Biblio

Data Transmission in network security is one of the most vital issues in today's communication world. The outcome of the suggested method is outlined over here. Enhanced security can be achieved by this method. The vigorous growth in the field of information communication has made information transmission much easier. But this type of advancement has opened up many possibilities of information being snooped. So, day-by-day maintaining of information security is becoming an inseparable part of computing and communication. In this paper, the authors have explored techniques that blend cryptography & steganography together. In steganography, information is kept hidden behind a cover image. In this paper, approaches for information hiding using both cryptography & steganography is proposed keeping in mind two considerations - size of the encrypted object and degree of security. Here, signature image information is kept hidden into cover image using private key of sender & receiver, which extracts the information from stego image using a public key. This approach can be used for message authentication, message integrity & non-repudiation purpose.

One of a high relentless attack is the crucial distributed DoS attacks. The types and tools for this attacks increases day-to-day as per the technology increases. So the methodology for detection of DDoS should be advanced. For this purpose we created an automated DDoS detector using ML which can run on any commodity hardware. The results are 98.5 % accurate. We use three classification algorithms KNN, RF and NB to classify DDoS packets from normal packets using two features, delta time and packet size. This detector mostly can detect all types of DDoS such as ICMP flood, TCP flood, UDP flood etc. In the older systems they detect only some types of DDoS attacks and some systems may require a large number of features to detect DDoS. Some systems may work only with certain protocols only. But our proposed model overcome these drawbacks by detecting the DDoS of any type without a need of specific protocol that uses less amount of features.

The wireless technology has knocked the door of tremendous usage and popularity in the last few years along with a high growth rate for new applications in the networking domain. Mobile Ad hoc Networks (MANETs) is solitary most appealing, alluring and challenging field where in the participating nodes do not require any active, existing and centralized system or rigid infrastructure for execution purpose and thus nodes have the moving capability on arbitrary basis. Radio range nodes directly communicate with each other through the wireless links whereas outside range nodes uses relay principle for communication. Though it is a rigid infrastructure less environment and has high growth rate but security is a major concern and becomes vital part of providing hostile free environment for communication. The MANET imposes several prominent challenges such as limited energy reserve, resource constraints, highly dynamic topology, sharing of wireless medium, energy inefficiency, recharging of the batteries etc. These challenges bound to make MANET more susceptible, more close to attacks and weak unlike the wired line networks. Theresearch paperismainly focused on two aspects, one is computation termination of cluster head algorithm and another is use of finite state machine for attacks identification.

The goal of this project is to create a realistic VR experience of solitary confinement and study its impact on users. Although there have been active debates and studies on this subject, very few people have personal experience of solitary confinement. Our first aim is to create such an experience in VR to raise the awareness of solitary confinement. We also want to conduct user studies to compare the VR solitary confinement experience with other types of media experiences, such as films or personal narrations. Finally, we want to study people’s sense of time in such a VR environment.

Present day's data security plays a vital role in digital human life. Data is a valuable asset to any organization and hence its security from external attacks is very important. Information security is not only an important aspect but essential, to secure data from unapproved access. Data encryption, decryption and key management are the key factors in data protection. It is very important to have the right data security solution to meet the challenging threats. Cryptosystem implementation and random number generators are crucial for Cryptosystem applications such as security applications, space applications, military applications and smart cards et al. In this paper, we present the implementation of hybrid cryptosystem based on the True Random number Generator, pseudo Random number Generator and whitening the data by using the ASIC design flow.

The following topics are dealt with: diseases; medical signal processing; learning (artificial intelligence); security of data; blood; patient treatment; patient monitoring; bioelectric phenomena; biomedical electrodes; biological tissues.

Reverse engineers are in high demand in digital forensics for their ability to investigate malicious cyberspace threats. This group faces unique challenges due to the security-intensive environment, such as working in isolated networks, a limited ability to share files with others, immense time pressure, and a lack of cognitive support tools supporting the iterative exploration of binary executables. This paper presents an exploratory study that interviewed experienced reverse engineers' work processes, tools, challenges, and visualization needs. The findings demonstrate that engineers have difficulties managing hypotheses, organizing results, and reporting findings during their analysis. By considering the provenance support techniques of existing research in other domains, this study contributes new insights about the needs and opportunities for reverse engineering provenance tools.

