Biblio

Information Security in cloud storage is a key trepidation with regards to Degree of Trust and Cloud Penetration. Cloud user community needs to ascertain performance and security via QoS. Numerous models have been proposed [2] [3] [6][7] to deal with security concerns. Detection and prevention of insider threats are concerns that also need to be tackled. Since the attacker is aware of sensitive information, threats due to cloud insider is a grave concern. In this paper, we have proposed an authentication mechanism, which performs authentication based on verifying facial features of the cloud user, in addition to username and password, thereby acting as two factor authentication. New QoS has been proposed which is capable of monitoring and detection of insider threats using Machine Learning Techniques. KNN Classification Algorithm has been used to classify users into legitimate, possibly legitimate, possibly not legitimate and not legitimate groups to verify image authenticity to conclude, whether there is any possible insider threat. A threat detection model has also been proposed for insider threats, which utilizes Facial recognition and Monitoring models. Security Method put forth in [6] [7] is honed to include threat detection QoS to earn higher degree of trust from cloud user community. As a recommendation, Threat detection module should be harnessed in private cloud deployments like Defense and Pharma applications. Experimentation has been conducted using open source Machine Learning libraries and results have been attached in this paper.

Currently, little attention has been carried out to address the feasibility of in-network caching in Information-Centric Networking (ICN) for the design and real-world deployment of future networks. Towards this line, in this paper, we propose a beneficial caching scheme in ICN by storing no more than a specific number of replicas for each content. Particularly, to realize an optimal content distribution for deploying caches in ICN, a content can be cached either partially or as a full-object corresponding to its request arrival rate and data traffic. Also, we employ a utility-based replacement in each content node to keep the most recent and popular content items in the ICN interconnections. The evaluation results show that the proposal improves the cache hit rate and cache diversity considerably, and acts as a beneficial caching approach for network and service providers in ICN. Specifically, the proposed caching mechanism is easy to deploy, robust, and relevant for the content-based providers by enabling them to offer users high Quality of Service (QoS) and gain benefits at the same time.

Software Defined Networking (SDN) focus on the isolation of control plane and data plane, greatly enhancing the network's support for heterogeneity and flexibility. However, although the programmable network greatly improves the performance of all aspects of the network, flexible load balancing across controllers still challenges the current SDN architecture. Complex application scenarios lead to flexible and changeable communication requirements, making it difficult to guarantee the Quality of Service (QoS) for SDN users. To address this issue, this paper proposes a paradigm that uses blockchain to incentive safe load balancing for multiple controllers. We proposed a controller consortium blockchain for secure and efficient load balancing of multi-controllers, which includes a new cryptographic currency balance coin and a novel consensus mechanism Proof-of-Balance (PoB). In addition, we have designed a novel game theory-based incentive mechanism to incentive controllers with tight communication resources to offload tasks to idle controllers. The security analysis and performance simulation results indicate the superiority and effectiveness of the proposed scheme.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

Cognitive radio systems aim to solve the issue of spectrum scarcity through implementation of dynamic spectrum management and cooperative spectrum access. However, the structure of such systems introduced unique types of vulnerabilities and attacks, one of which is spectrum sensing data falsification attack (SSDF). In such attacks malicious users provide incorrect observations to the fusion center of the system, which may result in severe quality of service degradation and interference for licensed users. In this paper we investigate this type of attacks and propose a combined approach to their mitigation. On the first step a reputational method is used to isolate the initially untrustworthy nodes, on the second step specialized q-out-of-m fusion rule is utilized to mitigate the remains of attack. In this paper we present theoretical analysis of the proposed combined method.

