Our daily lives are increasingly dependent on small, resource-limited devices that often handle sensitive information, emphasizing the need for robust security measures. However, many lightweight cryptographic algorithms compromise speed and efficiency to conserve resources. To overcome this challenge, we introduce the AxLEA cryptosystem, the first to apply approximation techniques to a reversible cryptographic system. AxLEA improves computational efficiency by mitigating carry propagation overhead through approximation. We design an invertible 32-bit approximate adder and integrate it into the round function and round key generation process. This integration reduces delay by 80% and decreases area and power consumption by 9% in rolled implementations, while unrolled implementations achieve overall 50% performance improvement. Also, the proposed design achieves a 5 \(\times\) reduction in energy consumption in both rolled and unrolled implementations, demonstrating improved efficiency over LEA across different architectural configurations. AxLEA meets critical security requirements, satisfying the avalanche effect and randomness tests specified by the NIST and ENT test suites. Additionally, it demonstrates strong resistance to linear and differential cryptanalysis. These results make AxLEA an efficient and secure solution for highly resource-constrained devices.

Research on image caption generation has predominantly focused on resource-rich languages like English, leaving resource-poor languages (like Assamese and several others) largely understudied. In this context, this paper leverages both visual and semantic attribute based features for generating captions in Assamese language. Semantic attributes refer to the significant words that represent higher-level knowledge about the image content. This work contributes through the effective use of features derived from semantic words in low resource Assamese language. The second contribution is the proposal of a Visual-Semantic Self-Attention (VSSA) module for the combination of features derived from images and semantic attributes. The VSSA module enables the image captioning model to dynamically attend to relevant regions of the image as well as the important semantic attributes, thereby leading to more contextually relevant and linguistically accurate Assamese captions. Moreover, the VSSA module is incorporated into a Transformer model to leverage the stacked attention for performance improvement. The model is trained by using both cross-entropy loss optimization and reinforcement learning approach. The effectiveness of the proposed model is evaluated through both qualitative and quantitative analyses (using BLEU-n and CIDEr metrics). The proposed model shows significant performance improvement in Assamese caption synthesis compared to previous methods, achieving 93.7% CIDEr score on the COCO-Assamese Caption (COCO-AC) dataset.

The global paradigm shift toward edge computing has led to a growing demand for efficient integrity verification. Hash functions are one-way algorithms which act as a zero-knowledge proof of a datum’s contents. However, it is infeasible to compute hashes on devices with limited processing power and memory. Hence, we propose four novel LiteHash functions which are architecturally similar to SHA-512 yet simpler. By using various approximation techniques, our implementations reduce the computational costs of digesting a message into a hash. On validating our proposed designs using the NIST PRNG Test Suite, we observe SHA-512 equivalent cryptographic security while satisfying all desired hash function property requirements. We observe a minimum of 9.41% reduction in area, 20.47% reduction in power, and 22.05% increase in throughput. Our designs offer a throughput of up to 2 Gbps while reducing area and power by a maximum of 16.86% and 32.48%, respectively. LiteHash functions also support the computation of the entire SHA-2 family of hash functions (SHA-224/256/384/512) with minor architectural modifications.

Word embeddings and Language models are the building blocks of modern Deep Neural Network-based Natural Language Processing. They are extensively explored in high-resource languages and provide state-of-the-art (SOTA) performance for a wide range of downstream tasks. Nevertheless, these word embeddings are not explored in languages such as Assamese, where resources are limited. Furthermore, there has been limited study into the performance evaluation of these word embeddings for low-resource languages in downstream tasks. In this research, we explore the current state of Assamese pre-trained word embeddings. We evaluate these embeddings’ performance on sequence labeling tasks such as Parts-of-speech and Named Entity Recognition. In order to assess the efficiency of the embeddings, experiments are performed utilizing both ensemble and individual word embedding approaches. The ensembling approach that uses three word embeddings outperforms the others. In the paper, the outcomes of the investigations are described. The results of this comparative performance evaluation may assist researchers in choosing an Assamese pre-trained word embedding for subsequent tasks.

Saurabh Kumar, Ranbir Sanasam, Sukumar Nandi. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Underwater images often suffer from color distortion, haze, and limited visibility due to light refraction and absorption in water. These challenges significantly impact autonomous underwater vehicle applications, necessitating efficient image enhancement techniques. To address these challenges, we propose a Multi-Domain Query Cascaded Transformer Network for underwater image enhancement. Our approach includes a novel Multi-Domain Query Cascaded Attention mechanism that integrates localized transmission features and global illumination features. To improve feature propagation from the encoder to the decoder, we propose a Spatio-Spectro Fusion-Based Attention Block. Additionally, we introduce a Hybrid Fourier-Spatial Up-sampling Block, which uniquely combines Fourier and spatial upsampling techniques to enhance feature resolution effectively. We evaluate our method on benchmark synthetic and real-world underwater image datasets, demonstrating its superiority through extensive ablation studies and comparative analysis. The testing code is available at: https://github.com/Mdraqibkhan/Spectroformer.

With the rapid advancement in cellular communications, there has been significant growth in the number of connected devices like smartphones, tablets, and wearables. Device-to-Device (D2D) communication is one of the emerging technologies to support future generations of Cellular Networks. It improves throughput, energy efficiency and reduces delay by allowing proximal devices to communicate directly. Device discovery is the initial and one of the crucial phases to enable D2D communication. In order to have direct communication, the devices need to find other devices in their proximity. A network-assisted discovery mechanism relying on the serving Base Station (BS) provides incomplete neighborhood information, whereas discovery, if performed by the user equipment(UE)s themselves, increases the number of discovery message exchanges in the network. In this regard, we propose a robust D2D Discovery framework that leverages the collective knowledge of neighboring Base Stations to provide complete neighborhood information. We implement and evaluate our proposed framework on the D2D module of the NS3 simulator. We demonstrate the improvement in terms of the number of discovered UEs, reduction in number of missed UEs compared to existing network-assisted discovery mechanisms, and reduced number of discovery message exchanges compared to the state-of-the-art 3GPP standards.

Multiple software and hardware intellectual property (IP) components are combined on a single chip to form Multi-Processor Systems-on-Chips (MPSoCs). Due to the rigid time-to-market constraints, some of the IPs are from outsourced third parties. Due to the supply-chain management of IP blocks being handled by unreliable third-party vendors, security has grown as a crucial design concern in the MPSoC. These IPs may get exposed to certain unwanted practises like the insertion of malicious circuits called Hardware Trojan (HT) leading to security threats and attacks, including sensitive data leakage or integrity violations. A Network-on-Chip (NoC) connects various units of an MPSoC. Since it serves as the interface between various units in an MPSoC, it has complete access to all the data flowing through the system. This makes NoC security a paramount design issue. Our research focuses on a threat model where the NoC is infiltrated by multiple HTs that can corrupt packets. Data integrity verified at the destination’s network interface (NI) triggers re-transmissions of packets if the verification results in an error. In this article, we propose an opportunistic trust-aware routing strategy that efficiently avoids HT while ensuring that the packets arrive at their destination unaltered. Experimental results demonstrate the successful movement of packets through opportunistically selected neighbours along a trust-aware path free from the HT effect. We also observe a significant reduction in the rate of packet re-transmissions and latency at the expense of incurring minimum area and power overhead.

In recent years the number of attacks on computer networks has increased exponentially due to the easy availability of sophisticated tools and attack techniques. These attacks are possible due to existing vulnerabilities in networking protocols. Most of the machine learning based intrusion detection systems proposed earlier, to mitigate these attacks, consider training a model for the group of attacks, which doesn’t consider protocol-specific properties into account and is biased toward attacks where most of the data is available. In this paper, we propose protocol aware unsupervised method based on an autoencoder-based learning approach to detect the attack in network flows by training the model using only normal traffic and using reconstruction error as the parameter to classify the attack event. Our proposed method is based on building protocol aware model by combining individual protocol-specific encoders and learning the protocol channel importance using attention mechanism. We perform various experiments on different recent datasets like CICDDoS2019, and CICIDS2018, and experimental results show that the proposed protocol aware model performs better than the non-protocol aware method.

With the advancement of Internet technologies Cyber attacks have become a significant risk to overall security, therefore, intelligent security systems are required to strengthen the network security against these threats. Machine learning has played a pivotal role in the detection and mitigation of these attacks over the years. However, to identify the zero-day attacks and incorporate frequently changing attack scenarios, techniques need to be developed that can work with minimally labeled data. In this paper, we propose Time aware LSTM Autoencoder-based learning approach to detect the attack in network flows by training the model using only normal traffic and using reconstruction error as the parameter to classify the attack event. We perform the experiments on different recent datasets like CICDDoS2019, & CICIDS2018 and experimental results exhibit that the proposed model overall provides better classification metrics.

