Prospects of cloud computing as a technology that optimizes resources, reduces complexity, and provides cost‐effective solutions to its consumers are well established. The future of cloud is the “cloud of clouds,” where cloud service providers (CSPs) collaborate with each other to provide ever‐scalable solutions to their customers. However, one of the most restricting factors toward the use of the cloud by its consumers is their concerns about data security. Most sensitive to any organization is its data, thus, to give confidence to these organizations to put their data in the cloud requires a trustworthy framework. Therefore, this paper proposes an inter‐cloud data security framework, which is a set of controls and a mechanism to measure trust for data sharing based on compliance with the controls. The proposed framework for building inter‐cloud trust for data security (FBI‐TDS) defines a set of data security controls extracted to cover the possible data‐related threats linked with various inter‐cloud use cases. As part of FBI‐TDS, a mechanism is suggested that would enable CSPs to view compliance with data security controls and the overall trustworthiness of other CSPs. This would enable them to decide the level of interaction that they might undertake, depending upon their data security commitments. A data security compliance monitor service is proposed which measures compliance with data security controls. This service communicates with data trust as a service (DTaaS), which measures the trustworthiness of a CSP based on its total compliance value, users’ feedback rating, and cloud security auditor rating. CSPs who subscribe to DTaaS would be able to view the trustworthiness of other CSPs, yet they would be bound to provide access to the service to measure theirs as well. This new approach to data security in inter‐cloud is a mix of data security controls, their measure of compliance, and based on this trust value of a CSP for handling data. The proposed solution thus promotes the cloud of clouds by securing inter‐cloud interactions for data‐related use cases.

The emergence of software-defined networking (SDN) has revolutionized the management of an enterprise network. The SDN-based design provides flexibility in network management, which spans over multiple applications, e.g., routing, switching, forwarding, and controlling. It reduces the reliance on vendor-specific devices and middlebox solutions, such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), etc. Furthermore, due to the integration of different technologies, privacy is one of the core issues faced by the enterprise. Host anonymity is one of the techniques to safeguard against privacy attacks; however, the existing anonymization solutions provide better anonymity, but at the cost of higher latency and are most suited for Internet traffic. To tackle this issue in an enterprise network, we propose an SDN-based communication framework using enterprise integration patterns (EIPs) that offers anonymous communication in an enterprise environment. Host anonymity is achieved by replacing the real IP address with the spoofed IP address during the transmission of data packets inside the network. Unlike the traditional networks, SDN can modify the header fields of packets as they traverse in the network from the source to the destination. In addition to the host anonymity, this framework also provides context-aware communication by leveraging the SDN global visibility characteristic, where application services are discoverable on the network without disclosing the addresses of the application servers. Moreover, context-aware services enable network traffic to be routed based on the application-layer services rather than the network-layer information. In the end, evaluation of the proposed framework is carried out with respect to the performance of anonymous communication, computational complexity, and security of the complete proposed framework. In addition, we also highlighted that the proposed framework is more suitable for heterogeneous network environments such as Internet of Things-based solutions.

Android has become ubiquitous by taking more than 86 % of the market share. This rapid expansion has brought a slew of new threats, including privacy breaches, data thefts, and cyberespionage. As today's malware are highly sophisticated and capable to evade even state-of-the-art malware detection tools using obfuscation and repacking techniques. However, existing surveys possess challenges for researchers as they classify tools based on analysis methodology such as static and dynamic analysis. They do not cover accuracy rate, false-positive rate, effectiveness, performance evaluation, and additional features such as secure backup and spam blocking. Therefore, to check the strength of existing anti-malware tools, we present an in-depth study of Android anti-malware tools built over the last decade. This research classifies anti-malware tools, according to their analysis methodology along with their protection capabilities, performance, accuracy rate, usability, and ability to classify malware families. Based on our thorough studies, shortcomings are discussed and research gaps have been identified.

