Biblio
Fog computing extends cloud computing technology to the edge of the infrastructure to support dynamic computation for IoT applications. Reduced latency and location awareness in objects' data access is attained by displacing workloads from the central cloud to edge devices. Doing so, it reduces raw data transfers from target objects to the central cloud, thus overcoming communication bottlenecks. This is a key step towards the pervasive uptake of next generation IoT-based services. In this work we study efficient orchestration of applications in fog computing, where a fog application is the cascade of a cloud module and a fog module. The problem results into a mixed integer non linear optimisation. It involves multiple constraints due to computation and communication demands of fog applications, available infrastructure resources and it accounts also the location of target IoT objects. We show that it is possible to reduce the complexity of the original problem with a related placement formulation, which is further solved using a greedy algorithm. This algorithm is the core placement logic of FogAtlas, a fog computing platform based on existing virtualization technologies. Extensive numerical results validate the model and the scalability of the proposed algorithm, showing performance close to the optimal solution with respect to the number of served applications.
Researchers and industry experts are looking at how to improve a shopper's experience and a store's revenue by leveraging and integrating technologies at the edges of the network, such as Internet-of-Things (IoT) devices, cloud-based systems, and mobile applications. The integration of IoT technology can now be used to improve purchasing incentives through the use of electronic coupons. Research has shown that targeted electronic coupons are the most effective and coupons presented to the shopper when they are near the products capture the most shoppers' dollars. Although it is easy to imagine coupons being broadcast to a shopper's mobile device over a low-power wireless channel, such a solution must be able to advertise many products, target many individual shoppers, and at the same time, provide shoppers with their desired level of privacy. To support this type of IoT-enabled shopping experience, we have designed Aggio, an electronic coupon distribution system that enables the distribution of localized, targeted coupons while supporting user privacy and security. Aggio uses cryptographic mechanisms to not only provide security but also to manage shopper groups e.g., bronze, silver, and gold reward programs) and minimize resource usage, including bandwidth and energy. The novel use of cryptographic management of coupons and groups allows Aggio to reduce bandwidth use, as well as reduce the computing and energy resources needed to process incoming coupons. Through the use of local coupon storage on the shopper's mobile device, the shopper does not need to query the cloud and so does not need to expose all of the details of their shopping decisions. Finally, the use of privacy preserving communication between the shopper's mobile device and the CouponHubs that are distributed throughout the retail environment allows the shopper to expose their location to the store without divulging their location to all other shoppers present in the store.
The prevalent use of mobile applications using location information to improve the quality of their service has arisen privacy issues, particularly regarding the extraction of user's points on interest. Many studies in the literature focus on presenting algorithms that allow to protect the user of such applications. However, these solutions often require a high level of expertise to be understood and tuned properly. In this paper, the first control-based approach of this problem is presented. The protection algorithm is considered as the ``physical'' plant and its parameters as control signals that enable to guarantee privacy despite user's mobility pattern. The following of the paper presents the first control formulation of POI-related privacy measure, as well as dynamic modeling and a simple yet efficient PI control strategy. The evaluation using simulated mobility records shows the relevance and efficiency of the presented approach.
Mobile military networks are uniquely challenging to build and maintain, because of their wireless nature and the unfriendliness of the environment, resulting in unreliable and capacity limited performance. Currently, most tactical networks implement TCP/IP, which was designed for fairly stable, infrastructure-based environments, and requires sophisticated and often application-specific extensions to address the challenges of the communication scenario. Information Centric Networking (ICN) is a clean slate networking approach that does not depend on stable connections to retrieve information and naturally provides support for node mobility and delay/disruption tolerant communications - as a result it is particularly interesting for tactical applications. However, despite ICN seems to offer some structural benefits for tactical environments over TCP/IP, a number of challenges including naming, security, performance tuning, etc., still need to be addressed for practical adoption. This document, prepared within NATO IST-161 RTG, evaluates the effectiveness of Named Data Networking (NDN), the de facto standard implementation of ICN, in the context of tactical edge networks and its potential for adoption.
