Biblio

The exceptional attributes of impromptu network of being framework less, self-composed and unconstrained make the task more challenging to secure it. In mobile Ad-hoc network nodes reliant on one another for transmitting information, that make MANET helpless against different sorts of security attacks. These security attacks can be arranged as Passive and Active attacks. Wormhole is an Active attack and considered generally risky as it can make significant harm routing. Various secure routing mechanism has been created are based on cryptography mechanism, need pre-organized structure, centralized authority, or need external hardware, etc. These components are unreasonable due to restricted accessible assets in MANET. In this paper, we are proposing an effective trust-based mechanism based on the concept of Node to Node packet delay for the detection of the malevolent node of wormhole. The trust value of each node is calculated by observing the packet transaction among adjacent nodes and later this trust value is used for identification of malevolent node. Based on the trust values, further routing decisions and selecting a secured route can be perform.

This work proposes a plausible detection scheme for Hardware Trojan (HT) detection in frequency domain analysis. Due to shrinking technology every node consumes low power values (in the range of $μ$W) which are difficult to manipulate for HT detection using conventional methods. The proposed method utilizes the time domain power signals which is converted to frequency domain that represents the implausible signals and analyzed. The precision of HT detection is found to be increased because of the magnified power values in frequency domain. This work uses ISCAS89 bench mark circuits for conducting experiments. In this, the wide range of power values that spans from 695 $μ$W to 22.3 $μ$W are observed in frequency domain whereas the respective powers in time domain have narrow span of 2.29 $μ$W to 0.783 $μ$W which is unconvincing. This work uses the wide span of power values to identify HT and observed that the mid-band of frequencies have larger footprints than the side bands. These methods intend to help the designers in easy identification of HT even of single gate events.

A distributed denial of service attack is a major security challenge in modern communications networks. In this article, we propose models that capture all the key performance indicators of synchronized denial of service protection mechanisms. As a result of the conducted researches, it is found out that thanks to the method of delay detection it is possible to recognize semi-open connections that are caused by synchronous flood and other attacks at an early stage. The study provides a mechanism for assessing the feasibility of introducing and changing the security system of a wireless sensor network. The proposed methodology will allow you to compare the mechanisms of combating denial of service for synchronized failures and choose the optimal protection settings in real-time.

Globalization of IC manufacturing has led to increased security concerns, notably IP theft. Several logic locking techniques have been developed for protecting designs, but they typically display very large overhead, and are generally susceptible to deciphering attacks. In this paper, we propose latch-based logic locking, which manipulates both the flow of data and logic in the design. This method converts an interconnected subset of existing flip-flops to pairs of latches with programmable phase. In tandem, decoy latches and logic are added, inhibiting an attacker from determining the actual design functionality. To validate this technique, we developed and verified a locking insertion flow, analyzed PPA and ATPG overhead on benchmark circuits and industry cores, extended existing attacks to account for the technique, and taped out a demonstration chip. Importantly, we show that the design overhead with this approach is significantly less than with previous logic locking schemes, while resisting model checker-based, oracle-driven attacks. With minimal delay overhead, large numbers of decoy latches can be added, cheaply increasing attack resistance.

As a new type of complex system, multi delay network in the field of information security undertakes the important responsibility of solving information congestion, balancing network bandwidth and traffic. The problems of data loss, program failure and a large number of system downtime still exist in the conventional multi delay system when dealing with the problem of information jam, which makes the corresponding reliability of the whole system greatly reduced. Based on this, this paper mainly studies and analyzes the stability system and reliability of the corresponding multi delay system in the information security perspective. In this paper, the stability and reliability analysis of multi delay systems based on linear matrix and specific function environment is innovatively proposed. Finally, the sufficient conditions of robust asymptotic stability of multi delay systems are obtained. At the same time, the relevant stability conditions and robust stability conditions of multi delay feedback switched systems are given by simulation. In the experimental part, the corresponding data and conclusions are simulated. The simulation results show that the reliability and stability analysis data of multi delay system proposed in this paper have certain experimental value.

This paper presents Forensic methods for detection and prevention of multiple attacks along with neural networks like Denial-of-Service (DoS), probe, vampire, and User-to-Root (U2R) attacks, in a Mobile Ad hoc Network (MANET). We accomplish attacker(s) detection and prevention percentage upto 99% in varied node density scenarios 50/100/150.