The current trend of IoT user is toward the use of services and data externally due to voluminous processing, which demands resourceful machines. Instead of relying on the cloud of poor connectivity or a limited bandwidth, the IoT user prefers to use a cloudlet-based fog computing. However, the choice of cloudlet is solely dependent on its trust and reliability. In practice, even though a cloudlet possesses a required trusted platform module (TPM), we argue that the presence of a TPM is not enough to make the cloudlet trustworthy as the TPM supports only the primitive security of the bootstrap. Besides uncertainty in security, other uncertain conditions of the network (e.g. network bandwidth, latency and expectation time to complete a service request for cloud-based services) may also prevail for the cloudlets. Therefore, in order to evaluate the trust value of multiple cloudlets under uncertainty, this paper broadly proposes the empirical process for evaluation of trust. This will be followed by a measure of trust-based reputation of cloudlets through computational intelligence such as fuzzy logic and ant colony optimization (ACO). In the process, fuzzy logic-based inference and membership evaluation of trust are presented. In addition, ACO and its pheromone communication across different colonies are being modeled with multiple cloudlets. Finally, a measure of affinity or popular trust and reputation of the cloudlets is also proposed. Together with the context of application under multiple cloudlets, the computationally intelligent approaches have been investigated in terms of performance. Hence the contribution is subjected towards building a trusted cloudlet-based fog platform.

Mobile Ad hoc Network (MANET) is one of the emerging technologies to communicate between nodes and its decentralized structure, self-configuring nature are the few properties of this Ad hoc network. Due to its undefined structure, it has found its usage in the desired and temporary communication network. MANET has many routing protocols governing it and due to its changing topology, there can be many issues arise in recent times. Problems like no central node, limited energy, and the quality of service, performance, design issues, and security challenges have been bugging the researchers. The black hole attacks are the kind that cause ad hoc network to be at loss of information and make the source to believe that it has the actual least distance path to the destination, but in real scenario the packets do not get forwarded to neighbouring nodes. In this paper, we have discussed different solutions over the past years to deal with such attacks. A summary of the schemes with their results and drawbacks in terms of performance metrics is also given.

Mobile Ad hoc Network is a very important key technology for device to device communication without any support of extra infrastructure. As it is being used as a mode of communication in various fields, protecting the network from various attacks becomes more important. In this research paper, we have created a real network scenario using random mobility of nodes and implemented Black hole Attack and Gray hole Attack, which degrades the performance of the network. In our research, we have found a novel mitigation technique which is efficient to mitigate both the attack from the network.

Wireless technology is widely used today and is growing rapidly. One of the wireless technologies is VANET where the network can communicate with vehicles (V2V) which can prevent accidents on the road. Energy is also a problem in VANET so it needs to be used efficiently. The presence of malicious nodes or nodes can eliminate and disrupt the process of data communication. The routing protocol used in this study is AODV. The purpose of this study is to analyze the comparison of blackhole attack and flooding attack against energy-efficient AODV on VANET. This research uses simulation methods and several supporting programs such as OpenStreetMap, SUMO, NS2, NAM, and AWK to test the AODV routing protocol. Quality of service (QOS) parameters used in this study are throughput, packet loss, and end to end delay. Energy parameters are also used to examine the energy efficiency used. This study uses the number of variations of nodes consisting of 20 nodes, 40 nodes, 60 nodes, and different network conditions, namely normal network conditions, network conditions with black hole attacks, and network conditions with flooding attacks. The results obtained can be concluded that the highest value of throughput when network conditions are normal, the greatest value of packet loss when there is a black hole attack, the highest end to end delay value and the largest remaining energy when there is a flooding attack.

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.

Wireless Sensor Network (WSN) is a heterogeneous type of network consisting of scattered sensor nodes and working together for data collection, processing, and transmission functions[1], [2]. Because WSN is widely used in vital matters, aspects of its security must also be considered. There are many types of attacks that might be carried out to disrupt WSN networks. The methods of attack that exist in WSN include jamming attack, tampering, Sybil attack, wormhole attack, hello flood attack, and, blackhole attack[3]. Blackhole attacks are one of the most dangerous attacks on WSN networks. Enhanced Check Agent method is designed to detect black hole attacks by sending a checking agent to record nodes that are considered black okay. The implementation will be tested right on a wireless sensor network using ZigBee technology. Network topology uses a mesh where each node can have more than one routing table[4]. The Enhanced Check Agent method can increase throughput to 100 percent.

