportunity-driven). Contextual-awareness has great influence over the future of systems engineering and of systems security. Those contextual environments contain fitness functions that will naturally select compatible approaches and filter out the incompatible, with prejudice. We don't have to guess at what those environmental shaping forces will look like. William Gibson famously tells us why: “The future is already here, it's just not evenly distributed;” and, sometimes difficult to discern. This paper provides archetypes that 1) characterize general systems engineering for products, processes, and operations; 2) characterize the integration of security to systems engineering; and, 3) characterize contextually aware agile-security. This paper is more of a problem statement than a solution. Solution objectives and tactics for guiding the path forward have a broader range of options for subsequent treatment elsewhere. Our purpose here is to offer a short list of necessary considerations for effective contextually aware adaptive system security in the future of systems engineering.

Bluetooth (BR/EDR) is a pervasive technology for wireless communication used by billions of devices. The Bluetooth standard includes a legacy authentication procedure and a secure authentication procedure, allowing devices to authenticate to each other using a long term key. Those procedures are used during pairing and secure connection establishment to prevent impersonation attacks. In this paper, we show that the Bluetooth specification contains vulnerabilities enabling to perform impersonation attacks during secure connection establishment. Such vulnerabilities include the lack of mandatory mutual authentication, overly permissive role switching, and an authentication procedure downgrade. We describe each vulnerability in detail, and we exploit them to design, implement, and evaluate master and slave impersonation attacks on both the legacy authentication procedure and the secure authentication procedure. We refer to our attacks as Bluetooth Impersonation AttackS (BIAS).Our attacks are standard compliant, and are therefore effective against any standard compliant Bluetooth device regardless the Bluetooth version, the security mode (e.g., Secure Connections), the device manufacturer, and the implementation details. Our attacks are stealthy because the Bluetooth standard does not require to notify end users about the outcome of an authentication procedure, or the lack of mutual authentication. To confirm that the BIAS attacks are practical, we successfully conduct them against 31 Bluetooth devices (28 unique Bluetooth chips) from major hardware and software vendors, implementing all the major Bluetooth versions, including Apple, Qualcomm, Intel, Cypress, Broadcom, Samsung, and CSR.

Latest forecasts show that 50 billion devices will be connected to the Internet by 2020. These devices will provide ubiquitous data access and enable smarter interactions in all aspects of our everyday life, including vital domains such as healthcare and battlefields, where privacy is a key requirement. With the increasing adoption of IoT and the explosion of these resource-constrained devices, manual discovery and configuration become significantly challenging. Despite there is a number of resource discovery protocols that can be efficiently used in IoT deployments, none of these protocols provides any privacy consideration. This paper presents LPA-SDT, a novel technique for service discovery that builds privacy into the design from the ground up. Performance evaluation demonstrates that LPA-SDT outperforms state-of-the-art discovery techniques for resource-constrained environments while preserving user and data privacy.