The deployment of datasets in the heterogeneous edge-cloud computing paradigm has received increasing attention in state-of-the-art research. However, due to their large sizes and the existence of private scientific datasets, finding an optimal data placement strategy that can minimize data transmission as well as improve performance, remains a persistent problem. In this study, the advantages of both edge and cloud computing are combined to construct a data placement model that works for multiple scientific workflows. Apparently, the most difficult research challenge is to provide a data placement strategy to consider shared datasets, both within individual and among multiple workflows, across various geographically distributed environments. According to the constructed model, not only the storage capacity of edge micro-datacenters, but also the data transfer between multiple clouds across regions must be considered. To address this issue, we considered the characteristics of this model and identified the factors that are causing the transmission delay. The authors propose using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO) to distribute dataset during workflow execution. Based on this, a new data placement strategy named DE-DPSO-DPS is proposed. DE-DPSO-DPS is evaluated using several experiments designed in simulated heterogeneous edge-cloud computing environments. The results demonstrate that our data placement strategy can effectively reduce the data transmission time and achieve superior performance as compared to traditional strategies for data-sharing scientific workflows.

The Security is a vital aspect of cloud service as it comprises of data that belong to multiple users. Cloud service providers are responsible for maintaining data integrity, confidentiality and availability. They must ensure that their infrastructure and data are protected from intruders. In this research work Intrusion Detection System is designed to detect malicious server by using Cuckoo Search (CS) along with Artificial Intelligence. CS is used for feature optimization with the help of fitness function, the server's nature is categorized into two types: normal and attackers. On the basis of extracted features, ANN classify the attackers which affect the networks in cloud environment. The main aim is to distinguish attacker servers that are affected by DoS/DDoS, Black and Gray hole attacks from the genuine servers. Thus, instead of passing data to attacker server, the server passes the data to the genuine servers and hence, the system is protected. To validate the performance of the system, QoS parameters such as PDR (Packet delivery rate), energy consumption rate and total delay before and after prevention algorithm are measured. When compared with existing work, the PDR and the delay have been enhanced by 3.0 %and 21.5 %.

Over the last couple of decades, mobile ad-hoc networks (MANETs) have been at the forefront of research, yet still are afflicted by high network fragmentation, due to their continuous node mobility and geographical dispersion. To address these concerns, a new paradigm was proposed, Information-Centric Networks (ICN), whose focus is the delivery of Content based on names. This article aims to use ICN concepts towards the delivery of both urgent and non-urgent information in urban mobile environments. In order to do so, a context-based forwarding strategy was proposed, with a very clear goal: to take advantage of both packet Names and Data, and node's neighborhood analysis in order to successfully deliver content into the network in the shortest period of time, and without worsening network congestion. The design, implementation and validation of the proposed strategy was performed using the ndnSIM platform along with real mobility traces from communication infrastructure of the Porto city. The results show that the proposed context-based forwarding strategy presents a clear improvement regarding the Data resolution, while maintaining network overhead at a constant.

The ever-increasing growth of urban populations coupled with recent mobile data usage trends has led to an unprecedented increase in wireless devices, services and applications, with varying quality of service needs in terms of latency, data rate, and connectivity. To cope with these rising demands and challenges, next-generation wireless networks have resorted to cloud radio access network (Cloud-RAN) technology as a way of reducing latency and network traffic. A concrete example of this is New York City's LinkNYC network infrastructure, which replaces the city's payphones with kiosk-like structures, called Links, to provide fast and free public Wi-Fi access to city users. When enabled with data storage capability, these Links can, for example, play the role of edge cloud devices to allow in-network content caching so that access latency and network traffic are reduced. In this paper, we propose HybridCache, a hybrid proactive and reactive in-network caching scheme that reduces content access latency and network traffic congestion substantially. It does so by first grouping edge cloud devices in clusters to minimize intra-cluster content access latency and then enabling cooperative-proactively and reactively-caching using LSTM-based prediction to minimize in-network content redundancy. Using the LinkNYC network as the backbone infrastructure for evaluation, we show that HybridCache reduces the number of hops that content needs to traverse and increases cache hit rates, thereby reducing both network traffic and content access latency.

