Biblio

The research in MANET has been carried out for the development of various techniques which will increase the competency of the network only. A plenty number of proposed routing protocols are magnificent in terms of efficiency. However, proposed protocols were generally fulfilling the set of trusted network and not considered for adversarial network setting, hence there is no security mechanism has been considered. MANET is widely used in sensitive fields like battlefield, police rescue operation and many more in such type of sensitive field an attacker may try to gather information about the conversation starting from the origin node to the terminal node. Secure route selection approach for route selection in adverse environment is discussed in this article. The results shows that proposed algorithm, will resolve the single & collaborative attack by increasing the computational & storage overhead and by improving the significant PDR, achieves a noticeable enhancement in the end to end delay.

Mobile Adhoc networks are wireless adhoc networks that have property of self organizing, less infrastructure, multi hoping, which are designed to work under low power vulnerable environment. Due to its very unique characteristics, there is much chances of threat of malicious nodes within the network. Blackhole attack is a menace in MANETs which redirects all traffic to itself and drops it. This paper’s objective is to analyze the effects of blackhole attack under reactive routing protocol such as Adhoc on Demand Distance Vector routing (AODV). The performance of this protocol is assessed to find the vulnerability of attack and also compared the impact of attack on both AODV, AODV with blackhole and proposed AODV protocols. The analysis is done by simulated using NS- 2.35 and QoS parameters such as Throughput, PDR, and Average Energy Consumed are measured further.

Low power and lossy network (LLN) comprising of constrained devices like sensors and RFIDs, is a major component in the Internet of Things (IoT) environment as these devices provide global connectivity to physical devices or “Things”. LLNs are tied to the Internet or any High Performance Computing environment via an adaptation layer called 6LoWPAN (IPv6 over Low power Personal Area Network). The routing protocol used by 6LoWPAN is RPL (IPv6 Routing Protocol over LLN). Like many other routing protocols, RPL is susceptible to blackhole attacks which cause topological isolation for a subset of nodes in the LLN. A malicious node instigating the blackhole attack drops received packets from nodes in its subtree which it is supposed to forward. Thus, the malicious node successfully isolates nodes in its subtree from the rest of the network. In this paper, we propose an algorithm based on the concept of exponential smoothing to detect the topological isolation of nodes due to blackhole attack. Exponential smoothing is a technique for smoothing time series data using the exponential window function and is used for short, medium and long term forecasting. In our proposed algorithm, exponential smoothing is used to estimate the next arrival time of packets at the sink node from every other node in the LLN. Using this estimation, the algorithm is designed to identify the malicious nodes instigating blackhole attack in real time.

The QoS performance of MANET routing protocols is significantly affected by the mobility conditions in network. Secondly, as MANET open nature network, there is strong possibility of different types of vulnerabilities such as blackhole attack, malicious attack, DoS attacks etc. In this research work, we are designing the novel opportunistic routing protocol in order to address the challenges of network security as well as QoS improvement. There two algorithms designed in this paper. First we proposed and designed novel QoS improvement algorithm based on optimization scheme called Ant Colony Optimization (ACO) with swarm intelligence approach. This proposed method used the RSSI measurements to determine the distance between two mobile nodes in order to select efficient path for communication. This new routing protocol is named as QoS Mobility Aware ACO (QMAA) Routing Protocol. Second, we designed security algorithm for secure communication and user's authentication in MANET under the presence attackers in network. With security algorithm the QoS aware protocol is proposed named as Secure-QMAA (SQMAA). The SQMAA achieved secure communications while guaranteed QoS performance against existing routing protocols. The simulation results shows that under the presence of malicious attackers, the performance of SQMAA are efficient as compared to QMAA and state-of-art routing protocol.

Mobile Ad-hoc Networks (MANETs) have gained popularity both in research and in industrial fields. This is due to their ad hoc nature, easy deployment thanks to the lack of fixed infrastructure, self-organization of its components, dynamic topologies and the absence of any central authority for routing. However, MANETs suffer from several vulnerabilities such as battery power, limited memory space, and physical protection of network nodes. In addition, MANETs are sensitive to various attacks that threaten network security like Blackhole attack in its different implementation (single and multiple). In this article, we present the simulation results of single and multiple Blackhole attack in AODV and OLSR protocols on using NS-3.27 simulator. In this simulation, we took into consideration the density of the network described by the number of nodes included in the network, the speed of the nodes, the mobility model and even we chose the IEEE 802.11ac protocol for the pbysicallayer, in order to have a simulation, which deals with more general and more real scenarios. To be able to evaluate the impact of the attack on the network, the Packet delivery rate, Routing overhead, Throughput and Average End to End delay have been chosen as metrics for performance evaluation.