Distributed Denial-of-Service (DDoS) attacks are widespread for Internet of Things (IoT) systems that aim to disrupt the availability of a system completely (high-rate DDoS) or partially (low-rate DDoS). Design and placement of intrusion detection systems (IDSs) for DDoS attacks on IoT systems are challenging due to the low power and lossy nature of networks. Existing IDSs are designed to handle either high-rate or low-rate DDoS but cannot handle both with good accuracy. Existing IDS placement techniques are mostly nontransparent, making malicious nodes aware of the presence of IDS nodes. Most of the IDS placement strategies are nonoptimal, making them energy inefficient. Accordingly, this work proposes a transparent, optimally placed, distributed IDS solution, namely, OPTIMIST, which can handle both high-rate and low-rate DDoS attacks with good accuracy. The placement problem is formulated as the weighted minimum vertex cover problem of a${K}$-uniform hypergraph and solved with an approximation algorithm. The IDS module is based on a long short-term memory (LSTM) model where a novel offline training method for LSTM is proposed using Wasserstein GAN-generated artificial flows. Extensive experimentation on simulation and testbed shows that the OPTIMIST can best achieve the balance between DDoS detection and energy overhead.

Ensemble system for part-of-speech (POS) tagging is beneficial for many resource-poor languages that do not have enough annotated training data to train Deep Learning (DL, also named Deep Neural Network)-based POS taggers. An Ensemble system is a better choice to incorporate the linguistic features of a language and leverage the benefits of various types of POS taggers. In this work, we present our experiment of developing an ensemble tagger for Assamese, a low-resource, morphologically rich scheduled language of India, spoken by more than 15 million people. Despite the success of modern neural-network-based models in sequence tagging tasks, it has yet to receive attention in developing tasks such as POS in a resource-poor language such as Assamese. We develop a POS tagging model based on the BiLSTM-CRF architecture with a corpus of 404k tokens. We cover several word embeddings during training. Among all the experiments, the top two POS tagging models achieve tagging F1 scores of 0.746 and 0.745. We observe that the DL-based taggers are not able to achieve decent accuracy. It may be due to the inability to capture the linguistic features of the language or due to comparatively less annotated data. So, we build another POS tagger using a rule-based approach considering several morphological phenomena of the language and get an F1 score of 0.85. Subsequently, we integrate the top two DL-based taggers with the rule-based ones and develop a new POS tagger using an ensemble approach, of which we get an improved F1 score of 0.925. Performance improvement of our new ensemble POS taggers over the baseline taggers suggests that integration of the taggers combines the qualities of all taggers in the new tagger. Therefore, this study also states ensemble taggers are more suitable for highly inflectional, morphologically rich resource-poor languages.

Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP). It is a well-studied topic in several resource-rich languages. However, the development of computational linguistic resources is still in its infancy despite the existence of numerous languages that are historically and literary rich. Assamese, an Indian scheduled language, spoken by more than 25 million people, falls under this category. In this paper, we present a Deep Learning (DL)-based POS tagger for Assamese. The development process is divided into two stages. In the first phase, several pretrained word embeddings are employed to train several tagging models. This allows us to evaluate the performance of the word embeddings in the POS tagging task. The top-performing model from the first phase is employed to annotate another set of new sentences. In the second phase, the model is trained further using the fresh dataset. Finally, we attain a tagging accuracy of 86.52 in F1 score. The model may serve as a baseline for further study on DL-based Assamese POS tagging.

With the progressive innovation of VLSI technology, Tiled Chip Multicore Processors (TCMP) have surfaced up as the backbone of the modern data intensive parallel multi-core systems. Network-on-Chip (NoC) is considered as the most preferred choice for on-chip communication. Manufacturers have begun to investigate the prospects of using third-party IP in sophisticated TCMP designs due to strict time-to-market limitations. The inflated reliance over third party IPs induced security vulnerabilities in inter-tile communication. In this paper, we implement a novel Hardware Trojan (HT) called as Delay Trojan (DT) placed in an NoC router. Proposed DT adds random delay to flits going through it, while other NoC routers merely experience regular congestion, making DT detection difficult. As a result, packets of latency-critical applications stalls impacting system performance and throughput. Further, we propose a dynamic adaptive learning framework embedded in NoC routers that detects DT with reasonable accuracy and alerts neighboring routers. We also propose a caging technique to re-route packets. Our experimental study evaluates the impact of DT and the effectiveness of the proposed solution.

Video traffic demands for vehicle infotainment services have rapidly increased in recent years. To reduce bandwidth costs and delays, content distributors (CD) use edge vehicles to distribute video contents over vehicular networks. CDs usually offer multiple subscription tiers of varying video qualities for their clients. A CD’s business goal is to make a minimal edge vehicle selection to maximize Internet traffic offloading while satisfying heterogeneous video quality requirements of clients belonging to different subscription tiers. This work proposes an optimum cost-effective solution for edge vehicle selection for delay-constrained live video streaming over vehicular networks with multi-tier bitrate agreements. The edge vehicle selection problem is formulated as the minimum set covering problem for optimum results, and a distributed approximation solution is proposed to increase scalability. Simulation results show that the proposed solution is cost-effective for CDs and saves significant control message overhead while satisfying delay constraints and multi-tier bitrate agreements.

Bodo is a scheduled Indian language spoken largely by the Bodo community of Assam and other northeastern Indian states. Due to a lack of resources, it is difficult for young languages to communicate more effectively with the rest of the world. This leads to a lack of research in low-resource languages. The creation of a dataset is a tedious and costly process, particularly for languages with no participatory research. This is more visible for languages that are young and have recently adopted standard writing scripts. In this paper, we present a methodology using Google Keep for OCR to generate a monolingual Bodo corpus from different books. In this work, a Bodo text corpus of 192,327 tokens and 32,268 unique tokens is generated using free, accessible, and daily-usable applications. Moreover, some essential characteristics of the Bodo language are discussed that are neglected by Natural Language Progressing (NLP) researchers.

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.

With the advancements in VLSI technology, Tiled Chip Multicore Processors (TCMP) with packet switched Network-on-Chip (NoC) have emerged as the backbone of the modern data intensive parallel multi-core systems. Tight time-to-market and cost constraints have forced chip manufacturers to use third-party IPs in sophisticated TCMP designs. This dependence over third party IPs has instigated security vulnerabilities in inter-tile communication that cannot be detected at manufacturing and testing phases. This includes possibility of having malicious circuits like Hardware Trojans (HT). NoC is the likely target of HT insertion due to its significance and positional advantage from system and communication standpoints. Recent research shows that HTs can manipulate control fields of NoC packets and leads to dead flit attacks that has the potential to disrupt the on-chip communication resulting in application level stalling. In this paper, we propose run time detection of such dead flit attacks by analyzing packet movement behaviours. We also propose a cost effective mitigation mechanism by re-routing the packets around the HT infected router. Our experimental study with real benchmarks on 8x8 mesh TCMP evaluates the effectiveness of the proposed solution.

Reduplication is a productive morphological process widely used in a substantial number of languages in the world. Reduplication is a well-studied phenomenon, and several typological works have provided evidence for different types of reduplication in most of the languages around the world. Addressing reduplication plays a vital role in the efficiency of POS tagger, sentiment analysis, as well as other NLP tasks. However, it is an understudied area in computational linguistics, especially in low-resource languages like Assamese. This article first describes different types of reduplication and their shapes that occur in Assamese. Second, an exhaustive set of reduplication formation rules is compiled that is incorporated to build a system to identify reduplication in Assamese text. The results of the experiments performed on three different domain datasets showed that the rule-based system can identify reduplicated expressions with an average precision, recall, and F1 scores of 94.19%, 98.07%, and 96.07%, respectively. Third, it is shown that the Assamese reduplication processes can be captured through a two-way finite-state transducer (2-way FST). Finally, two broad categories of reduplicative processes along with their corresponding 2-way FST model are presented.

The volume of video traffic as infotainment service over vehicular ad hoc network (VANET) has rapidly increased for past few years. Providing video streaming as VANET infotainment service is very challenging because of high mobility and heterogeneity of vehicular networks. While the number of mobile gateways (vehicles connected to the Internet) needs to be minimized to reduce service cost, the streaming quality also needs to be satisfactory for end-users. Existing works either focus on gateway minimization or focus on enhancing user satisfaction. We propose a video streaming solution, namely, DGSVS, which does gateway minimization with the constrained time data delivery to end-users. We formulate our constrained gateway minimization problem as minimum set covering (MSC) problem and solve with a distributed approximation method for MSC. We assume that only a subset of vehicles in VANET run DGSVS application. Therefore, instead of application layer cooperation for gateway-client association, network layer cooperation is proposed. We propose a novel multicast protocol DSS-CAST for this purpose, which is specialized in streaming data distribution for dynamic scenarios. We compare the performance of DGSVS with other existing protocols and found that DGSVS is most effective in service cost minimization while it is able to achieve competitive QoE performance.