Traditional anti-virus technologies have failed to keep pace with proliferation of malware due to slow process of their signatures and heuristics updates. Similarly, there are limitations of time and resources in order to perform manual analysis on each malware. There is a need to learn from this vast quantity of data, containing cyber attack pattern, in an automated manner to proactively adapt to ever-evolving threats. Machine learning offers unique advantages to learn from past cyber attacks to handle future cyber threats. The purpose of this research is to propose a framework for multi-classification of malware into well-known categories by applying different machine learning models over corpus of malware analysis reports. These reports are generated through an open source malware sandbox in an automated manner. We applied extensive pre-modeling techniques for data cleaning, features exploration and features engineering to prepare training and test datasets. Best possible hyper-parameters are selected to build machine learning models. These prepared datasets are then used to train the machine learning classifiers and to compare their prediction accuracy. Finally, these results are validated through a comprehensive 10-fold cross-validation methodology. The best results are achieved through Gaussian Naive Bayes classifier with random accuracy of 96% and 10-Fold Cross Validation accuracy of 91.2%. The said framework can be deployed in an operational environment to learn from malware attacks for proactively adapting matching counter measures.

Lack of healthcare infrastructure has caused millions of deaths in developing countries. People in such countries generally suffer from infectious diseases and are denied treatment at appropriate time or not warned about emerging epidemics. This mainly happens because of poor establishment of public health services, lack of access to health statistics and nonexistence of precise medical data. The deployment of e-health and specifically Electronic Health Record (EHR) system may help in upgrading and boosting healthcare in developing countries like Pakistan. This paper studies and compares different approaches taken by a group of countries from North America, Europe, Western Asia and Africa for developing and implementing a national EHR in the public health system. The foremost challenges highlighted in this paper are of integration, interoperability, trainings, awareness, standardization, funds, and legal framework, which would help in developing a National EHR system in Pakistan. A National Healthcare Information System (NHIS) is proposed for health sector of Pakistan. The proposed system will be capable of efficient storage, management and sharing of electronic health information of patients in Pakistan. NHIS will also help in conducting surveys and analysis of health statistics for future health planning.

One of the major challenges in a wireless sensor network (WSN) is to extend the network's lifetime by minimizing the energy consumption. One of the ways to do so is to reduce network congestion as it increases delays and introduces additional packet collisions- thus, adversely affecting network performance. In this paper, we analyze this issue in routing and take an evolutionary game theoretic approach to show how sensor nodes in a WSN could evolve their routing strategies to transmit data packets in an efficient and stable manner. We derive the equilibrium state for the routing game and prove that there is no mutant- an individual node that adopts another strategy to invade the evolutionary stable strategy (ESS). In addition, we introduce a replicator dynamic model to show the behavior of nodes with various strategies over time. The proposed equilibrium solution aims to alleviate congestion and thereby improves the network lifetime. Simulation results show that the proposed system is successful in converging strategy choices to ESS even under dynamic network conditions.

Abstract Collocated cognitive radio networks (CRNs) employ coexistence protocols to share the spectrum when it is not being used by the licensed primary users. These protocols work under the assumption that all spectrum bands provide the same level of quality of service, which is somewhat simplistic because channel conditions as well as the licensee's usage of allocated channels can vary significantly with time and space. These circumstances dictate that some channels may be considered better than others; therefore, CRNs are expected to have a preference over the choice of available channels. Because all CRNs are assumed to be rational and select the best available channels, it can lead to an imbalance in contention for disparate channels, degraded quality of service, and an overall inefficient utilization of spectrum resource. In this paper, we analyze this situation from a game theoretic perspective and model the coexistence of CRNs with heterogeneous spectrum as an evolutionary anti‐coordination spectrum‐sharing game. We derive the evolutionarily stable strategy (ESS) of the game by proving that it cannot be invaded by a greedy strategy. We also derive the replicator dynamics of the proposed evolutionary game, a mechanism with which players can learn from their payoff outcomes of strategic interactions and modify their strategies at every stage of the game and subsequently converge to ESS. Because all CRNs approach ESS based solely upon the common knowledge payoff observations, the evolutionary game can be implemented in a distributed manner. Finally, we analyze the game from the perspective of fairness using Jain's fairness index under selfish behavior from CRNs. Copyright © 2016 John Wiley & Sons, Ltd.