We propose a novel application of coded computing to the problem of the nearest neighbor estimation using MatDot Codes (Fahim et al., Allerton'17) that are known to be optimal for matrix multiplication in terms of recovery threshold under storage constraints. In approximate nearest neighbor algorithms, it is common to construct efficient in-memory indexes to improve query response time. One such strategy is Multiple Random Projection Trees (MRPT), which reduces the set of candidate points over which Euclidean distance calculations are performed. However, this may result in a high memory footprint and possibly paging penalties for large or high-dimensional data. Here we propose two techniques to parallelize MRPT that exploit data and model parallelism respectively by dividing both the data storage and the computation efforts among different nodes in a distributed computing cluster. This is especially critical when a single compute node cannot hold the complete dataset in memory. We also propose a novel coded computation strategy based on MatDot codes for the model-parallel architecture that, in a straggler-prone environment, achieves the storage-optimal recovery threshold, i.e., the number of nodes that are required to serve a query. We experimentally demonstrate that, in the absence of straggling, our distributed approaches require less query time than execution on a single processing node, providing near-linear speedups with respect to the number of worker nodes. Our experiments on real systems with simulated straggling, we also show that in a straggler-prone environment, our strategy achieves a faster query execution than the uncoded strategy.

Nowadays, with such a huge amount of information available online, one key challenge is how to retrieve target data efficiently. A recent state-of-art solution, k-means hashing (KMH), codes data via a string of binary code obtained by iterative k-means clustering and binary code optimizing. To deal with high dimensional data, KMH divides the space into low-dimensional subspaces, places a hypercube in each subspace and finds its proper location by the mentioned optimizing process. However, the complexity of the optimization increases rapidly when the dimension of the hypercube increases. To address this issue, we propose an improved hashing method stacked k-means hashing (SKMH). The main idea is to increase the approximation by a coarse-to-fine multi-layer lower-dimensional cubes. With these kinds of lower-dimensional cubes, SKMH can achieve a similar approximation ability via a less optimizing time, compared with KMH method using higher-dimensional cubes. Extensive experiments have been conducted on two public databases, demonstrating the performance of our method by some common metrics in fast nearest neighbor search.

Cloud computing has become an important and popular infrastructure for data storage and sharing. Typically, data owners outsource their massive data to a public cloud that will provide search services to authorized data users. With privacy concerns, the valuable outsourced data cannot be exposed directly, and should be encrypted before outsourcing to the public cloud. In this paper, we focus on k-Nearest Neighbor (k-NN) search over encrypted data. We propose efficient and secure k-NN search schemes based on matrix similarity to achieve efficient and secure query services in public cloud. In our basic scheme, we construct the traces of two diagonal multiplication matrices to denote the Euclidean distance of two data points, and perform secure k-NN search by comparing traces of corresponding similar matrices. In our enhanced scheme, we strengthen the security property by decomposing matrices based on our basic scheme. Security analysis shows that our schemes protect the data privacy and query privacy under attacking with different levels of background knowledge. Experimental evaluations show that both schemes are efficient in terms of computation complexity as well as computational cost.

This paper introduces a biologically inspired associative data model and structure for finding nearest neighbors and similar patterns. The method can be used as an alternative to the classical approaches to accelerate the search for such patterns using various priorities for attributes according to the Sebestyen measure. The presented structure, together with algorithms developed in this paper can be useful in various computational intelligence tasks like pattern matching, recognition, clustering, classification, multi-criterion search etc. This approach is particularly useful for the on-line operation of associative neural network graphs. Graphs that dynamically develop their structure during learning on training data. The results of experiments show that the associative approach can substantially accelerate the nearest neighbor search and that associative structures can also be used as a model for KNN tasks. Finally, this paper presents how the associative structures can be used to self-organize data and represent knowledge about them in the associative way, which yields new search approaches described in this paper.

Continuous growth in sensor data and other temporal sequence data necessitates efficient retrieval and similarity search support on these big time series datasets. However, finding exact similarity results, especially at the granularity of subsequences, is known to be prohibitively costly for large data sets. In this paper, we thus propose an efficient framework for solving this exact subsequence similarity match problem, called TINN (TIme series Nearest Neighbor search). Exploiting the range interval diversity properties of time series datasets, TINN captures similarity at two levels of abstraction, namely, relationships among subsequences within each long time series and relationships across distinct time series in the data set. These relationships are compactly organized in an augmented relationship graph model, with the former relationships encoded in similarity vectors at TINN nodes and the later captured by augmented edge types in the TINN Graph. Query processing strategy deploy novel pruning techniques on the TINN Graph, including node skipping, vertical and horizontal pruning, to significantly reduce the number of time series as well as subsequences to be explored. Comprehensive experiments on synthetic and real world time series data demonstrate that our TINN model consistently outperforms state-of-the-art approaches while still guaranteeing to retrieve exact matches.