Formal languages are ubiquitous wherever software systems need to exchange or store data. Unparsing into and parsing from such languages is an error-prone process that has spawned an entire class of security vulnerabilities. There has been ample research into finding vulnerabilities on the parser side, but outside of language specific approaches, few techniques targeting unparser vulnerabilities exist. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software. This work presents a language-agnostic approach for spotting injection vulnerabilities in unparsers. It achieves this by mining unparse trees using dynamic taint analysis to extract language keywords, which are leveraged for guided fuzzing. Vulnerabilities can thus be found without requiring prior knowledge about the formal language, and in fact, the approach is even applicable where no specification thereof exists at all. This empowers security researchers and developers alike to gain deeper understanding of unparser implementations through examination of the unparse trees generated by the approach, as well as enabling them to find new vulnerabilities in poorly-understood software.

An adversarial attack is an algorithm that perturbs the input of a machine learning model in an intelligent way in order to change the output of the model. An important property of adversarial attacks is transferability. According to this property, it is possible to generate adversarial perturbations on one model and apply it the input to fool the output of a different model. Our work focuses on studying the transferability of adversarial attacks in sound event classification. We are able to demonstrate differences in transferability properties from those observed in computer vision. We show that dataset normalization techniques such as z-score normalization does not affect the transferability of adversarial attacks and we show that techniques such as knowledge distillation do not increase the transferability of attacks.

This paper proposes a Time Difference of Arrival (TDoA) based method for the localization of Radio Frequency (RF) emitters working at different carriers, by using wideband spectrum sensors exploiting compressive sampling. The proposed measurement method is based on four or more RF receivers, with known Cartesian positions, performing non uniform sampling on the received signal. By means of simulations, the method has been compared against a localization method adopting RF receivers performing uniform sampling at Nyquist rate. The obtained preliminary results demonstrate that the method is capable of localizing two RF emitters achieving the same results obtained with uniform sampling, with a compression ratio up to CR = 20.

In recent years, attacks against cyber-physical systems have become increasingly frequent and widespread. The inventiveness of such attacks increases significantly. In particular, zero-day attacks are widely used. The rapid development of the industrial Internet of things, the expansion of the application areas of service robots, the advent of the Internet of vehicles and the Internet of military things have led to a significant increase of attention to deceptive attacks. Especially great threat is posed by deceptive attacks that do not use hiding malicious components. Such attacks can naturally be used against robotic systems. In this paper, we consider an approach to the development of an intrusion detection system for closed-loop robotic systems. The system is based on an abnormal behavioral pattern detection technique. The system can be used for detection of zero-day deceptive attacks. We provide an experimental comparison of our approach and other behavior-based intrusion detection systems.

In the past decades, learning an effective distance metric between pairs of instances has played an important role in the classification and retrieval task, for example, the person identification or malware retrieval in the IoT service. The core motivation of recent efforts focus on improving the metric forms, and already showed promising results on the various applications. However, such models often fail to produce a reliable metric on the ambiguous test set. It happens mainly due to the sampling process of the training set, which is not representative of the distribution of the negative samples, especially the examples that are closer to the boundary of different categories (also called hard negative samples). In this paper, we focus on addressing such problems and propose an adaptive margin deep adversarial metric learning (AMDAML) framework. It exploits numerous common negative samples to generate potential hard (adversarial) negatives and applies them to facilitate robust metric learning. Apart from the previous approaches that typically depend on the search or data augmentation to find hard negative samples, the generation of adversarial negative instances could avoid the limitation of domain knowledge and constraint pairs' amount. Specifically, in order to prevent over fitting or underfitting during the training step, we propose an adaptive margin loss that preserves a flexible margin between the negative (include the adversarial and original) and positive samples. We simultaneously train both the adversarial negative generator and conventional metric objective in an adversarial manner and learn the feature representations that are more precise and robust. The experimental results on practical data sets clearly demonstrate the superiority of AMDAML to representative state-of-the-art metric learning models.

Bluetooth Classic (BT) remains the de facto connectivity technology in car stereo systems, wireless headsets, laptops, and a plethora of wearables, especially for applications that require high data rates, such as audio streaming, voice calling, tethering, etc. Unlike in Bluetooth Low Energy (BLE), where address randomization is a feature available to manufactures, BT addresses are not randomized because they are largely believed to be immune to tracking attacks. We analyze the design of BT and devise a robust de-anonymization technique that hinges on the apparently benign information leaking from frame encoding, to infer a piconet's clock, hopping sequence, and ultimately the Upper Address Part (UAP) of the master device's physical address, which are never exchanged in clear. Used together with the Lower Address Part (LAP), which is present in all frames transmitted, this enables tracking of the piconet master, thereby debunking the privacy guarantees of BT. We validate this attack by developing the first Software-defined Radio (SDR) based sniffer that allows full BT spectrum analysis (79 MHz) and implements the proposed de-anonymization technique. We study the feasibility of privacy attacks with multiple testbeds, considering different numbers of devices, traffic regimes, and communication ranges. We demonstrate that it is possible to track BT devices up to 85 meters from the sniffer, and achieve more than 80% device identification accuracy within less than 1 second of sniffing and 100% detection within less than 4 seconds. Lastly, we study the identified privacy attack in the wild, capturing BT traffic at a road junction over 5 days, demonstrating that our system can re-identify hundreds of users and infer their commuting patterns.

