Biblio

Psychologists, economists, and human-factors people in addition to computer scientists need to be working on improving cybersecurity as the frequency and sophistication of cyberattacks grows. Cybersecurity professionals call for the exploration of behavioral science and economics in regard to cybercriminals and victims. The discovery of weakness in user behavior could leaked to the discovery of vulnerabilities among cybercriminals. 

The recent analysis indicates more than 250,000 people in the United States of America (USA) die every year because of medical errors. World Health Organisation (WHO) reports states that 2.6 million deaths occur due to medical and its prescription errors. Many of the errors related to the wrong drug/dosage administration by caregivers to patients due to indecipherable handwritings, drug interactions, confusing drug names, etc. The espousal of Mobile-based speech recognition applications will eliminate the errors. This allows physicians to narrate the prescription instead of writing. The application can be accessed through smartphones and can be used easily by everyone. An application program interface has been created for handling requests. Natural language processing is used to read text, interpret and determine the important words for generating prescriptions. The patient data is stored and used according to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) guidelines. The SMS4-BSK encryption scheme is used to provide the data transmission securely over Wireless LAN.

Data security is the process of protecting data from loss, alteration, or unauthorised access during its entire lifecycle. It includes everything from the policies and practices of a company to the hardware, software, storage, and user devices used by that company. Data security tools and technology increase transparency into an organization's data and its usage. These tools can protect data by employing methods including encryption and data masking personally identifiable information.. Additionally, the method aids businesses in streamlining their auditing operations and adhering to the increasingly strict data protection rules.

Vehicular Ad hoc Network (VANET) is an emerging technology that is used to provide communication between vehicle users. VANET provides communication between one vehicle node to another vehicle node, vehicle to the roadside unit, vehicle to pedestrian, and even vehicle to rail users. Communication between nodes should be very secure and confidential, Since VANET communicates through wireless mode, a malicious node may enter inside the communication zone to hack, inject false messages, and interrupt the communication. A strong protocol is necessary to detect malicious nodes and authenticate the VANET user to protect them from malicious attacks. In this paper, a fuzzy-based trust authentication scheme is used to detect malicious nodes with the Mamdani fuzzy Inference system. The parameter estimation, rules have been framed using MATLAB Mamdani Fuzzy Inference system to select a genuine node for data transmission.

The cloud infrastructure is not new to the society from past one decade. But even in recent time, the companies started migrating from local services to cloud services for better connectivity and for other requirements, this is due to companies financial limitations on existing infrastructure, they are migrating to less cost and hire and fire support based cloud infrastructures. But the proposed cloud infrastructure require security on event logs accessed by different end users on the cloud environment. To adopt the security on local services to cloud service based infrastructure, it need better identify management between end users. Therefore this paper presents the related works of user identity as a service for each user involving in cloud service and the accessing permission and protection will be monitored and controlled by the cloud security infrastructures.

Image encryption is an essential part of a Visual Cryptography. Existing traditional sequential encryption techniques are infeasible to real-time applications. High-performance reformulations of such methods are increasingly growing over the last decade. These reformulations proved better performances over their sequential counterparts. A rotational encryption scheme encrypts the images in such a way that the decryption is possible with the rotated encrypted images. A parallel rotational encryption technique makes use of a high-performance device. But it less-leverages the optimizations offered by them. We propose a rotational image encryption technique which makes use of memory coalescing provided by the Compute Unified Device Architecture (CUDA). The proposed scheme achieves improved global memory utilization and increased efficiency.

In recent years, the attacks on systems have increased and among such attack is Distributed Denial of Service (DDoS) attack. The path identifiers (PIDs) used for inter-domain routing are static, which makes it easier the attack easier. To address this vulnerability, this paper addresses the usage of Dynamic Path Identifiers (D-PIDs) for routing. The PID of inter-domain path connector is kept oblivious and changes dynamically, thus making it difficult to attack the system. The prototype designed with major components like client, server and router analyses the outcome of D-PID usage instead of PIDs. The results show that, DDoS attacks can be effectively prevented if Dynamic Path Identifiers (D-PIDs) are used instead of Static Path Identifiers (PIDs).

Cloud Computing has emerged as a paradigm to deliver on demand resources to facilitate the customers with access to their infrastructure and applications as per their requirements on a subscription basis. An exponential increase in the number of cloud services in the past few years provides more options for customers to choose from. To assist customers in selecting a most trustworthy cloud provider, a unified trust evaluation framework is needed. Trust helps in the estimation of competency of a resource provider in completing a task thus enabling users to select the best resources in the heterogeneous cloud infrastructure. Trust estimates obtained using the AHP process exhibit a deviation for parameters that are not in direct proportion to the contributing attributes. Such deviation can be removed using the Fuzzy AHP model. In this paper, a Fuzzy AHP based hierarchical trust model has been proposed to rate the service providers and their various plans for infrastructure as a service.