Recently, named data networking (NDN) has been introduced to connect the world of computing devices via naming data instead of their containers. Through this strategic change, NDN brings several new features to network communication, including in-network caching, multipath forwarding, built-in multicast, and data security. Despite these unique features of NDN networking, there exist plenty of opportunities for continuing developments, especially with packet forwarding and caching. In this context, we introduce liteNDN, a novel forwarding and caching strategy for NDN networks. liteNDN comprises a cooperative forwarding strategy through which NDN routers share their knowledge, i.e. data names and interfaces, to optimize their packet forwarding decisions. Subsequently, liteNDN leverages that knowledge to estimate the probability of each downstream path to swiftly retrieve the requested data. Additionally, liteNDN exploits heuristics, such as routing costs and data significance, to make proper decisions about caching normal as well as segmented packets. The proposed approach has been extensively evaluated in terms of the data retrieval latency, network utilization, and the cache hit rate. The results showed that liteNDN, compared to conventional NDN forwarding and caching strategies, achieves much less latency while reducing the unnecessary traffic and caching activities.

In the coming future, Software-defined networking (SDN) will become a technology more responsive, fully automated, and highly secure. SDN is a way to manage networks by separate the control plane from the forwarding plane, by using software to manage network functions through a centralized control point. A distributed denial-of-service (DDoS) attack is the most popular malicious attempt to disrupt normal traffic of a targeted server, service, or network. The problem of the paper is the DDoS attack inside the SDN environment and how could use SDN specifications through the advantage of Open vSwitch programmability feature to stop the attack. This paper presents DDoS attack detection and mitigation in the SDN data-plane by applying a written SDN application in python language, based on the malicious traffic abnormal behavior to reduce the interference with normal traffic. The evaluation results reveal detection and mitigation time between 100 to 150 sec. The work also sheds light on the programming relevance with the open daylight controller over an abstracted view of the network infrastructure.

Adversarial models are well-established for cryptographic protocols, but distributed real-time protocols have requirements that these abstractions are not intended to cover. The IEEE/IEC 61850 standard for communication networks and systems for power utility automation in particular not only requires distributed processing, but in case of the generic object oriented substation events and sampled value (GOOSE/SV) protocols also hard real-time characteristics. This motivates the desire to include both quality of service (QoS) and explicit network topology in an adversary model based on a π-calculus process algebraic formalism based on earlier work. This allows reasoning over process states, placement of adversarial entities and communication behaviour. We demonstrate the use of our model for the simple case of a replay attack against the publish/subscribe GOOSE/SV subprotocol, showing bounds for non-detectability of such an attack.

The challenge of network traffic classification exists at the heart of many networking related tasks aimed at improving the overall user experience and usability of the internet. Current techniques, such as deep packet inspection, depend heavily on interaction by network administrators and engineers to maintain up to date stores of application network signatures and the infrastructure required to utilize them effectively. In this paper, we introduce Network Traffic Images, a 2-dimensional (2D) formulation of a stream of packet header lengths, which enable us to employ deep convolutional neural networks for network traffic classification. Five different network traffic image orientation mappings are carefully designed to deduce the best way to transform the 1-dimensional packet-subflow into a 2D image. Two different mapping strategies, one packet-relative and the other time-relative, are experimented with to map the packets of a packet flow to the pixels in the image. Experiments shows that high classification accuracy can be achieved with minimal manual effort using network traffic images in deep learning.

Traffic Characterization, Application Identification, Per Application Classification, and VPN/Non-VPN Traffic Characterization have been some of the most notable research topics over the past few years. Deep Packet Inspection (DPI) promises an increase in Quality of Service (QoS) for Internet Service Providers (ISPs), simplifies network management and plays a vital role in content censoring. DPI has been used to help ease the flow of network traffic. For instance, if there is a high priority message, DPI could be used to enable high-priority information to pass through immediately, ahead of other lower priority messages. It can be used to prioritize packets that are mission-critical, ahead of ordinary browsing packets. Throttling or slowing down the rate of data transfer can be achieved using DPI for certain traffic types like peer-to-peer downloads. It can also be used to enhance the capabilities of ISPs to prevent the exploitation of Internet of Things (IoT) devices in Distributed Denial-Of-Service (DDOS) attacks by blocking malicious requests from devices. In this paper, we introduce a novel architecture for DPI using neural networks utilizing layers of word embedding, convolutional neural networks and bidirectional recurrent neural networks which proved to have promising results in this task. The proposed architecture introduces a new mix of layers which outperforms the proposed approaches before.