Android Applications have become an integral fraction of entwined contemporary subsistence. The entire sphere is employing diverse assortment of applications for distinguished intention. Among all the flamboyant assortment of applications, some applications have engrossed apiece individual and are unanimously accepted. With apiece fleeting instant, numerous applications are emerging in the market and are contending amid the contemporary applications in use. The proposed work is a pioneering approach to develop an application for message transference in a cosseted manner. The eminence of the work lies in ensuring that the messages send are in a coded structure, more precisely in encrypted form, formulated from the proposed Cryptographic modus operandi. The focal intention of the proposed work is to augment the status of safekeeping in data transference. The work is a multidisciplinary work and includes Biological principles in devising the Cryptographic modus operandi. Hormonal system is one of the most decisive fractions of human well-being and fundamental structure. There are numerous hormones meant for diverse purposes in human anatomy, more precisely, they are exclusively distinct for male and female. Although, the numeral quotient of hormones is colossal, but in the work, preferred male and female hormones have been employed. The hormones employed, their operational cycle and their way of illustration in the proposed work opens a unique mode to encrypt data and augment the safekeeping echelon. The augmented safekeeping could unearth its employment in numerous modes and in countless places, not only for personal purposes but could also be employed for organizational purpose. The Android Application for the said Cryptographic modus operandi is an initiative for safekeeping of apiece individual employing the Application as well as a universal mold for societal impact on the whole.
In Ad hoc networks the main purpose is communication without infrastructure and there are many implementations already done on that. There is little effort done for security to prevent threats in ad hoc networks (like MANETs). It is already proven that; there is no any centralized mechanism for defence against threats, such as a firewall, an intrusion detection system, or a proxy in ad hoc networks. Ad hoc networks are very convenient due to its features like self-maintenance, self-organizing and providing wireless communication. In Ad hoc networks there is no fixed infrastructure in which every node works like simply a router which stores and forwards packet to final destination. Due to these dynamic topology features, Ad hoc networks are anywhere, anytime. Therefore, it is necessary to make a secure mechanism for the ad hoc components so that with flexibility they have that security also. This paper shows the secure and flexible implementation about to protect any ad hoc networks. This proposed system design is perfect solution to provide security with flexibility by providing a hybrid system which combines ECC and MAES to detect and prevent Ad hoc network attacks using Intrusion detection system. The complete proposed system designed on NS 2.35 software using Ubuntu (Linux) OS.
Due to the changing nature of Mobile Ad-Hoc Network (MANET) security is an important concern and hence in this paper, we carryout vector-based trust mechanism, which is established on the behavior of nodes in forwarding and dropping the data packets determines the trust on each node and we are using the Enhanced Certificate Revocation scheme (ECR), which avoid the attacker by blacklisting the blackhole attacker. To enhance more security for node and network, we assign a unique key for every individual node which can avoid most of the attacks in MANET
Mobile Ad hoc Network (MANET for short) is a new art of wireless technology that connect a group of mobile nodes in a dynamically decentralized fashion without the need of a base station, or a centralized administration, whereas each mobile node can work as a router. MANET topology changes frequently, because of the MANET dynamically formation nature, and freely to move randomly. MANET can function as standalone or can be connected to external networks. Mobile nodes are characterized with minimal human interaction, weight, less memory, and power. Despite all the pros of MANET and the widely spreading in many and critical industries, MANET has some cons and suffers from severe security issues. In this survey we emphasize on the different types of attacks at MANET protocol stack, and show how MANET is vulnerable to those attacks.
Information centric network (ICN) based Mobile Edge Computing (MEC) network has drawn growing attentions in recent years. The distributed network architecture brings new security problems, especially the identity security problem. Because of the cloud platform deployed on the edge of the MEC network, multiple channel attributes can be easily obtained and processed. Thus this paper proposes a multiple channel attributes based spoofing detection mechanism. To further reduce the complexity, we also propose an improved clustering algorithm. The simulation results indicate that the proposed spoofing detection method can provide near-optimal performance with extremely low complexity.