Internet of Things (IoT) is a way to communicate with the real world without much human involvement. It is booming in today's computing world, with billions of devices having sensors and actuators connected to the internet using various low power technologies. Despite several profits, it experiences multiple security threats that impel catastrophic crashes in the IEEE 802.15.4e (6TiSCH) network. Various threats like jamming attacks, DDoS, abnormal behavior, etc., are detected using multiple Machine Learning (ML) and Deep Learning (DL) approaches. In this paper, an edge-based ML enables Intrusion Detection Systems (IDS) is proposed to detect distributed denial-of-service (DDoS) attack patterns from a particular source. Experimental outcomes confirm that the proposed approach is scalable and efficient in terms of computation and storage. Hence, the intended approach gives a faster response as (24.2- 68.9) Sec. The average memory utilization (ROM/RAM), energy usage, and accuracy achieved by our intended solution are 35834B/5378B, 85916mJ, 98.7%, respectively, which outperform closely related work.

With the advancement of VLSI technology, Tiled Chip Multicore Processors (TCMP) with packet switched Network-on-Chip (NoC) have been emerged as the backbone of the modern data intensive parallel systems. Due to tight time-to-market constraints, manufacturers are exploring the possibility of integrating several third-party Intellectual Property (IP) cores in their TCMP designs. Presence of malicious Hardware Trojan (HT) in the NoC routers can adversely affect communication between tiles leading to degradation of overall system performance. In this paper, we model an HT mounted on the input buffers of NoC routers that can alter the destination address field of selected NoC packets. We study the impact of such HTs and analyse its first and second order impacts at the core level, cache level, and NoC level both quantitatively and qualitatively. Our experimental study shows that the proposed HT can bring application to a complete halt by stalling instruction issue and can significantly impact the miss penalty of L1 caches. The impact of re-transmission techniques in the context of HT impacted packets getting discarded is also studied. We also expose the unrealistic assumptions and unacceptable latency overheads of existing mitigation techniques for packet header attacks and emphasise the need for alternative cost effective HT management techniques for the same.

Constrained Application Protocol (CoAP), an IETF-standardized web transfer protocol, is specially designed to facilitate resource-constrained devices and low power, lossy networks. The steep rise in extensive deployment of Internet of Things (IoT) technology, of late, makes it necessary for web services to be used over the internet by constrained wireless nodes for a majority of Machine-to-Machine (M2M) applications. However, considering that the system is constrained, ensuring security is a challenge when employing CoAP since it uses User Datagram Protocol (UDP), which is unrealiable and lacks handshaking mechanism. A malicious endpoint with read and write access can severly jeopardize service availability by launching an IP address spoofed attack. Moreover, it can help mount other complex Distributed Denial-of-Service (DDoS) attacks like amplification attacks. Though response spoofing vulnerability has been addressed in the research community for quite some time now, request spoofing countermeasures are relatively few. In this paper, we propose an attacker identification scheme using a Discrete Event System (DES) based Intrusion Detection System (IDS). Through our scheme, we not only detect request and response spoofing attack in CoAP, but also identify the adversarial node by analysing the LoWPAN event dynamics generated due to probe responses. Correctness of our approach is guaranteed by construction of a DES diagnoser. We have tested our approach on Contiki OS. The experimental results show our proposed scheme to be energy efficient with response time of approx 6 sec at 99.2% accuracy.

In the Internet of Vehicles (IoV), vehicles communicate wirelessly with other vehicles, sensors, pedestrians, and roadside units. IoV is aimed at improving road safety, driving comfort, and traffic efficiency. However, IoV is exposed to a range of threats to security and privacy. The presence of dishonest and misbehaving peers in the system is of a major concern, which may put lives in danger. Thus, establishing trust among these probable untrusted vehicles is one of the most significant challenges of such a network. The critical pitfalls of existing and traditional mechanisms are scalability, a single point of failure, maintaining the quality of service, verification, and revocation and dealing with sparsity, consistency, availability, efficiency, robustness, privacy concerns are some of the biggest challenges to be addressed. Blockchain technology, with its great success in applications like cryptocurrencies and smart contracts, is considered as one of the potential candidates to build trust in IoV. In this paper, we propose a blockchain-based decentralized trust management scheme using smart contracts. Specifically, we introduce the concept of blockchain sharding for reducing the load on the main blockchain and increasing the transaction throughput. Our proposal has two key contributions: blockchain to maintain and update reliable and consistent trust values across the network and incentive scheme to encourage peers to perform well. We also conduct extensive experiments, which demonstrate the implementation feasibility of proposed mechanisms in the real world.

With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design where power grid networks are designed in order to provide adequate power to all the underlying functional blocks. Power planning also requires multiple iterative steps to create the power grid network while satisfying the allowed worst-case IR drop and Electromigration (EM) margin. For the first time, this paper introduces Deep learning (DL)-based framework to approximately predict the initial design of the power grid network, considering different reliability constraints. The proposed framework reduces many iterative design steps and speeds up the total design cycle. Neural Network-based multi-target regression technique is used to create the DL model. Feature extraction is done, and training dataset is generated from the floorplans of some of the power grid designs extracted from IBM processor. The DL model is trained using the generated dataset. The proposed DL-based framework is validated using a new set of power grid specifications (obtained by perturbing the designs used in the training phase). The results show that the predicted power grid design is closer to the original design with minimal prediction error (~2%). The proposed DL- based approach also improves the design cycle time with a speedup of ~6x for standard power grid benchmarks.

Named Data Networking is a promising Future Internet Architecture, where consumers fetch contents from the network using unique names. It has in-built security features like data integrity, and data origin authentication as contents are signed by the original producer. Though, NDN's stateful forwarding plane helps to remove some of the existing DDoS attacks in the current IP architecture, still it can face one specific kind of attack called Interest flooding attack (IFA). In this paper, we focus on one variant of IFA where malicious consumers and malicious producers collaboratively launch an attack to degrade the QoS of legitimate consumers. Here we have shown how attackers can intelligently fix the Interest sending rate so that attack goes undetected easily. Simulation results demonstrate that with proper placement of malicious consumers and producers, the attackers can successfully launch the attack with moderate traffic and less number of malicious consumers.

Multicast communication in IoT is prevalent in many applications like smart lighting, firmware update etc. In such applications a single multicast message is sent to all the group members. The group members respond with unicast messages. Multicasting saves energy, decreases network traffic by reducing the number of messages in the network. Security in multicasting is of the utmost importance due to its deployment in many sensitive applications and inherent properties of IoT network. In the literature, various solutions have been proposed to secure group communication. However, there is still no suitable approach that satisfies the secure multicasting need of IoT. In this paper we propose a DTLS (Datagram Transport Layer Security) based secure group communication scheme. The proposed scheme is lightweight, scalable, and robust against member compromise. Moreover, the proposed scheme authenticates each group member and is also suitable for dynamic groups. The simulation results prove that the proposed scheme is more suitable for secure IoT framework in terms of energy and memory requirement than other related schemes.

Internet of Things (IoT) platforms use a large number of low-cost resource constrained devices and generates millions of short-flows. In-network processing is gaining popularity day by day to handle IoT applications and services. However, traditional software-defined networking (SDN) based management systems are not suitable to handle the plug and play nature of such systems. In this paper, we propose Aloe, an auto-scalable SDN orchestration framework. Aloe exploits in-network processing framework by using multiple lightweight controller instances in place of service grade SDN controller applications. The proposed framework ensures the availability and significant reduction in flow-setup delay by deploying instances in the vicinity the resource constraint IoT devices dynamically. Aloe supports fault-tolerance with recovery from network partitioning by employing self-stabilizing placement of migration capable controller instances. Aloe also provides resource reservation for micro-controllers so that they can ensure the quality of services (QoS). The performance of the proposed system is measured by using an in-house testbed along with a large scale deployment in Amazon Web services (AWS) cloud platform. The experimental results from these two testbeds show significant improvement in response time for standard IoT based services. This improvement of performance is due to the reduction in flow-setup time. We found that Aloe can improve flow-setup time by around 10%-30% in comparison to one of the states of the art orchestration framework.