Reconnaissance might be the longest phase, sometimes take weeks or months. The black hat makes use of passive information gathering techniques. Once the attacker has sufficient statistics, then the attacker starts the technique of scanning perimeter and internal network devices seeking out open ports and related services. In this paper we are showing traffic accountability and time to complete the specific task during reconnaissance phase active scanning with nmap tool and proposed strategies that how to deal with large volumes of hosts and conserve network traffic as well as time of the specific task.

A main goal of the paper is to discuss the world telecommunications strategy in transition to the IP world. The paper discuss the shifting from circuit switching to packet switching in telecommunications and show the main obstacle is excessive software. As a case, we are passing through the three generations of American military communications: (1) implementation of signaling protocol SS7 and Advanced Intelligent Network, (2) transformation from SS7 to IP protocol and, finally, (3) the extremely ambitious cybersecurity issues. We use the newer unclassified open Defense Information Systems Agency documents, particularly: Department of Defense Information Enterprise Architecture; Unified Capabilities the Army. We discuss the newer US Government Accountability Office (2018) report on military equipment cyber vulnerabilities.

Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.

Once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. Release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using RV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIRA and Rally can be enhanced to include suggested algorithms for managing release planning in Agile.

Phishing attacks are one of the most common and least defended security threats today. We present an approach which uses natural language processing techniques to analyze text and detect inappropriate statements which are indicative of phishing attacks. Our approach is novel compared to previous work because it focuses on the natural language text contained in the attack, performing semantic analysis of the text to detect malicious intent. To demonstrate the effectiveness of our approach, we have evaluated it using a large benchmark set of phishing emails.

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.

As Multi-Processor Systems-on-Chip (MPSoCs) permeate the Internet by powering IoT devices, they are exposed to new threats. One major threat is Denial-of-Service (DoS) attacks, which make communication services slow or even unavailable. While mainly studied on desktop and server systems, some DoS attacks on mobile devices and Network-on-Chip (NoC) platforms have also been considered. In the context of NoC-based MPSoC architectures, previous works have explored flooding DoS attacks and their countermeasures, however, these protection techniques are ineffective to mitigate new DoS attacks. Recently, a shift of the network attack paradigm from flooding DoS to Low-and-Slow DoS has been observed. To this end, we present two contributions. First, we demonstrate, for the first time, the impact of Low-and-Slow DoS attacks in NoC environments. Second, we propose a lightweight online monitor able to detect and mitigate these attacks. Results show that our countermeasure is feasible and that it effectively mitigates this new attack. Moreover, since the monitors are placed at the entry points of the network, both, single- and multi-source attacks can be neutralized.

In the Network on Chip (NoC), performance optimization has always been a research focus. Compared with the static routing scheme, dynamical routing schemes can better reduce the data of packet transmission latency under network congestion. In this paper, we propose a dynamical Q-learning routing approach with real-time monitoring of NoC. Firstly, we design a real-time monitoring scheme and the corresponding circuits to record the status of traffic congestion for NoC. Secondly, we propose a novel method of Q-learning. This method finds an optimal path based on the lowest traffic congestion. Finally, we dynamically redistribute network tasks to increase the packet transmission speed and balance the traffic load. Compared with the C-XY routing and DyXY routing, our method achieved improvement in terms of 25.6%-49.5% and 22.9%-43.8%.

Since the performance of bus interconnects does not scale with the number of processors connected to the bus, chip multiprocessors make use of on-chip networks that implement packet switching and virtual channel flow control to efficiently transport data. In this paper, we consider the test and fault-tolerance aspects of such a network-on-chip (NoC). Past work in this area has addressed the communication efficiency and deadlock-free properties in NoC, but when routing externally received data, aspects of security must be addressed. A malicious denial-of-service attack or a power virus can be launched by a malicious external agent. We propose a two-tier solution to this problem, where a local self-test manager in each processing element runs test algorithms to detect faults in local processing element and its associated physical and virtual channels. At the global level, the health of the NoC is tested using a sorting-based algorithm proposed in this paper. Similarly, we propose to handle fault-tolerance and security concerns in routing at two levels. At the local level, each node is capable of fault-tolerant routing by deflecting packets to an alternate path; when doing so, since a chance of deadlock may be created, the local router must be capable of guestimating a deadlock situation, switch to packet-switching instead of flit-switching and attempt to reroute the packet. At the global level, a routing agent plays the role of gathering fault data and provide the fault-information to nodes that seek this information periodically. Similarly, the agent is capable of detecting malformed packets coming from an external source and prevent injecting such packets into the network, thereby conserving the network bandwidth. The agent also attempts to guess attempts at denial-of-service attacks and power viruses and will reject packets. Use of a two-tier approach helps in keeping the IP modular and reduces their complexity, thereby making them easier to verify.