Indoor localization has been a popular research subject in recent years. Usually, object localization using sound involves devices on the objects, acquiring data from stationary sound sources, or by localizing the objects with external sensors when the object generates sounds. Indoor localization systems using microphones have traditionally also used systems with several microphones, setting the limitations on cost efficiency and required space for the systems. In this paper, the goal is to investigate whether it is possible for a stationary system to localize a silent object in a room, with only one microphone and ambient noise as information carrier. A subtraction method has been combined with a fingerprint technique, to define and distinguish the noise absorption characteristic of the silent object in the frequency domain for different object positions. The absorption characteristics of several positions of the object is taken as comparison references, serving as fingerprints of known positions for an object. With the experiment result, the tentative idea has been verified as feasible, and noise signal based lateral localization of silent objects can be achieved.

Analyzing the security of closed source binaries is currently impractical for end-users, or even developers who rely on third-party libraries. Such analysis relies on automatic vulnerability discovery techniques, most notably fuzzing with sanitizers enabled. The current state of the art for applying fuzzing or sanitization to binaries is dynamic binary translation, which has prohibitive performance overhead. The alternate technique, static binary rewriting, cannot fully recover symbolization information and hence has difficulty modifying binaries to track code coverage for fuzzing or to add security checks for sanitizers.The ideal solution for binary security analysis would be a static rewriter that can intelligently add the required instrumentation as if it were inserted at compile time. Such instrumentation requires an analysis to statically disambiguate between references and scalars, a problem known to be undecidable in the general case. We show that recovering this information is possible in practice for the most common class of software and libraries: 64-bit, position independent code. Based on this observation, we develop RetroWrite, a binary-rewriting instrumentation to support American Fuzzy Lop (AFL) and Address Sanitizer (ASan), and show that it can achieve compiler-level performance while retaining precision. Binaries rewritten for coverage-guided fuzzing using RetroWrite are identical in performance to compiler-instrumented binaries and outperform the default QEMU-based instrumentation by 4.5x while triggering more bugs. Our implementation of binary-only Address Sanitizer is 3x faster than Valgrind's memcheck, the state-of-the-art binary-only memory checker, and detects 80% more bugs in our evaluation.

The paper presents a comprehensive model of cybersecurity threats for a system of autonomous and remotely controlled vehicles (AV) in the environment of a smart city. The main focus in the security context is given to the “integrity” property. That property is of higher importance for industrial control systems in comparison with other security properties (availability and confidentiality). The security graph, which is part of the model, is dynamic, and, in real cases, its analysis may require significant computing resources for AV systems with a large number of assets and connections. The simplified example of the security graph for the AV system is presented.

Development of large-scale and complex software systems requires multiple teams, including software development teams, domain experts, user representatives, and other project stakeholders, to work collaboratively to achieve software development goals. These teams rely on the use of agreed software development processes, knowledge management tools, and communication channels collaboratively in the software development project. Software testing is an important and complicated process due to reasons such as difficulties in achieving testing goals with the given time constraint, absence of efficient data sharing policies, vague testing acceptance criteria at various levels of testing, and lack of trusted coordination among the teams involved in software testing. The efficiency of the software testing relies on efficient, reliable, and trusted information sharing among these teams. Existing approaches to software testing for collaborative software development use centralized or decentralize tools for software testing, knowledge management, and communication channels. Existing approaches have the limitations of centralized authority, a single point of failure/compromise, lack of automatic requirement compliance checking and transparency in information sharing, and lack of unified data sharing policy, and reliable knowledge management repositories for sharing and storing past software testing artifacts and data. In this paper, a software testing approach for collaborative software development using private blockchain is presented, and the desirable properties of private blockchain, such as distributed data management, tamper-resistance, auditability and automatic requirement compliance checking, are incorporated to greatly improve the quality of software testing for collaborative software development.

Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput.