With the advancement of technology nodes, Electromigration (EM) signoff has become increasingly difficult, which requires a considerable amount of time for an incremental change in the power grid (PG) network design in a chip. The traditional Black’s empirical equation and Blech’s criterion are still used for EM assessment, which is a time-consuming process. In this article, for the first time, we propose a machine learning (ML) approach to obtain the EM-aware aging prediction of the PG network. We use neural network--based regression as our core ML technique to instantly predict the lifetime of a perturbed PG network. The performance and accuracy of the proposed model using neural network are compared with the well-known standard regression models. We also propose a new failure criterion based on which the EM-aging prediction is done. Potential EM-affected metal segments of the PG network is detected by using a logistic-regression--based classification ML technique. Experiments on different standard PG benchmarks show a significant speedup for our ML model compared to the state-of-the-art models. The predicted value of MTTF for different PG benchmarks using our approach is also better than some of the state-of-the-art MTTF prediction models and comparable to the other accurate models.

Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users’ opinions and attempt to understand (i) changing characteristics of users’ emotions toward a social issue over time, (ii) influence of public emotions on individuals’ emotions, (iii) cause of changing opinion by social factors, and so on. We study users’ emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.

Named Data Networking (NDN) is a newly-emerging future Internet Architecture which focuses on content rather than hosts. It changes the communication model from `delivering content to a specific host' to `fetching content by using names'. One of the major components in NDN is Pending Interest Table (PIT). Its unique design brings the following advantages in NDN: anonymity, loop-free forwarding, multipath delivery, and multicast. One of the key challenges in NDN is choosing the appropriate PIT size. Current NFD (NDN Forwarding Daemon) does not limit the PIT size. Over-allocation of the PIT does not help in improving network performance. So, PIT needs to have a limited size. However, under bursty traffic, the PIT may become full. In this paper, we present a novel PIT replacement policy called EQPR. It evicts the existing entry based on two factors: Priority and Estimated Round Trip Time (RTT), aiming towards achieving Quality of Service for the valuable users who pay an additional cost for better service. We evaluate EQPR using ndnSIM simulator and compare the results with basic NDN under moderate to high traffic. Simulation results show that EQPR maintains a similar overall satisfaction rate as Basic NDN and most importantly it provides 99.7% Interest satisfaction rate for valuable users.

Management of networked Internet of Things (IoT) infrastructure with in-network processing capabilities is becoming increasingly difficult due to the volatility of the system with low-cost resource-constraint devices. Traditional software-defined networking (SDN) based management systems are not suitable to handle the plug and play nature of such systems. Therefore, in this paper, we propose Aloe, an elastically auto-scalable SDN orchestration framework. Instead of using service grade SDN controller applications, Aloe uses multiple lightweight controller instances to exploit the capabilities of in-network processing infrastructure. The proposed framework ensures the availability and significant reduction in flow-setup delay by deploying instances near the resource constraint IoT devices dynamically. Aloe supports fault-tolerance and can recover from network partitioning by employing self-stabilizing placement of migration capable controller instances. The performance of the proposed system is measured by using an in-house testbed along with a large scale deployment in Amazon web services (AWS) cloud platform. The experimental results from these two testbed show significant improvement in response time for standard IoT based services. This improvement of performance is due to the reduction in flow-setup time. We found that Aloe can improve flow-setup time by around 10%-30% in comparison to one of the state of the art orchestration framework.

Improving Quality of Experience for video streaming remains a challenge as the Internet consists of numerous shared links which turn into shared bottlenecks due to lack of Quality of Service measures or inconsistent QoS policies between ISPs. Solutions to this have come up involving Dynamic Adaptive Streaming over HTTP (DASH) (that adapts its playback bitrate according to available throughput) with Multipath TCP (that uses multiple network interface paths to increase throughput and reliability) as transport protocol. Even then, DASH QoE can degrade as MPTCP subflows between same end-system pairs can overlap on shared bottleneck links. To reduce this degradation, we need to understand the behaviour of DASH on MPTCP in that scenario. While the current MPTCP literature has extensive coverage on shared bottleneck links in congestion control design and bandwidth usage fairness, the same is not true for DASH QoE. We rectify this shortcoming by presenting a detailed comparative performance evaluation between DASH on MPTCP and DASH on TCP in presence of a shared bottleneck link in terms of objective QoE parameters under various video segment sizes with the results from a topology having independent links as a reference. There are two major outcomes of this experimental evaluation. First, the optimal video segment size for best QoE is a value large enough to incur least network overhead while being small enough to not congest the shared bottleneck. Second, it is verified that the objective QoE parameters have strong correlation to network resource allocation and ISPs can use them to improve real-time video streaming on their network.

With the increasing number of vehicles on the road, the insurance market for vehicular insurance is also increasing. There has been a massive proliferation in the number of policy taken by the drivers over the year. The traditional vehicular insurance processes used by the insurance companies rely on analyzing the history of the behaviors of the drivers for deciding the suitable premium amount to be paid by the vehicles. Usage-Based Insurance (UBI), which is based on telematics, turns out to be a modern and effective approach for providing insurance to the vehicles. Unlike the traditional approach, the premiums in the UBI are calculated based on the current behavior of the drivers. Moreover, there exists a lack of transparency in the processing of the claims, which not only results in delays of receiving the claims but also leads to a number of frauds. Decentralized technology such as blockchain turns out to be an effective solution for the problems mentioned above. In this work, we propose a blockchain-based framework for vehicular UBI and incentives in ITS. We demonstrate and analyze the feasibility of our work with proper experimental testbed setup.

With the emerging advancements in VANETs, preserving location privacy has become a vital requirement. Most of the privacy schemes focus on changing the pseudonyms after a certain interval of time or in some special scenarios (can be mix-context or mix-zone) to avoid linking messages. Due to pseudonym linking attacks, elementary methods of pseudonym changing are found inefficient to provide desirable protection or privacy. The spatiotemporal information contained in beacons makes vehicles traceable, and the driver's privacy is breached. In this paper, we propose a mechanism CCAPS, which is a combination of two previously proposed schemes CAPS and CPN. This scheme takes the advantages of these two mix-context schemes and combines them. We evaluate this scheme against a global multi-target tracking adversary using PREXT simulator, and we compare the performance of proposed mechanism against the CAPS and CPN schemes.

Over the past decade, several approximate adders have been proposed in the literature based on the design concept ofEqual Segment Adder(ESA). In this approach, an$N$-bit adder is segmented into several smaller and independent equally sized accurate sub-adders. An$N$-bit ESA has two primary design parameters: (i) Segment size ($k$), which represents the maximum length of carry propagation; and (ii) Overlapping bits ($l$), which represents the minimum number of bits used in carry prediction, where$1 \leq k < N$and$0 \leq l < k$. Based on the combinations of$k$and$l$, an$N$-bit ESA has$N(N-1)/2$possible configurations. In this paper, we analyse ESAs and propose analytical models to estimate accuracy, delay, power and area of ESAs. The key features of the proposed analytical models are that: (i) They are generalized, i.e., work for all possible configurations of an$N$-bit ESA; and (ii) They are superior (i.e., estimate more accurately) or at par to the existing analytical models. From the proposed analytical models, we observe that in an$N$-bit ESA, there exist multiple (more than one) configurations which exhibit similar accuracy. However, these configurations exhibit different delay, power and area. Therefore, for a given accuracy, the configurations which provide minimal delay, power and/or area need to be known apriori for efficient, intelligent and goal oriented implementations of ESAs. In this regard, we present an optimization framework that exploits the proposed analytical models to find the optimal configurations of an$N$-bit ESA. Further, we know that accuracy of an ESA does not depend on the adder architecture used to implement it, however, its delay, power and area depend significantly. Consequently, the optimal configurations vary with adder architectures used to implement the ESA. In order to cover a wide range of adders, we consider three types of adder architecture in our analysis: (i) Architectures having smaller area ($O(N)$); (ii) Architectures having smaller delay ($O(log_2N)$); and (iii) Architectures having in-between delay ($O(N/4)$) and area ($O(2N)$).

The need for Internet-based services is increasing at a tremendous pace in smart cities. The driver and occupants of the vehicle access Internet, and different intelligent transportation system (ITS) related services such as real-time traffic information, parking space availability, downloading the map, etc., in a vehicle to infrastructure communication (V2I) mode. In a highly dynamic network environment like vehicular network, software-defined networking (SDN) promises to be an ideal solution. However, it also opens doors for various distributed denial of service (DDoS) attacks. An attacker can easily flood short-lived spoofed flows and exhaust network resources. This motivates us to find a solution to detect the attacks in a V2I communication under SDN. In this paper, we propose a machine learning (ML) based DDoS attack detection. The proposed system uses various ML schemes, and few of them found to be accurate with a high detection rate and a relatively low false alarm rate.