Traffic congestion is a critical problem in urban area. In this study, our objective is the control of traffic lights in an urban environment, in order to avoid traffic jams and optimize vehicle traffic; we aim to minimize the total waiting time. Our system is based on a new paradigm, which is deep reinforcement learning; it can automatically learn all the useful characteristics of traffic data and develop a strategy optimizing adaptive traffic light control. Our system is coupled to a microscopic simulator based on agents (Simulation of Urban MObility - SUMO) providing a synthetic but realistic environment in which the exploration of the results of potential regulatory actions can be carried out.

Layer assignment, a major step in global routing of integrated circuits, is usually performed to assign segments of nets to multiple layers. Besides the traditional optimization goals such as overflow and via count, interconnect delay plays an important role in determining chip performance and has been attracting much attention in recent years. Accordingly, in this paper, we propose MiniDelay, a timing-aware layer assignment algorithm to minimize delay for advanced technology nodes, taking both wire congestion and coupling effect into account. MiniDelay consists of the following three key techniques: 1) a non-default-rule routing technique is adopted to reduce the delay of timing critical nets, 2) an effective congestion assessment method is proposed to optimize delay of nets and via count simultaneously, and 3) a net scalpel technique is proposed to further reduce the maximum delay of nets, so that the chip performance can be improved in a global manner. Experimental results on multiple benchmarks confirm that the proposed algorithm leads to lower delay and few vias, while achieving the best solution quality among the existing algorithms with the shortest runtime.

One of the most important information in Wireless Sensor Networks (WSNs) is the location of each sensor node. This kind of information is very attractive to attackers for real position exposure of nodes making the whole network vulnerable to different kinds of attacks. According to WSNs privacy, there are two types of threats affect the network: Contextual and Content privacy. In this work, we study contextual privacy, where an eavesdropper tries to find the location of the source or sink node. We propose a Multi-Sinks Cluster-Based Location Privacy Protection (MSCLP) scheme in WSNs that divides the WSN into clusters, each cluster managed by one cluster head (CH). Each CH sends random fake packets in a loop then sends the real packet to the neighbor's CHs using a dynamic routing method to confuse the attacker from tracing back the real packet to reveal the actual location of the source node, we are taking in our consideration two important metrics: the energy consumption, and the delay.

Internet of Things (IoT) enable information transmission and sharing among massive IoT devices. However, the key establishment and management in IoT become more challenging due to the low latency requirements and resource constrained IoT devices. In this work, we propose a practical physical layer based secret key sharing scheme for MIMO (multiple-input-multiple-output) IoT devices to reduce the communication delay caused by key establishment of MIMO IoT devices. This is because the proposed scheme attachs secret key sharing with communication simultaneously. It is achieved by the proposed MIMO self-injection AN (SAN) tranmsission, which is designed to deliberately maximum the receive SNR (signal to noise ratio) at different antenna of the legitimate IoT device, based on the value of secret key sharing to him. The simulation results verified the validity and security of the proposed scheme.

In this article, authors present the results of testing the modified HTB traffic control algorithm in an experimental setup. The algorithm is implemented as a Linux kernel module. An analysis of the experimental results revealed the effect of uneven packet loss in priority classes. In the second part of the article, the authors propose a solution to this problem by applying a distribution scheme for the excess of tokens, according to which excess class tokens are given to the leaf with the highest priority. The new modification of the algorithm was simulated in the AnyLogic environment. The results of an experimental study demonstrated that dividing the excess tokens of the parent class between daughter classes is less effective in terms of network performance than allocating the excess tokens to a high-priority class during the competition for tokens between classes. In general, a modification of the HTB algorithm that implements the proposed token surplus distribution scheme yields more consistent delay times for the high-priority class.