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn't avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.

Robust and stringent fault detection and correction techniques in executing Advanced Encryption Standard (AES) are still interesting issues for many critical applications. The purpose of fault detection and correction techniques is not only to ensure the reliability of a cryptosystem, but also protect the system against side channel attacks. Such errors could result due to a fault injection attack, production faults, noise or radiation effects in deep space. Devising a proper error control mechanisms for AES cipher during execution would improve both system reliability and security. In this work a novel fault detection and correction algorithm is proposed. The proposed mechanism is making use of the linear mappings of AES round structure to detect errors in the ShiftRow (SR) and MixColumn (MC) transformations. The error correction is achieved by creating temporary redundant check words through the combined SR and MC mapping to create in case of errors an error syndrome leading to error correction with relatively minor additional complexity. The proposed technique is making use of an error detecting and correcting capability in the combined mapping of SR and MC rather than detecting and/or correcting errors in each transformation separately. The proposed technique is making use especially of the MC mapping exhibiting efficient ECC properties, which can be deployed to simplify the design of a fault-tolerance technique. The performance of the algorithm proposed is evaluated by a simulated system model in FPGA technology. The simulation results demonstrate the ability to reach relatively high fault coverage with error correction up to four bytes of execution errors in the merged transformation SR-MC. The overall gate complexity overhead of the resulting system is estimated for proposed technique in FPGA technology.

Massive MIMO and tight cooperation between transmission nodes are expected to become an integral part of a future 5G radio system. As part of an overall interference mitigation scheme substantial gains in coverage, spectral as well as energy efficiency have been reported. One of the main limitations for massive MIMO and coordinated multi-point (CoMP) systems is the aging of the channel state information at the transmitter (CSIT), which can be overcome partly by state of the art channel prediction techniques. For a clean slate 5G radio system, we propose to integrate channel prediction from the scratch in a flexible manner to benefit from future improvements in this area. As any prediction is unreliable by nature, further improvements over the state of the art are needed for a convincing solution. In this paper, we explain how the basic ingredients of 5G like base stations with massive MIMO antenna arrays, and multiple UE antennas can help to stretch today's limits with an approximately 10 dB lower normalized mean square error (NMSE) of the predicted channel. In combination with the novel introduced concept of artificially mutually coupled antennas, adding super-directivity gains to virtual beamforming, robust and accurate prediction over 10 ms with an NMSE of -20 dB up to 15 km/h at 2.6 GHz RF frequency could be achieved. This result has been achieved for measured channels without massive MIMO, but a comparison with ray-traced channels for the same scenario is provided as well.

Industrial Control Systems (ICS) which among others are comprised of Supervisory Control and Data Acquisition (SCADA) and Distributed Control Systems (DCS) are used to control industrial processes. ICS have now been connected to other Information Technology (IT) systems and have as a result become vulnerable to Advanced Persistent Threats (APT). APTs are targeted attacks that use zero-day attacks to attack systems. Current ICS security mechanisms fail to deter APTs from infiltrating ICS. An analysis of possible solutions to deter APTs was done. This paper proposes the use of Artificial Immune Systems to secure ICS from APTs.

Non-intrusive load monitoring (NILM) extracts information about how energy is being used in a building from electricity measurements collected at a single location. Obtaining measurements at only one location is attractive because it is inexpensive and convenient, but it can result in large amounts of data from high frequency electrical measurements. Different ways to compress or selectively measure this data are therefore required for practical implementations of NILM. We explore the use of random filtering and random demodulation, techniques that are closely related to compressed sensing, to offer a computationally simple way of compressing the electrical data. We show how these techniques can allow one to reduce the sampling rate of the electricity measurements, while requiring only one sampling channel and allowing accurate NILM performance. Our tests are performed using real measurements of electrical signals from a public data set, thus demonstrating their effectiveness on real appliances and allowing for reproducibility and comparison with other data management strategies for NILM.

Ensuring system survivability in the wake of advanced persistent threats is a big challenge that the security community is facing to ensure critical infrastructure protection. In this paper, we define metrics and models for the assessment of coordinated massive malware campaigns targeting critical infrastructure sectors. First, we develop an analytical model that allows us to capture the effect of neighborhood on different metrics (infection probability and contagion probability). Then, we assess the impact of putting operational but possibly infected nodes into quarantine. Finally, we study the implications of scanning nodes for early detection of malware (e.g., worms), accounting for false positives and false negatives. Evaluating our methodology using a small four-node topology, we find that malware infections can be effectively contained by using quarantine and appropriate rates of scanning for soft impacts.