The next big innovation in the automotive industry is connected and automated vehicles (CAVs). CAVs would allow a higher throughput in the roads and also lead to fewer accidents. Currently, Level 4 autonomy has been achieved in the labs, and active research is being done to achieve full autonomy (Level 5). To improve road safety, traffic and many other driving scenarios, communication between vehicles and infrastructure are key. However, connectivity between Autonomous vehicles also opens doors for cyber-attacks. In this study, we use VENTOS, a simulation platform based on SUMO and OMNET++ to model various attack on the cooperative driving use case cooperative adaptive cruise control based platooning and show their impacts. Our simulation-based study shows that various attacks exploiting the communication between vehicles can cause significant problems related to safety and stability in the platoon.

The primary objective behind the development of Multipath TCP (MPTCP) is to aggregate throughput by creating multiple sub-flows via different network interfaces. A difference in end-to-end path characteristics for the sub-flows may generate out of order segments, causing head of line (HOL) blocking at the receiver. An intelligent selection of a subset of the available sub-flows can reduce the number of out of order segments; thus sub-flow selection can enhance the performance of MPTCP. In this paper, we first propose a Markov model for the performance of MPTCP in terms of end-to-end sub-flow characteristics. Based on the theoretical model, we present an optimization framework for active sub-flow selection by exploiting the controller functionalities over a software defined network (SDN) architecture. Finally, experimental results are obtained to demonstrate performance improvements of MPTCP in terms of aggregated throughput.

The concept of approximate computing, that is, to sacrifice computation quality for computation efforts, has recently emerged as a promising design approach. Over the past decade, several research works have explored approximate computing at both the software level and hardware level of abstraction with encouraging results. At the hardware level of abstraction, adders (being the fundamental and most widely used data operators in digital systems) have attracted a significant attention for approximation. In this article, we first explain briefly the need/significance of approximate adders. We then propose four Approximate Full Adders (AFAs) for high-performance energy-efficient approximate computing. The key design objective behind the proposed AFAs is to curtail the length of carry propagation subjected to minimal error rate. Next, we exploit one of the proposed AFAs (optimal one) to construct an N -bit approximate adder that hereinafter is referred as “ApproxADD.” An emergent property of ApproxADD is that carries do not propagate in it, and, consequently, it provides bit-width-aware constant delay ( O (1)). ApproxADD also provides improvement in dynamic power consumption by 46.31% and in area by 28.57% w.r.t. Ripple Carry Adder (RCA), which exhibits the lowest power and area. Although ApproxADD provides a significant improvement in delay, power, and area, it may not be preferred for some of the error-resilient applications because its: (i) Error Distance (ED) is too high; and (ii) Error Rate (ER) increases rapidly with bit-width ( N ). To improve ED and ER, we exploit the concept of carry-lifetime and Error Detection and Correction logic, respectively. In this way, we introduce two more (improved) versions of ApproxADD--ApproxADD υ 1 and ApproxADD. We call these as ApproxADD υ 1 and ApproxADD υ 2 with existing approximate adders based on conventional design metrics and approximate computing design metrics. Furthermore, to inspect effectiveness of the proposed approach in real-life applications, we demonstrate image compression and decompression by replacing the conventional addition operations in Discrete Cosine Transform (DCT) and Inverse Discrete Cosine Transform (IDCT) modules with ApproxADD υ 2.

The current developments of software defined networking (SDN) paradigm provide a flexible architecture for network control and management, in the cost of deploying new hardwares by replacing the existing routing infrastructure. Further, the centralized controller architecture of SDN makes the network prone to single point failure and creates performance bottleneck. To avoid these issues and to support network manageability over the existing network infrastructure, we develop Flipper in this paper, that uses only software augmentation to convert existing off-the-shelf routers to network policy design and enforcement points (PDEP). We develop a distributed self-stabilized architecture for dynamic role change of network devices from routers to PDEPs, and make the architecture fault-tolerant. The performance of Flipper has been analyzed from both simulation over synthetic networks, and emulation over real network protocol stacks, and we observe that Flipper is scalable, flexible and fail-safe that can significantly boost up the manageability of existing network infrastructure.

This poster provides an in-depth analysis of the primary path effect in Multi-path TCP using thorough experimentation over a realistic network setup. We observe the impact of various primary path parameters, like bandwidth, delay and loss, over the end-to-end performance. It is shown that under certain circumstances overall network performance can be improved by more than 50% with proper primary path selection. This study may drive the research community towards the design of new segment scheduling algorithms considering the effect of primary path selection over Multi-path TCP.

Rapid evolution of E-world demands delay, power and area optimized digital circuits/systems while still meeting the security requirements of the cryptography applications. Cryptographic hash functions (which are considered the workhorse of security layers) provide compressive and non-invertible outputs. This signifies that approximate implementation of cryptographic hash functions can provide improvements in delay, power and area without considerable change in security level. In this paper, we first examine likelihood of infusing approximation in cryptographic hash functions and then propose a methodology to evaluate the effects of approximation. Further, we demonstrate four approximate pipelined implementations of Secure Hash Algorithm 1 (SHA-1). Our simulation results show that the proposed approximate pipelined SHA-1s provide significant improvements in delay, power and area with negligible change in security level.

Existing studies on evolution of social network largely focus on addition of new nodes and links in the network. However, as network evolves, existing relationships degrade and break down, and some nodes go to hibernation or decide not to participate in any kind of activities in the network where it belongs. Such nodes and links, which we refer as "dull", may affect analysis and prediction tasks in networks. This paper formally defines the problem of predicting dull nodes and links at an early stage, and proposes a novel time aware method to solve it. Pruning of such nodes and links is framed as "network data cleaning" task. As the definitions of dull node and link are non-trivial and subjective, a novel scheme to label such nodes and links is also proposed here. Experimental results on two real network datasets demonstrate that the proposed method accurately predicts potential dull nodes and links. This paper further experimentally validates the need for data cleaning by investigating its effect on the well-known "link prediction" problem.

The wireless sensor network WSN is more vulnerable than the wired network because it is relatively easy for an adversary to eavesdrop, insert, alter, and intercept the message communicated in the network. Sensor nodes have very limited power and memory. While designing the secure communication architectures, the main goal is to design and memory efficient protocols. Most of the well-known communication architectures like TinySec, LLSP, and MiniSec compromise with the security issues because of and memory constraints of sensor nodes. In WSN, communication consumes more than computation. By minimizing communication overhead, the consumption can be reduced. We propose a new energy efficient secure communication called EESCA for WSN, to make the communication secure. Along with authentication, confidentiality and integrity, the proposed architecture provides strong replay protection, and data freshness as permutation and substitution are used to generate massage authentication code. EESCA is robust against heavy packet loss because reset or resynchronization of counter is not required. The proposed scheme is efficient as the security packet overhead is reduced by 1i¾źbyte over the best-known secure communication architectures. The efficacy of EESCA is shown through theoretical and experimental analysis. Copyright © 2016 John Wiley & Sons, Ltd.

This paper proposes an opportunistic approach to mitigating the hidden and exposed node problem in a high-throughput mesh network, by exploiting the frame aggregation and block acknowledgment (BACK) capabilities of IEEE 802.11n/ac wireless networking standard. Hidden nodes significantly drop down the throughput of a wireless mesh network by increasing data loss due to collision, whereas exposed nodes cause under-utilization of the achievable network capacity. The problem becomes worse in IEEE 802.11n/ac supported high-throughput mesh networks, due to the large physical layer frame size and prolonged channel reservation from frame aggregation. The proposed approach uses the standard carrier sense multiple access (CSMA) technology along with an opportunistic collision avoidance (OCA) method that blocks the communication for hidden nodes and opportunistically allows exposed nodes to communicate with the peers. The performance of the proposed CSMA/OCA mechanism for high throughput mesh networks is studied using the results from an IEEE 802.11n+s wireless mesh networking testbed, and the scalability of the scheme has been analyzed using simulation results.

Media Access Layer (MAC) vulnerabilities are the primary reason for the existence of the significant number of Denial of Service (DoS) attacks in 802.11 Wi-Fi networks. In this paper we focus on the de-authentication DoS (Deauth-DoS) attack in Wi-Fi networks. In Deauth-DoS attack an attacker sends a large number of spoofed de-authentication frames to the client (s) resulting in their disconnection. Existing solutions to mitigate Deauth-DoS attack rely on encryption, protocol modifications, 802.11 standard up gradation, software and hardware upgrades which are costly. In this paper we propose a Machine Learning (ML) based Intrusion Detection System (IDS) to detect the Deauth-DoS attack in Wi-Fi network which does not suffer from these drawbacks. To the best of our knowledge ML based techniques have never been used for detection of Deauth-DoS attack. We have used a variety of ML based classifiers for detection of Deauth-DoS attack enabling an administrator to choose among a host of classification algorithms. Experiments performed on in-house test bed shows that the proposed ML based IDS detects Deauth-DoS attack with precision (accuracy) and recall (detection rate) exceeding 96% mark.