The continuous integration of intermittent low-carbon energy resources makes islanded microgrids vulnerable to voltage fluctuations. Besides, different dynamic response of synchronous-based and inverter-based distributed generation (DG) units can result in an instantaneous power imbalance between supply and demand during transients. As a result, the ac-bus voltage of microgrid starts oscillating which might have severe consequences such as blackouts. This paper modifies the conventional control scheme of battery energy storage systems (BESSs) to participate in improving the dynamic behavior of islanded microgrids by mitigating the voltage fluctuations. A piecewise linear-elliptic (PLE) droop is proposed and employed in BESS to achieve an enhanced voltage profile by injecting/absorbing reactive power during transients. In this way, the conventional inverter implemented in BESS turns into a smart inverter to cope with fast transients. Using the proposed approach in this paper, any linear droop curve with a specified coefficient can be replaced by a PLE droop curve. Compared with linear droop, an enhanced dynamic response is achieved by utilizing the proposed PLE droop. Case study results are presented using PSCAD/EMTDC to demonstrate the superiority of the proposed approach in improving the dynamic behavior of islanded microgrids.

The recent advances in mobile devices and wireless communication sector transformed Mobile Augmented Reality (MAR) from science fiction to reality. Among the other MAR use cases, the incorporation of this MAR technology in the healthcare sector can elevate the quality of diagnosis and treatment for the patients. However, due to the highly sensitive nature of the data available in this process, it is also highly vulnerable to all types of security threats. In this paper, an edge-based secure architecture is presented for a MAR healthcare application. Based on the ESSMAR architecture, a secure key management scheme is proposed for both the registration and authentication phases. Then the security of the proposed scheme is validated using formal and informal verification methods.

Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of possible attacks to real-time privacy-preserving algorithms for SMs. In particular, we consider a deep adversarial learning framework, in which the desired releaser, which is a Recurrent Neural Network (RNN), is trained by fighting against an adversary network until convergence. To define the objective for training, two different approaches are considered: the Causal Adversarial Learning (CAL) and the Directed Information (DI)-based learning. The main difference between these approaches relies on how the privacy term is measured during the training process. The releaser in the CAL method, disposing of supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood. On the other hand, the releaser in the DI approach completely relies on the feedback received from the adversary and is optimized to maximize its uncertainty. The performance of these two algorithms is evaluated empirically using real-world SMs data, considering an attacker with access to SI (e.g., the day of the week) that tries to infer the occupancy status from the released SMs data. The results show that, although they perform similarly when the attacker does not exploit the SI, in general, the CAL method is less sensitive to the inclusion of SI. However, in both cases, privacy levels are significantly affected, particularly when multiple sources of SI are included.

A continually growing number of sensors is required for monitoring industrial processes and for continuous data acquisition from industrial plants and devices. The cabling of sensors represent a considerable effort and potential source of error, which can be avoided by using wireless sensor nodes. These wireless sensor nodes form a wireless sensor network (WSN) to efficiently transmit data to the destination. For the acceptance of WSNs in industry, it is important to build up networks with high trustworthiness. The trustworthiness of the WSN depends not only on a secure wireless communication but also on the ability to detect modifications at the wireless sensor nodes itself. This paper presents the enhancement of the WSN's trustworthiness using an optical localization system. It can be used for the preparation phase of the WSN and also during operation to track the positions of the wireless sensor nodes and detect spatial modification. The location information of the sensor nodes can also be used to rate their trustworthiness.

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

Nowadays, machine learning is a popular method for DDoS detection. However, machine learning algorithms are very vulnerable under the attacks of adversarial samples. Up to now, multiple methods of generating adversarial samples have been proposed. However, they cannot be applied to LSTM-based DDoS detection directly because of the discrete property and the utility requirement of its input samples. In this paper, we propose two methods to generate DDoS adversarial samples, named Genetic Attack (GA) and Probability Weighted Packet Saliency Attack (PWPSA) respectively. Both methods modify original input sample by inserting or replacing partial packets. In GA, we evolve a set of modified samples with genetic algorithm and find the evasive variant from it. In PWPSA, we modify original sample iteratively and use the position saliency as well as the packet score to determine insertion or replacement order at each step. Experimental results on CICIDS2017 dataset show that both methods can bypass DDoS detectors with high success rate.

Deep learning methods are increasingly becoming solutions to complex problems, including the search for anomalies. While fully-connected and convolutional neural networks have already found their application in classification problems, their applicability to the problem of detecting anomalies is limited. In this regard, it is proposed to use autoencoders, previously used only in problems of reducing the dimension and removing noise, as a method for detecting anomalies in the industrial control system. A new method based on autoencoders is proposed for detecting anomalies in the operation of industrial control systems (ICS). Several neural networks based on auto-encoders with different architectures were trained, and the effectiveness of each of them in the problem of detecting anomalies in the work of process control systems was evaluated. Auto-encoders can detect the most complex and non-linear dependencies in the data, and as a result, can show the best quality for detecting anomalies. In some cases, auto-encoders require fewer machine resources.