Current Internet Protocol routing provides minimal privacy, which enables multiple exploits. The main issue is that the source and destination addresses of all packets appear in plain text. This enables numerous attacks, including surveillance, man-in-the-middle (MITM), and denial of service (DoS). The talk explains how these attacks work in the current network. Endpoints often believe that use of Network Address Translation (NAT), and Dynamic Host Configuration Protocol (DHCP) can minimize the loss of privacy.We will explain how the regularity of human behavior can be used to overcome these countermeasures. Once packets leave the local autonomous system (AS), they are routed through the network by the Border Gateway Protocol (BGP). The talk will discuss the unreliability of BGP and current attacks on the routing protocol. This will include an introduction to BGP injects and the PEERING testbed for BGP experimentation. One experiment we have performed uses statistical methods (CUSUM and F-test) to detect BGP injection events. We describe work we performed that applies BGP injects to Internet Protocol (IP) address randomization to replace fixed IP addresses in headers with randomized addresses. We explain the similarities and differences of this approach with virtual private networks (VPNs). Analysis of this work shows that BGP reliance on autonomous system (AS) numbers removes privacy from the concept, even though it would disable the current generation of MITM and DoS attacks. We end by presenting a compromise approach that creates software-defined data exchanges (SDX), which mix traffic randomization with VPN concepts. We contrast this approach with the Tor overlay network and provide some performance data.

The dynamic and adaptable characteristics of mobile ad hoc networks have made it a significant field for deploying various applications in wireless sensor networks. Increasing popularity of the portable devices is the main reason for the development of mobile ad hoc networks. Furthermore, the network does not require a fixed architecture and it is easy to deploy. This type of network is highly vulnerable to cyber-attacks as the nodes communicate with each other through a Wireless medium. The most critical attack in ad hoc network is jellyfish attack. In this research we have proposed a Direct Trust-based Detection Algorithm to detect and prevent jellyfish attack in MANET.

The location information of a node is one of the essential attributes used in most underwater communication routing algorithms to identify a candidate forwarding node by any of the sources. The exact location information of a node exchanged with its neighbours' in plain text and the absence of node authentication results in some of the attacks such as Sybil attack, Blackhole attack, and Wormhole attack. Moreover, the severe consequence of these attacks is Denial of Service (DoS), poor network performance, reduced network lifetime, etc. This paper proposes an anti-Spoof (a-Spoof) algorithm for mitigating localization and neighbour spoofing attacks in UASN. a-Spoof uses three pre-shared symmetric keys to share the location. Additionally, location integrity provided through the hash function. Further, the performance of a-Spoof demonstrated through its implementation in UnetStack with reference to end-to-end packet delay and the number of hops.

A Field Programmable Gate Array (FPGA) is a digital Integrated Circuit made up of interconnected functional blocks, which can be programmed by the end-user to perform required logic functions. As FPGAs are re-programmable, partially re-configurable and have lowertime to market, FPGA has become a vital component in the field of electronics. FPGAs are undergoing many security issues as the adversaries are trying to make profits by replicating the original design, without any investment. The major security issues are cloning, counterfeiting, reverse engineering, Physical tampering, and insertion of malicious components, etc. So, there is a need for security of FPGAs. A Secret key must be embedded in an IC, to provide identification and authentication to it. Physical Unclonable Functions (PUFs) can provide these secret keys, by using the physical properties of the chip. These physical properties are not reproducible even by the manufacturer. Hence the responses produced by the PUF are unique for every individual chip. The method of generating unique binary signatures helps in cryptographic key generation, digital rights management, Intellectual Property (IP) protection, IC counterfeit prevention, and device authentication. The PUFs are very promising in signature generation in the field of hardware security. In this paper, the secret binary responses is generated with the help of a delay based Ring Oscillator PUF, which does not use a clock circuit in its architecture.

With an incredible growth in MEMS and Internet, IoT has developed to an inevitable invention and resource for human needs. IoT reframes the communication and created a new way of machine to machine communication. IoT utilizes smart sensor to monitor and track environmental changes in any area of interest. The high volume of sensed information is processed, formulated and presented to the user for decision making. In this paper a model is designed to perform trust evaluation and data aggregation with confidential transmission of secured information in to the network and enables higher secure and reliable data transmission for effective analysis and decision making. The Sensors in IoT devices, senses the same information and forwards redundant data in to the network. This results in higher network congestion and causes transmission overhead. This could be control by introducing data aggregation. A gateway sensor node can act as aggregator and a forward unique information to the base station. However, when the network is adulterated with malicious node, these malicious nodes tend to injects false data in to the network. In this paper, a trust based malicious node detection technique has been introduced to isolate the malicious node from forwarding false information into the network. Simulation results proves the proposed protocol can be used to reduce malicious attack with increased throughput and performance.