IEEE 802.11s mesh networking standard supports Mesh Coordinated Channel Access (MCCA) to provide better quality of service (QoS) through channel reservation during the MAC layer channel access. According to the current QoS specifications, network traffic can be broadly classified into four classes-voice, video, background and best effort. However, MCCA does not directly support the standard service differentiation that is essential for service level QoS assurance. Further, assuring fairness among the flows of similar service classes is required for effective bandwidth utilization. Providing service differentiation along with the fairness is challenging in a distributed environment due to their non-linearity and non-additive properties. This paper uses the concept of (α, p)-proportional fairness to design a distributed method for providing service differentiation with minimum fairness guarantee. An admission control mechanism is designed over standard mesh protocols to manage the minimum service guarantee for existing flows in the network. The effectiveness of the proposed scheme is analyzed using experimental results from an IEEE 802.11n+s mesh networking testbed. The scalability and performance bound of the proposed scheme is further analyzed using simulation results.

Current deployment of wireless community networks and wireless municipal services utilizes multi-hop backbone mesh network technology to provide ubiquitous Internet connectivity to the end users. IEEE 802.11s Wireless Mesh Network (WMN) is a promising technology to increase spatial reuse in a mesh backbone using high gain directional antennas. Uses of directional communication in a multi-interface mesh network introduce the problem of unbalanced traffic allocation among the end-to-end flows that results in inefficient channel access using the standard EDCA mechanism. Furthermore, the forwarding protocol should coordinate with the channel access to improve the network performance. In this paper, a localized distributed mechanism is proposed to share the channel bandwidth effectively among interfering interfaces based on the solution of the balanced traffic allocation problem. The standard forwarding algorithm is augmented to use the channel access information effectively in a dynamic network scenario. The performance of the proposed scheme is evaluated through the results obtained from a practical indoor IEEE 802.11n+s directional multi-interface mesh testbed.

The IEEE 802.11 standard for wireless local area networks defines a power management algorithm for Independent Basic Service Set (IBSS) allowing it to save critical battery energy in low powered wireless devices. The power management algorithm for IBSS uses beacon intervals (BIs) as the time unit, where every BI consists of an Announcement Traffic Indication Message (ATIM) window and a data window. The stations that have data to send need to go through a handshaking procedure in the ATIM window. If this handshaking is successful, the station remains awake in the data window and participates in the data communication. Otherwise, it goes into the sleep mode. This paper presents an analytical model to compute the throughput, expected delay and expected power consumption in an IEEE 802.11 IBSS in power save mode (PSM) for different traffic conditions in the network. The impact of data arrival rate, network size, and size of the BI on the performance of the IEEE 802.11 DCF in PSM is also analyzed. This analysis reveals a clear trade-off among throughput, delay, and average power consumption. The trade-off analysis is useful for designing efficient power consumption algorithms while maintaining the consistence performance of the network in terms of throughput and delay.

Wireless sensor networks have grown significant attentions among researchers for providing a flexible and low-cost framework to design an architecture for Intelligent Transport Systems. The inherent challenges in distribution and management of sensor networks along the road require an application-specific protocol support for the network connectivity, the sensing coverage, the reliable data forwarding, and the network lifetime improvement. This paper introduces the concept of k-strip length coverage along the road, which ensures a better sensing coverage for the detection of moving vehicles compared with the conventional barrier coverage and full area coverage, in terms of the availability of sufficient information for statistical processing and the number of sensors required to be active. To extend the network lifetime, every sensor follows a sleep-wakeup schedule maintaining the network connectivity and the k-strip length coverage. This scheduling problem is modeled as a graph optimization, the NP-hardness of which motivates to design a centralized heuristic, providing an approximate solution. As a sensor network is inherently distributed in nature, properties of the centralized heuristic are explored to design a per-node solution based on local information. Performance of the proposed scheme is analyzed through simulation results.

IEEE 802.11 Community wireless hotspots are widely used to provide ubiquitous Internet connections to the end-users in public areas such as airports and restaurants. The recent analysis of the traffic pattern in a wireless hotspot shows more APs are deployed in a public area than required, though the users visit only a few APs. As a consequence, severe load imbalance is observed in a hotspot local area network (LAN), that results in performance degradation in terms of quality of service (QoS) for the network and quality of experience (QoE) for the end-users. This paper proposes a set of bandwidth management policies to achieve this goal, and the effectiveness of these policies is analyzed theoretically. According to the theoretical foundation, a context aware handover management scheme for proper load distribution in a public IEEE 802.11 network, supporting the class-aware bandwidth management policies, is designed in this paper. The performance of the proposed scheme is evaluated using an IEEE 802.11g+e wireless LAN testbed, and compared with other schemes proposed in the literature.

Data gathering or converge cast is one of the most popular applications of road side sensor network where the data sensed from the road are accumulated in the road side gateways or sinks for traffic monitoring purpose. The required delay sensitivity and reliability of the application as well as the scarcity of sensor resources make the task challenging. In this paper, a novel tree based data gathering scheme has been proposed exploiting the strip like structure of the road network. Sensor nodes are distributed in several virtual blocks along the road and a converge cast tree is constructed selecting one active node from each block. Implementation of efficient scheduling assures both the coverage and critical power savings of sensor nodes. The network connectivity is guaranteed throughout by the proposed tree maintenance module that handles the sensor node joining and leaving events. Simulation results show that the tree maintenance overhead in terms of both delay and control message communication is nominal.

One of the challenging issues in video watermarking is robustness against collusion attacks. To resist collusion attack, the same video scene should carry the same watermark, whenever and wherever it appears in the video. Inter-frame correlation is more within a short video neighborhood when no scene change is detected. Motion coherency has recently been recognized as a desirable property for watermarks to resist temporal frame averaging attacks. To the best of our knowledge, motion coherent watermarking in compressed domain has not yet been well explored. In this paper, a compressed domain technique has been proposed to detect motion coherent regions within a short video neighborhood. The time complexity of the proposed method is discussed. Simulation results evaluate the effectiveness of the proposed method.

Sensor networks deployed for the border area monitoring requires a high degree of reliability for the data gathering in spite of any arbitrary node or sink failures. This paper proposes RelBAS, a robust data gathering scheme specially designed for the border area network to provide a guaranteed delivery of sensory data. The proposed protocol aims to find out multiple node-disjoint paths to multiple sinks so that the disconnectivity in one path due to a node failure does not disrupt the delivery of data to the sink. The forwarding path selection at every node in RelBAS is based on the combination of three parameters - the hop-count, the residual energy and the number of children for for parent of the corresponding tree. This helps in adapting the protocol to the application requirement depending on the delay, energy efficiency and data aggregation. Moreover, RelBAS is capable of detecting an affected zone due to multiple node failures. The effectiveness of the proposed scheme has been analyzed using the simulation results.

The lifetime of sensor network depends on the efficient utilization of resource-constrained sensor nodes. Several MAC protocols like DMAC and its variants have been proposed to save critical sensor resources through sleep-wakeup scheduling over data gathering tree. For applications where data aggregation is not possible, the sleep duration decreases gradually from the leaves to the root of the data gathering tree. This results early failure of sensor nodes near the sink, and affects network connectivity and coverage. Deploying redundant sensors can solve this problem where a faulty node is replaced by a redundant node to maintain network connectivity and coverage. However, the amount of redundancy depends on the node failure pattern, and thus more number of redundant nodes required to be deployed near the sink. This paper proposes a gradient based sensor deployment scheme for energy-efficient data gathering exploring the trade-off among connectivity, coverage, fault-tolerance and redundancy. The density of deployment is estimated based on the distance of a node from the sink while dealing with connectivity, coverage and fault-tolerance. The effectiveness of the proposed scheme has been analyzed both theoretically and with the help of simulation.

IEEE 802.11s provides Hybrid Wireless Mesh Protocol (HWMP) to find out the forwarding path in a mesh network based on mesh peering and MAC layer scheduling information. However, both proactive and reactive modes of HWMP perform poorly for multi-radio mesh network because of inefficient radio selection, time-varying channel conditions and interference among the radios. This paper proposes an improved opportunistic path selection protocol over HWMP for multi-radio support that goes beyond the traditional routing mechanisms, operates either in proactive, reactive or hybrid mode. The efficiency of the proposed scheme is analyzed using simulation results.