Evil Twin Attack (ETA) refers to a rogue Wi-Fi Access Point (AP) that appears to be a legitimate one but actually has been set up to eavesdrop on wireless communications [1]. Most of existing detection techniques assume that the attacker will use the same legitimate wireless network gateway to pass through victim's wireless data. These detection methods will fail if the attacker uses a different gateway, such as using his own broadband cellular connection through his own smartphone. In this paper, we present a new client-side detection method to detect such an ETA that uses a different gateway from the legitimate one. It relies on SSL/TCP connection to an arbitrary remote web server to avoid attacker's misleading message, and trying to detect the changing of gateway's public IP address by switching from one AP to another in the middle of the SSL/TCP connection. The detection method is on the client side which makes it more convenient for users to deploy and ensure their security.

Cognitive Radio Network (CRN) is an emerging paradigm that makes use of Dynamic Spectrum Access (DSA) to communicate opportunistically, in the un-licensed Industrial, Scientific and Medical bands or frequency bands otherwise licensed to incumbent users such as TV broadcast. The opportunistic mode of data transfer introduces network-wide quiet periods for spectrum sensing and increased packet losses due to activities by Primary Users. TCP attributes packet delays and losses to congestion in the Internet, which does not perform well in wireless networks where the medium has inherent higher error rates than wired Internet. Opportunistic mode of communication in CRNs aggravates the already error-prone wireless communication resulting in further degradation of TCP performance due to higher latency and decreased throughput. This paper presents an analysis of TCP performance in IEEE 802.22 Wireless Regional Area Network (WRAN) based on Cognitive Radio Networks and proposes two approaches to improve the networking performance. The first approach makes base station resort to local recovery of lost frames between CRN base station and its clients, while the second approach implements a modified split TCP connection in which the base station sends crafted acknowledgements back to an Internet-side host on behalf of the corresponding CRN client to boost transmission speed. To the best of our knowledge, this is the first effort to study TCP performance in IEEE 802.22 based WRANs. Simulation results show that the proposed mechanisms result in improvement in TCP performance by as much as 20 times and conserve bandwidth by reducing retransmission overheads.

IEEE 802.22 Cognitive Radio Networks (CRN) employ a two-stage quiet period mechanism based on a mandatory Fast Sensing and an optional Fine Sensing stage for Dynamic Spectrum Access (DSA) during every super frame. However, the two-stage spectrum sensing approach presents an opportunity to malicious users where they can launch a smart Denial of Service (DoS) attack by transmitting a very short jamming signal during the fast sensing stage and thus forcing the CRN to carry out fine sensing, thereby wasting spectrum opportunities for honest Secondary Users (SU). In this paper, we present ‘DS3’: A Dynamic and Smart Spectrum Sensing algorithm, which minimizes the effects of jamming as well as noise on the fast sensing phase of DSA. It improves system's efficiency by striking a balance between spectrum utilization by SUs and delay in the detection of Primary Users' (PU) presence on the spectrum, using a dynamic fine sensing decision algorithm with minimal overhead.

The most important component of a vehicular ad hoc network (VANET), besides VANET-enabled vehicles, is roadside units (RSUs). The effectiveness of a VANET largely depends on the density and location of these RSUs. During the initial stages of VANET, it will not be possible to deploy a large number of RSUs either due to the low market penetration of VANET-enabled vehicles or due to the deployment cost of RSUs. There is, therefore, a need to optimally place a limited number of RSUs in a given region in order to achieve maximum performance. In this paper, we present two different optimization methods for placement of a limited number of RSUs in an urban region: an analytical Binary Integer Programming (BIP) method and a novel Balloon Expansion Heuristic (BEH) method. BIP method utilizes branch and bound approach to find an optimal analytical solution whereas BEH method uses balloon expansion analogy to find an optimal or near optimal solution. Our evaluations show that both methods perform optimally or near optimally compared with the exhaustive method. Further, BEH method is more versatile and performs better than BIP method in terms of computational cost and scalability.