The enormous size of video data of natural scene and objects is a practical threat to storage, transmission. The efficient handling of video data essentially requires compression for economic utilization of storage space, access time and the available network bandwidth of the public channel. In addition, the protection of important video is of utmost importance so as to save it from malicious intervention, attack or alteration by unauthorized users. Therefore, security and privacy has become an important issue. Since from past few years, number of researchers concentrate on how to develop efficient video encryption for secure video transmission, a large number of multimedia encryption schemes have been proposed in the literature like selective encryption, complete encryption and entropy coding based encryption. Among above three kinds of algorithms, they all remain some kind of shortcomings. In this paper, we have proposed a lightweight selective encryption algorithm for video conference which is based on efficient XOR operation and symmetric hierarchical encryption, successfully overcoming the weakness of complete encryption while offering a better security. The proposed algorithm guarantees security, fastness and error tolerance without increasing the video size.

Traditional encryption techniques require packet overhead, produce processing time delay, and suffer from severe quality of service deterioration due to fades and interference in wireless channels. These issues reduce the effective transmission data rate (throughput) considerably in wireless communications, where data rate with limited bandwidth is the main constraint. In this paper, performance evaluation analyses are conducted for an integrated signaling-encryption mechanism that is secure and enables improved throughput and probability of bit-error in wireless channels. This mechanism eliminates the drawbacks stated herein by encrypting only a small portion of an entire transmitted frame, while the rest is not subject to traditional encryption but goes through a signaling process (designed transformation) with the plaintext of the portion selected for encryption. We also propose to incorporate error correction coding solely on the small encrypted portion of the data to drastically improve the overall bit-error rate performance while not noticeably increasing the required bit-rate. We focus on validating the signaling-encryption mechanism utilizing Hamming and convolutional error correction coding by conducting an end-to-end system-level simulation-based study. The average probability of bit-error and throughput of the encryption mechanism are evaluated over standard Gaussian and Rayleigh fading-type channels and compared to the ones of the conventional advanced encryption standard (AES).

With the growing number of proposed clean-slate redesigns of the Internet, the need for a medium that enables all stakeholders to participate in the realization, evaluation, and selection of these designs is increasing. We believe that the missing catalyst is a meta network architecture that welcomes most, if not all, clean-state designs on a level playing field, lowers deployment barriers, and leaves the final evaluation to the broader community. This paper presents Linux XIA, a native implementation of XIA in the Linux kernel, as a candidate. We first describe Linux XIA in terms of its architectural realizations and algorithmic contributions. We then demonstrate how to port several distinct and unrelated network architectures onto Linux XIA. Finally, we provide a hybrid evaluation of Linux XIA at three levels of abstraction in terms of its ability to: evolve and foster interoperation of new architectures, embed disparate architectures inside the implementation's framework, and maintain a comparable forwarding performance to that of the legacy TCP/IP implementation. Given this evaluation, we substantiate a previously unsupported claim of XIA: that it readily supports and enables network evolution, collaboration, and interoperability - traits we view as central to the success of any future Internet architecture.

In response to the critical challenges of the current Internet architecture and its protocols, a set of so-called clean slate designs has been proposed. Common among them is an addressing scheme that separates location and identity with self-certifying, flat and non-aggregatable address components. Each component is long, reaching a few kilobits, and would consume an amount of fast memory in data plane devices (e.g., routers) that is far beyond existing capacities. To address this challenge, we present Caesar, a high-speed and length-agnostic forwarding engine for future border routers, performing most of the lookups within three fast memory accesses. To compress forwarding states, Caesar constructs scalable and reliable Bloom filters in Ternary Content Addressable Memory (TCAM). To guarantee correctness, Caesar detects false positives at high speed and develops a blacklisting approach to handling them. In addition, we optimize our design by introducing a hashing scheme that reduces the number of hash computations from k to log(k) per lookup based on hash coding theory. We handle routing updates while keeping filters highly utilized in address removals. We perform extensive analysis and simulations using real traffic and routing traces to demonstrate the benefits of our design. Our evaluation shows that Caesar is more energy-efficient and less expensive (in terms of total cost) compared to optimized IPv6 TCAM-based solutions by up to 67% and 43% respectively. In addition, the total cost of our design is approximately the same for various address lengths.