ABSTRACT Anomaly based intrusion detection systems (IDSs) create a benign behavior profile of the network, and any deviation from this profile is considered as an attack. Many of the algorithms proposed in the literature for anomaly IDS fall into cluster analysis category. As networks become faster in operation, the amount of data that needs to be analyzed becomes huge. Many clustering techniques require more than one pass on the dataset; thus, when used as anomaly IDSs, these algorithms becomes computationally expensive and cannot work for such high‐speed networks. To handle voluminous data, anomaly IDS schemes have been proposed that use data summarization techniques. Data summarization techniques found in the literature suffer from false alarms due to improper clustering when used as anomaly IDS. In this paper, an anomaly IDS is proposed that is capable of handling large dataset yet minimizing false alarms. Copyright © 2012 John Wiley & Sons, Ltd.

Single channel multi-interface IEEE 802.11s Wireless Mesh Network(WMN) is a promising technology for increasing spatial reuse of wireless channel using high gain directional antennas. Use of single channel in WMN is advantageous for providing different services (like community mesh networking, vehicular mesh networking etc.) by different frequency channels so that several wireless networking services can co-exist. However, for effective use of multi-interface multi-beam directional antennas in single channel environment, proper scheduling of interfaces and prioritization among different beams are required to minimize channel interference. In this paper a distributed mechanism is proposed to share bandwidth effectively among different interfaces in a probabilistic way based on local communication and interference information. A beam prioritization mechanism is used based on IEEE 802.11s EDCA to minimize under-use or overuse of channel bandwidth by a directional beam and maximize concurrent packet transmission. Simulation result shows that the proposed scheme improves efficiency of the network over standard IEEE 802.11s EDCA based MAC protocol.

Event driven data gathering or convergecast through sensor nodes requires efficient and correct delivery of data at the sink. A tree rooted at the sink is an ideal topology for data gathering which utilizes sensor resources properly. Resource constrained sensor nodes are highly prone to sudden crash. A set of algorithms, proposed in this paper, builds a data gathering tree rooted at the sink. The tree eventually becomes a Breadth First Search (BFS) tree where each node maintains the shortest distance in hop-count to the root to reduce the routing delay and power consumption. The data gathering tree is repaired locally within a constant round of message transmissions after any random node fails. Simulation result shows that the repairing delay is very less in average, and the proposed scheme can repair from arbitrary node failure using constant number of message passing.

Neighbor Discovery Protocol (NDP) is one of the core protocol in IPv6 network. It provides facilities like Stateless Address Autoconfiguration (SLAAC), Neighbor unreachability Detection (NUD), address resolution (similar to ARP in IPv4) etc. Due to lack of authorization in NDP messages, many attacks like Neighbor Solicitation (NS) spoofing, Neighbor Advertisement (NA) spoofing, Man-in-the-Middle (MiTM), Denial-of- Service (DoS) etc. are possible. The attack detection mechanism proposed in this paper is based on two different schemes, passive monitoring scheme and active detection mechanism using probing. In the proposed scheme, we build state of the network using Multicast Listener Discovery (MLD) queries and validate captured packets with this state. This allows us to detect attacks almost instantaneously and reduce the network traffic induced by IDS as compared to Active Probing scheme and at the same time retain its high detection rate.

In recent years, the number of attacks in computer networks are constantly increasing due to the lack of proper authentication of communicating entities in the network. TCP/IP layering architecture is prone to various threats due to the vulnerabilities in each of its layers. This mandates the requirement for a suitable detection system in the network to monitor the possible attacks. ICMP is a mandatory protocol which provides the error reporting, control and network management functionalities to the Internet Protocol (IP). Many of the attacks in the network like MiTM and DoS can be initiated with the exploitation of this essential protocols. In this paper, an active detection mechanism to identify many ICMP Error messages based attacks is proposed. The ICMP messages are verified by sending suitable probe packets to the hosts and validating their responses. The detection scheme is successfully validated in a testbed with various attack scenarios and the results show the effectiveness of the proposed technique in terms of greater accuracy in the detection rates.

Wireless Mesh Network is a promising technology to construct backbone of vehicular network where multi-path communication can be used effectively to communicate between vehicular clients and Internet gateways. The performance of such network can be increased using high-gain smart antennas and beam-forming technology. The mesh access points use multi-interface multi-beam smart antenna technology to communicate with vehicular clients and neighboring access points. MAC layer channel access and scheduling is an important issue in this network architecture such that the interference is minimized among different communications. In this paper, a dynamic TDMA channel access mechanism based on HiperLAN/2 is designed to improve network capacity using efficient beam-scheduling. Simulation result shows the efficiency of the proposed scheme in terms of network throughput, end-to-end fairness and cumulative packet transmission ratio.

Tuple Space based Mobile Middleware (TSMM) is developed to tackle emerging dynamics in underlying infrastructure. Its coordination medium, viz. tuple space model, has multiple inherent decoupled behaviors to coordinate interactions between different agents of supported applications. This paper focuses on one such decoupling quality, synchronization decoupling, by which agents are decoupled from their invoked primitives. This decoupling ability is accomplished by including reactivity in tuple space model. Reactive tuple space model also enables development of robust and flexible applications for TSMM. However, in existing TSMM, this decoupling behavior has not been fully attained, as reactions are themselves coupled with invoked primitives, leading to additional network and processing overheads. This paper proposes an improved reactive tuple space model for TSMM with enhanced decoupling behavior of reactions to achieve robustness for supported applications. Proposed tuple space model is also formally modeled and verified by notation and proof logic of UNITY model.

Address Resolution Protocol (ARP) based attacks are caused by compromised hosts in the LAN and mainly involve spoofing with falsified IP-MAC pairs. Since ARP is a stateless protocol such attacks are possible. Neither there are signatures available for these attacks nor any significant statistical behavior change can be observed. So existing signature or anomaly intrusion detection systems are unable to detect these type of attacks. Several schemes have been proposed in the literature to circumvent these attacks, however, these techniques either make IP-MAC pairing static, modify the existing ARP, violate network layering architecture etc. In this paper a host based Discrete Event System (DES) approach is proposed for detecting ARP spoofing attacks. This approach does not require any extra constraint like static IP-MAC, changing the ARP or violation of network layering architecture.

With the increase in number of hosts in the Internet, there is also a rise in the demand for IP address space. To cater to this issue, IP version 6 (IPv6) succeeded IPv4. Compared to 32 bit IP address space in IPv4, IP address in IPv6 is composed of 128 bits. In IPv4, when a host wants to communicate with another host in an LAN, it needs to know the MAC address of the target host, which was possible through Address Resolution Protocol (ARP). As ARP is stateless and due to lack of authorization in ARP messages, many attacks like request spoofing, response spoofing, Man-in-the-Middle (MiTM), Denial-of- Service (DoS) etc. are possible. IPv6 uses Network Discovery Protocol (NDP) to find the MAC address. NDP is also stateless and lacks authentication of its messages by default. So NDP also suffers from many attacks similar to ARP. Although there are various attack detection and prevention mechanisms available for ARP attacks, they are not yet implemented for NDP (IPv6). In this paper we propose an attack detection mechanism for neighbor solicitation spoofing and neighbor advertisement spoofing.

A network Intrusion Detection System (IDS) is a device or software that monitors network activities and raises alerts on detection of malicious behavior. State-transition based framework like Finite State Machines (FSM), extended FSM, timed FSM, Discrete Event Systems (DES) etc. are widely used in network IDSs because the framework enables formal modeling, analysis, verification etc. The attack detection capability in these IDSs is based on passive monitoring of sequence of events with the assumption that intrusions lead to change in the sequence (which needs to be detected). However, there are certain attacks like ARP spoofing, Internet Control Message Protocol (ICMP) error message based attacks etc. for which passive monitoring schemes have several limitations because in such attacks there is no change in sequence of events. IDSs with active probing are now being proposed for such attacks which involve sending of probe packets that cause difference in sequence of events under attack condition and can be then detected using passive monitoring. In this paper we propose an IDS to detect ARP spoofing attacks using active state-transition framework called “active DES”.

ABSTRACT Intrusion Detection Systems (IDSs) are used to find the security violations in computer networks. Usually IDSs produce a vast number of alarms that include a large percentage of false alarms. One of the main reason for such false alarm generation is that, in most cases IDSs are run with default set of signatures. In this paper, a scheme for network specific false alarm reduction in IDS is proposed. A threat profile of the network is created and IDS generated alarms are correlated using neural network. Experiments conducted in a test bed have successfully filtered out most of the false alarms for a range of attacks yet maintaining theDetection Rate. Copyright © 2010 John Wiley & Sons, Ltd.