Network traffic classification has a range of applications in network management including QoS and security monitoring. Deep Packet Inspection (DPI) is one of the effective method used for traffic classification. DPI is computationally expensive operation involving string matching between payload and application signatures. Existing traffic classification techniques perform multiple scans of payload to classify the application flows - first scan to extract the words and the second scan to match the words with application signatures. In this paper we propose an approach which can classify network flows with single scan of flow payloads using a heuristic method to achieve a sub-linear search complexity. The idea is to scan few initial bytes of payload and determine potential application signature(s) for subsequent signature matching. We perform experiments with a large dataset containing 171873 network flows and show that it has a good classification accuracy of 98%.

Bloom filters (BFs) are fast and space-efficient data structures used for set membership queries in many applications. BFs are required to satisfy three key requirements: low space cost, high-speed lookups, and fast updates. Prior works do not satisfy these requirements at the same time. The standard BF does not support deletions of items and the variants that support deletions need additional space or performance overhead. The state-of-the-art cuckoo filters (CF) has high performance with seemingly low space cost. However, the CF suffers a critical issue of varying space cost per item. This is because the exclusive-OR (XOR) operation used by the CF requires the total number of buckets to be a power of two, leading to the space inflation. To address the issue, in this paper we propose a scalable variant of the cuckoo filter called additive and subtractive cuckoo filter (ASCF). We aim to improve the space efficiency while sustaining comparably high performance. The ASCF uses the addition and subtraction (ADD/SUB) operations instead of the XOR operation to compute an item's two candidate bucket indexes based on its fingerprint. Experimental results show that the ASCF achieves both low space cost and high performance. Compared to the CF, the ASCF reduces up to 1.9x space cost per item while maintaining the same lookup and update throughput. In addition, the ASCF outperforms other filters in both space cost and performance.

Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.

Mobile Ad-hoc Network (MANET) consists of different configurations, where it deals with the dynamic nature of its creation and also it is a self-configurable type of a network. The primary task in this type of networks is to develop a mechanism for routing that gives a high QoS parameter because of the nature of ad-hoc network. The Ad-hoc-on-Demand Distance Vector (AODV) used here is the on-demand routing mechanism for the computation of the trust. The proposed approach uses the Artificial neural network (ANN) and the Support Vector Machine (SVM) for the discovery of the black hole attacks in the network. The results are carried out between the black hole AODV and the security mechanism provided by us as the Secure AODV (SAODV). The results were tested on different number of nodes, at last, it has been experimented for 100 nodes which provide an improvement in energy consumption of 54.72%, the throughput is 88.68kbps, packet delivery ratio is 92.91% and the E to E delay is of about 37.27ms.

In service-oriented computing environment (e.g. cloud computing), Cloud Customers (CCs) and Cloud Service Providers (CSPs) require to calculate the trust ranks of impending partner prior to appealing in communications. Determining trustworthiness dynamically is a demanding dilemma in an open and dynamic environment (such as cloud computing) because of many CSPs providing same types of services. Presently, there are very less number of dynamic trust evaluation scheme that permits CCs to evaluate CSPs trustworthiness from multi-dimensional perspectives. Similarly, there is no scheme that permits CSPs to evaluate trustworthiness of CCs. This paper proposes a Multidimensional Dynamic Trust Evaluation Scheme (MDTES) that facilitates CCs to evaluate the trustworthiness of CSPs from various viewpoints. Similar approach can be employed by CSPs to evaluate the trustworthiness of CCs. The proposed MDTES helps CCs to choose trustworthy CSP and to have desired QoS requirements and CSPs to choose desired and legal CCs. The simulation results illustrate the MDTES is dynamic and steady in distinguishing trustworthy and untrustworthy CSPs and CCs.