Data races occur when two threads fail to use proper synchronization when accessing shared data. In kernel file systems, which are highly concurrent by design, data races are common mistakes and often wreak havoc on the users, causing inconsistent states or data losses. Prior fuzzing practices on file systems have been effective in uncovering hundreds of bugs, but they mostly focus on the sequential aspect of file system execution and do not comprehensively explore the concurrency dimension and hence, forgo the opportunity to catch data races.In this paper, we bring coverage-guided fuzzing to the concurrency dimension with three new constructs: 1) a new coverage tracking metric, alias coverage, specially designed to capture the exploration progress in the concurrency dimension; 2) an evolution algorithm for generating, mutating, and merging multi-threaded syscall sequences as inputs for concurrency fuzzing; and 3) a comprehensive lockset and happens-before modeling for kernel synchronization primitives for precise data race detection. These components are integrated into Krace, an end-to-end fuzzing framework that has discovered 23 data races in ext4, btrfs, and the VFS layer so far, and 9 are confirmed to be harmful.

Learning-enabled components (LECs) are widely used in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. However, it has been shown that LECs such as deep neural networks (DNN) are not robust and adversarial examples can cause the model to make a false prediction. The paper considers the problem of efficiently detecting adversarial examples in LECs used for regression in CPS. The proposed approach is based on inductive conformal prediction and uses a regression model based on variational autoencoder. The architecture allows to take into consideration both the input and the neural network prediction for detecting adversarial, and more generally, out-of-distribution examples. We demonstrate the method using an advanced emergency braking system implemented in an open source simulator for self-driving cars where a DNN is used to estimate the distance to an obstacle. The simulation results show that the method can effectively detect adversarial examples with a short detection delay.

Energy Internet is a typical cyber-physical system (CPS), in which the disturbance on cyber part may result in the operation risks on the physical part. In order to perform CPS assessment and research the interactive influence between cyber part and physical part, an integrated energy internet CPS model which adopts information flow matrix, energy control flow matrix and information energy hybrid flow matrix is proposed in this paper. The proposed model has a higher computational efficacy compared with simulation based approaches. Then, based on the proposed model, the influence of cyber disturbances such as data dislocation, data delay and data error on the physical part are studied. Finally, a 3 MW PET based energy internet CPS is built using PSCAD/EMTDC software. The simulation results prove the validity of the proposed model and the correctness of the interactive influence analysis.

Regardless of the setting, edge computing has drawn much attention from both the academic and industrial communities. For edge computing, content delivery networks are both a concrete and production deployable use case. While viable at the WAN or telco edge scale, it is unclear if this extends to others, such as in home WiFi routers, as has been assumed by some. In this work-in-progress, we present an initial study on the viability of using smart edge WiFi routers as a caching location. We describe the simulator we created to test this, as well as the analysis of the results obtained. We use 1 day of e-commerce web log traffic from a public data set, as well as a sampled subset of our own site - part of an ecosystem of over 111 million users. We show that in the best case scenario, smart edge routers are inappropriate for e-commerce web caching.

Caching strategies have been evaluated and compared in many studies, most often via simulation, but also in analytic methods. Knapsack solutions provide a general analytical approach for upper bounds on web caching performance. They assume objects of maximum (value/size) ratio being selected as cache content, with flexibility to define the caching value. Therefore the popularity, cost, size, time-to-live restrictions etc. per object can be included an overall caching goal, e.g., for reducing delay and/or transport path length in content delivery. The independent request model (IRM) leads to basic knapsack bounds for static optimum cache content. We show that a 2-dimensional (2D-)knapsack solution covers arbitrary request pattern, which selects dynamically changing content yielding maximum caching value for any predefined request sequence. Moreover, Belady's optimum strategy for clairvoyant caching is identified as a special case of our 2D-knapsack solution when all objects are unique. We also summarize a comprehensive picture of the demands and efficiency criteria for web caching, including updating speed and overheads. Our evaluations confirm significant performance gaps from LRU to advanced GreedyDual and score-based web caching methods and to the knapsack bounds.