Application based intrusion detection involves analysis of network packet payload data. Recently statistical methods for analyzing the payload are being used. Since behavior of every application is not same a different model is necessary for each application. Studies have revealed that higher order n-grams are good for capturing the network profile. In this paper we introduce a concept of layered version of n-gram for payload based anomaly network intrusion detection. Each layer works as an independent anomaly detection system. A packet is declared as normal after passing through all the layers. A packet is declared as anomalous if at any layer it is declared as anomalous and we stop further processing the packet. We create a set of bins and equally distribute the distinct n-grams to each bin. Each such n-gram is a 2 tulle where the first element is byte values of the n-gram and second is the frequency of gram in the entire training data. We assign an anomaly score to each bin based on the frequency of the individual gram in the bin and is termed as coverage of the bin.We evaluate the proposed scheme on normal traffic of DARLA 99 dataset mixed with a set of attacks. Experimental results shows the efficacy of the method with a false alarm rate as low as 0.001\%.

Wireless sensor networks aim at a special kind of ad hoc networks, exposing an energy-constrained distributed computing environment. Proposed protocols have tried to incorporate power management schemas of the likes of reduced duty cycles and active synchronization. However this has led to a significant loss in the latency of data delivery. In this work we have introduced low latency medium access control (LLM), as a mechanism to deliver data with low latency without compromising the energy efficiency of the network. To this end we exploit the data aggregating properties of a sensor network and introduce a pre-notification packet to keep potential forwarding nodes aware of a forthcoming data packet. Our simulations show that LLM does perform as per expectations

Existing protocols for medium access in wireless sensor networks opt for staggered wakeup scheduling among clusters of nodes. However intra-cluster contention and interference has remained an unaddressed issue. In this work we propose PELLMAC: power efficient and low latency MAC for wireless sensor networks, a schema that addresses node scheduling for channel access. By limiting contention between adjacent branches of the data gathering tree, we are able to reduce the latency of the network, as well as optimize energy utilization. Simulation results, in accordance with our claims, show that PELLMAC enhances performance in energy savings, latency and delivery ratio

Broadcasting is one of the essential communication models of MANET. Many MANET multicast routing protocols rely heavily upon MAC layer's broadcast support. However, the broadcast mechanism of the standard IEEE 802.11 cannot provide reliable broadcasting service, and therefore, degrades the multicasting performance. In this paper, we propose an extension to the IEEE 802.11 broadcast mechanism, called round-robin acknowledge and retransmit (RRAR), to improve the reliability. Different from other mechanisms, we employ a simple and effective DATA/BrACK (broadcast acknowledgment) mechanism. Receiving the BrACK, the DATA sender calculates and records the indices of the frames that are lost and retransmits them. The proposed scheme provides a highly reliable broadcast service to the routing layer, and the performance of multicast routing protocols can be improved significantly.

Achieving MAC layer fairness in wireless ad-hoc networks is a very challenging issue. We first show that IEEE 802.11 exhibits substantial short-term unfairness due to the randomness in the binary exponential back-off (BEB), the freezing mechanism of the back-off timer, and the concealed information problem (introduced in this paper). To achieve short-term fairness, we propose a novel fairness scheme, called adaptive transmission control (ATC). The basic idea of ATC is to make the senders to contend for the shared medium in a cooperative and adaptive manner by exploiting the overheard information. The ATC includes three mechanisms: CW-tuning, early-reset, and receiver-coordination, which are able to tackle the underlying problems that cause short-term unfairness in IEEE 802.11. Specifically, the CW-tuning mechanism reflects the past usage of the medium among the nodes, and thus greatly reduces the randomness in BEB, while the early-reset mechanism largely prevents the short-term unfairness caused by the freezing mechanism. At last, the receiver-coordination mechanism copes effectively with the concealed information problem by using information available at the receiver side. Extensive simulation results show that the proposed ATC substantially improves the short-term fairness without unduly degrading the throughput.

Broadcasting is one of the essential communication models of MANETs. Many MANET multicast routing protocols rely heavily upon MAC layer's broadcast support. However, the broadcast mechanism of the standard IEEE 802.11 cannot provide reliable broadcasting service. In this paper, we improve the IEEE 802.11 broadcast mechanism's reliability by introducing the adaptive round-robin acknowledge and retransmit (ARAR) scheme. Different from the other mechanisms, ARAR is able to adapt to the traffic load by adjusting the length of a frame transmitted. For short frames, a simple and effective DATA/BrACK (broadcast acknowledgment) scenario is applied; for long frames, RTS/CTS/DATA/BrACK exchange is utilized. Receiving the BrACK, the DATA sender calculates and records the indices of the frames that need retransmission and performs the retransmission when it is possible. The proposed scheme provides a highly reliable broadcast service to the routing layer, and the performance of multicast routing protocols can be improved significantly. This is shown through simulations, in which we have compared ARAR to the standard IEEE 802.11 approach using ODMRP [Y. Yi et al., Nov. 2002]. Our future work attempts to compare ARAR with some existing works.

Information and infrastructure security is a serious issue of global concern. As the last line of defense for security infrastructure, intrusion detection techniques are paid more and more attention. In this paper, one anomaly-based intrusion detection technique (ScanAID: Statistical ChAracteristics of N-grams for Anomaly-based Intrusion Detection) is proposed to detect intrusive behaviors in a computer system. The statistical properties in sequences of system calls are abstracted to model the normal behaviors of a privileged process, in which the model is characterized by a vector of anomaly values of N-grams. With a reasonable definition of efficiency parameter, the length of an N-gram and the size of the training dataset are optimized to get an efficient and compact model. Then, with the optimal modeling parameters, the flexibility and efficiency of the model are evaluated by the ROC curves. Our experimental results show that the proposed statistical anomaly detection technique is promising and deserves further research (such as applying it to network environments).

This paper presents a parallel algorithm for computing the Voronoi diagram for collision-free path planning of robots. The algorithm constructs the Voronoi diagram on a binary image of the workspace and is based on the Euclidean distance function. It has been implemented in ANSI-C on an HP workstation. The algorithm is suitable for VLSI implementation in a cellular architecture. Such an architecture is described. Comparisons with an existing algorithm are also presented.

This paper presents a new algorithm to extract the skeleton and its Euclidean distance values from a binary image. A VLSI implementation of the algorithm in a locally connected cellular array is also given. The algorithm runs in O(n) time for an image of size n/spl times/n. The extracted skeleton reconstructs the objects in the image exactly.

This reply emphasizes the point that the regular, modular, cascadable structure of local neighborhood CA can be employed for building low cost cipher system hardware. This cost effective engineering solution can achieve desired level of security with larger size CA.

Considerable interest has been recently generated in the study of Cellular Automata (CA) behavior. Polynomial and matrix algebraic tools are employed to characterize some of the properties of null/periodic boundary CA. Some other results of group CA behavior have been reported based on simulation studies. This paper reports a formal proof for the conjecture-there exists no primitive characteristic polynomial of 90/150 CA with periodic boundary condition. For generation of high quality pseudorandom patterns, it is necessary to employ CA having primitive characteristic polynomial. There exist two null boundary CA for every primitive polynomial. However, for such Cs the quality of pseudorandomness suffers in general, particularly in the regions around the terminal cells because of null boundary condition. In this background, a new concept of intermediate boundary CA has been proposed to generate pseudorandom patterns that are better in quality than those generated with null boundary CA. Some interesting properties of intermediate boundary CA are also reported.

Characterization of a special class of nongroup CA termed as D1*CA has been proposed previously (1993) along with its application for synthesis of easily testable FSM. This paper extends application of the D1*CA as an ideal test machine for testing combinational logic (CL) blocks and registers of a circuit. Such a test machine can be conveniently embedded in the data path synthesis phase around the function realized by a CL block and the register feeding the input data to the CL. In the normal mode of operation, the register and the CL realize the intended function. During testing, the D1*CA runs in autonomous mode generating the test vectors and also accumulating test responses. It is sufficient to observe the response only from the leftmost CA cell with aliasing error probability approaching zero value. Experiments conducted on CL benchmarks confirm 100% fault coverage of all stuck-at faults in CL and its associated lines. It does not incur any test generation and test application overheads. Further, test parallelism can be achieved through simultaneous testing of multiple combinational modules in a chip. The scheme provides a cost effective alternative to scan path.

This paper deals with the theory and application of Cellular Automata (CA) for a class of block ciphers and stream ciphers. Based on CA state transitions certain fundamental transformations are defined which are block ciphering functions of the proposed enciphering scheme, These fundamental transformations are found to generate the simple (alternating) group of even permutations which in turn is a subgroup of the permutation group, These functions are implemented with a class of programmable cellular automata (PCA) built around rules 51, 153, and 195. Further, high quality pseudorandom pattern generators built around rule 90 and 150 programmable cellular automata with a rule selector (i.e., combining function) has been proposed as running key generators in stream ciphers, Both the schemes provide better security against different types of attacks. With a simple, regular, modular and cascadable structure of CA, hardware implementation of such schemes idealy suit VLSI implementation.>