Optical burst switching (OBS) is a candidate switching paradigm for future backbone all-optical networks. However, data burst contention can be a major problem especially as the number of lightpath connections as well as the overall network radius increases. Furthermore, the absence of or limited buffering provision in core nodes, coupled with the standard one-way resources signaling aggravate contention occurrences resulting in some of the contending bursts being discarded as a consequence. Contention avoidance as well as resolution measures can be applied in such networks in order to resolve any contention issues. In that way, the offered quality of service (QoS) as well as the network performance will remain consistent and reliable. In particular, to maintain the cost effectiveness of OBS deployment, restricted intermediate buffering can be implemented to buffer contending bursts that have already traversed much of the network on their way to the intended destination. Hence in this paper we propose and analyze a restricted intermediate Node Buffering-based routing and wavelength assignment scheme (RI-RWA) scheme to address contention occurrences as well as prevent deletion of contending bursts. The scheme primarily prioritizes the selection of primary as well as deflection paths for establishing lightpath connections paths as a function of individual wavelength contention performances. It further facilitates and allows partial intermediate buffering provisioning for any data bursts that encounter contention after having already propagated more than half the network's diameter. We evaluate the scheme's performance by simulation and obtained results show that the scheme indeed does improve on key network performance metrics such as fairness, load balancing as well as throughput.

Resource scheduling in a computing system addresses the problem of packing tasks with multi-dimensional resource requirements and non-functional constraints. The exhibited heterogeneity of workload and server characteristics in Cloud-scale or Internet-scale systems is adding further complexity and new challenges to the problem. Compared with,,,, existing solutions based on ad-hoc heuristics, Machine Learning (ML) has the potential to improve further the efficiency of resource management in large-scale systems. In this paper we,,,, will describe and discuss how ML could be used to understand automatically both workloads and environments, and to help to cope with scheduling-related challenges such as consolidating co-located workloads, handling resource requests, guaranteeing application's QoSs, and mitigating tailed stragglers. We will introduce a generalized ML-based solution to large-scale resource scheduling and demonstrate its effectiveness through a case study that deals with performance-centric node classification and straggler mitigation. We believe that an MLbased method will help to achieve architectural optimization and efficiency improvement.

Intrusion Detection system (IDS) was an application which was aimed to monitor network activity or system and it could find if there was a dangerous operation. Implementation of IDS on Software Define Network architecture (SDN) has drawbacks. IDS on SDN architecture might decreasing network Quality of Service (QoS). So the network could not provide services to the existing network traffic. Throughput, delay and packet loss were important parameters of QoS measurement. Snort IDS and bro IDS were tools in the application of IDS on the network. Both had differences, one of which was found in the detection method. Snort IDS used a signature based detection method while bro IDS used an anomaly based detection method. The difference between them had effects in handling the network traffic through it. In this research, we compared both tools. This comparison are done with testing parameters such as throughput, delay, packet loss, CPU usage, and memory usage. From this test, it was found that bro outperform snort IDS for throughput, delay , and packet loss parameters. However, CPU usage and memory usage on bro requires higher resource than snort.

Deterministic latency is the key challenge that must be addressed in numerous 5G applications such as AR/VR. However, it is difficult to make customized end-to-end resource reservation across multiple ISPs using IP-based QoS mechanisms. Information-Centric Networking (ICN) provides scalable and efficient content distribution at the Internet scale due to its in-network caching and native multicast capabilities, and the deterministic latency can promisingly be guaranteed by caching the relevant content objects in appropriate locations. Existing proposals formulate the ICN cache placement problem into numerous theoretical models. However, the underlying mechanisms to support such cache coordination are not discussed in detail. Especially, how to efficiently make cache reservation, how to avoid route oscillation when content cache is updated and how to conduct the real-time latency measurement? In this work, we propose Latency Deterministic Information-Centric Networking (LD-ICN). LD-ICN relies on source routing-based latency telemetry and leverages an on-path caching technique to avoid frequent route oscillation while still achieve the optimal cache placement under the SDN architecture. Extensive evaluation shows that under LD-ICN, 90.04% of the content requests are satisfied within the hard latency requirements.