Globalization of semiconductor design and manufacturing has led to several hardware security issues. The problem of Hardware Trojans (HT) is one such security issue discussed widely in industry and academia. Adversary design engineer can insert the HT to leak confidential data, cause a denial of service attack or any other intention specific to the design. HT in cryptographic modules and processors are widely discussed. HT in Multi-Processor System on Chips (MPSoC) are also catastrophic, as most of the military applications use MPSoCs. Network on Chips (NoC) are standard communication infrastructure in modern day MPSoC. In this paper, we present a novel hardware Trojan which is capable of inducing performance degradation and denial of service attacks in a NoC. The presence of the Hardware Trojan in a NoC can compromise the crucial details of packets communicated through NoC. The proposed Trojan is triggered by a particular complex bit pattern from input messages and tries to mislead the packets away from the destined addresses. A mitigation method based on bit shuffling mechanism inside the router with a key directly extracted from input message is proposed to limit the adverse effects of the Trojan. The performance of a 4×4 NoC is evaluated under uniform traffic with the proposed Trojan and mitigation method. Simulation results show that the proposed mitigation scheme is useful in limiting the malicious effect of hardware Trojan.

Multi-Processor Systems-on-Chips (MPSoCs) are a platform for a wide variety of applications and use-cases. The high on-chip connectivity, the programming flexibility, and the reuse of IPs, however, also introduce security concerns. Problems arise when applications with different trust and protection levels share resources of the MPSoC, such as processing units, cache memories and the Network-on-Chip (NoC) communication structure. If a program gets compromised, an adversary can observe the use of these resources and infer (potentially secret) information from other applications. In this work, we explore the cache-based attack by Bogdanov et al., which infers the cache activity of a target program through timing measurements and exploits collisions that occur when the same cache location is accessed for different program inputs. We implement this differential cache-collision attack on the MPSoC Glass and introduce an optimized variant of it, the Earthquake Attack, which leverages the NoC-based communication to increase attack efficiency. Our results show that Earthquake performs well under different cache line and MPSoC configurations, illustrating that cache-collision attacks are considerable threats on MPSoCs.

pubcrawl, Network on Chip Security, Scalability, resiliency, resilience, metrics, Vulnerabilities and design flaws in Network-on-Chip (NoC) routers can be exploited in order to spy, modify and constraint the sensitive communication inside the Multi-Processors Systems-on-Chip (MPSoCs). Although previous works address the NoC threat, finding secure and efficient solutions to verify the security is still a challenge. In this work, we propose for the first time a method to formally verify the correctness and the security properties of a NoC router in order to provide the proper communication functionality and to avoid NoC attacks. We present a generalized verification flow that proves a wide set of implementation-independent security-related properties to hold. We employ unbounded model checking techniques to account for the highly-sequential behaviour of the NoC systems. The evaluation results demonstrate the feasibility of our approach by presenting verification results of six different NoC routing architectures demonstrating the vulnerabilities of each design.

In this paper we investigate deceptive defense strategies for web servers. Web servers are widely exploited resources in the modern cyber threat landscape. Often these servers are exposed in the Internet and accessible for a broad range of valid as well as malicious users. Common security strategies like firewalls are not sufficient to protect web servers. Deception based Information Security enables a large set of counter measures to decrease the efficiency of intrusions. In this work we depict several techniques out of the reconnaissance process of an attacker. We match these with deceptive counter measures. All proposed measures are implemented in an experimental web server with deceptive counter measure abilities. We also conducted an experiment with honeytokens and evaluated delay strategies against automated scanner tools.

We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients over the Internet. We study a proactive moving-target defense to thwart a DDoS attacker's reconnaissance phase and consequently reduce the attack's impact. The defense is effectively a moving-target (motag) technique in which the proxies dynamically change. The system is evaluated using an AWS prototype of HTTP redirection and by numerical evaluations of an “adversarial” coupon-collector mathematical model, the latter allowing larger-scale extrapolations.

Cyber reconnaissance is the process of gathering information about a target network for the purpose of compromising systems within that network. Network-based deception has emerged as a promising approach to disrupt attackers' reconnaissance efforts. However, limited work has been done so far on measuring the effectiveness of network-based deception. Furthermore, given that Software-Defined Networking (SDN) facilitates cyber deception by allowing network traffic to be modified and injected on-the-fly, understanding the effectiveness of employing different cyber deception strategies is critical. In this paper, we present a model to study the reconnaissance surface of a network and model the process of gathering information by attackers as interactions with a cyber defensive system that may use deception. To capture the evolution of the attackers' knowledge during reconnaissance, we design a belief system that is updated by using a Bayesian inference method. For the proposed model, we present two metrics based on KL-divergence to quantify the effectiveness of network deception. We tested the model and the two metrics by conducting experiments with a simulated attacker in an SDN-based deception system. The results of the experiments match our expectations, providing support for the model and proposed metrics.