While the rapid adaptation of mobile devices changes our daily life more conveniently, the threat derived from malware is also increased. There are lots of research to detect malware to protect mobile devices, but most of them adopt only signature-based malware detection method that can be easily bypassed by polymorphic and metamorphic malware. To detect malware and its variants, it is essential to adopt behavior-based detection for efficient malware classification. This paper presents a system that classifies malware by using common behavioral characteristics along with malware families. We measure the similarity between malware families with carefully chosen features commonly appeared in the same family. With the proposed similarity measure, we can classify malware by malware's attack behavior pattern and tactical characteristics. Also, we apply community detection algorithm to increase the modularity within each malware family network aggregation. To maintain high classification accuracy, we propose a process to derive the optimal weights of the selected features in the proposed similarity measure. During this process, we find out which features are significant for representing the similarity between malware samples. Finally, we provide an intuitive graph visualization of malware samples which is helpful to understand the distribution and likeness of the malware networks. In the experiment, the proposed system achieved 97% accuracy for malware classification and 95% accuracy for prediction by K-fold cross-validation using the real malware dataset.
Better understanding of mobile applications' behaviors would lead to better malware detection/classification and better app recommendation for users. In this work, we design a framework AppDNA to automatically generate a compact representation for each app to comprehensively profile its behaviors. The behavior difference between two apps can be measured by the distance between their representations. As a result, the versatile representation can be generated once for each app, and then be used for a wide variety of objectives, including malware detection, app categorizing, plagiarism detection, etc. Based on a systematic and deep understanding of an app's behavior, we propose to perform a function-call-graph-based app profiling. We carefully design a graph-encoding method to convert a typically extremely large call-graph to a 64-dimension fix-size vector to achieve robust app profiling. Our extensive evaluations based on 86,332 benign and malicious apps demonstrate that our system performs app profiling (thus malware detection, classification, and app recommendation) to a high accuracy with extremely low computation cost: it classifies 4024 (benign/malware) apps using around 5.06 second with accuracy about 93.07%; it classifies 570 malware's family (total 21 families) using around 0.83 second with accuracy 82.3%; it classifies 9,730 apps' functionality with accuracy 33.3% for a total of 7 categories and accuracy of 88.1 % for 2 categories.
Smartphones have evolved over the years from simple devices to communicate with each other to fully functional portable computers although with comparatively less computational power but inholding multiple applications within. With the smartphone revolution, the value of personal data has increased. As technological complexities increase, so do the vulnerabilities in the system. Smartphones are the latest target for attacks. Android being an open source platform and also the most widely used smartphone OS draws the attention of many malware writers to exploit the vulnerabilities of it. Attackers try to take advantage of these vulnerabilities and fool the user and misuse their data. Malwares have come a long way from simple worms to sophisticated DDOS using Botnets, the latest trends in computer malware tend to go in the distributed direction, to evade the multiple anti-virus apps developed to counter generic viruses and Trojans. However, the recent trend in android system is to have a combination of applications which acts as malware. The applications are benign individually but when grouped, these may result into a malicious activity. This paper proposes a new category of distributed malware in android system, how it can be used to evade the current security, and how it can be detected with the help of graph matching algorithm.
Mobile ad hoc networks (MANETs) are self-configuring, dynamic networks in which nodes are free to move. These nodes are susceptible to various malicious attacks. In this paper, we propose a distributed trust-based security scheme to prevent multiple attacks such as Probe, Denial-of-Service (DoS), Vampire, User-to-Root (U2R) occurring simultaneously. We report above 95% accuracy in data transmission and reception by applying the proposed scheme. The simulation has been carried out using network simulator ns-2 in a AODV routing protocol environment. To the best of the authors' knowledge, this is the first work reporting a distributed trust-based prevention scheme for preventing multiple attacks. We also check the scalability of the technique using variable node densities in the network.