Biblio

As demonstrated recently, Wireless Physical Layer Security (WPLS) has the potential to offer substantial advantages for key management for small resource-constrained and, therefore, low-cost IoT-devices, e.g., the widely applied 8-bit MCU 8051. In this paper, we present a WPLS testbed implementation for independent performance and security evaluations. The testbed is based on off-the-shelf hardware and utilizes the IEEE 802.15.4 communication standard for key extraction and secret key rate estimation in real-time. The testbed can include generically multiple transceivers to simulate legitimate parties or eavesdropper. We believe with the testbed we provide a first step to make experimental-based WPLS research results comparable. As an example, we present evaluation results of several test cases we performed, while for further information we refer to https://pls.rub.de.

The world is witnessing a remarkable increase in the usage of video surveillance systems. Besides fulfilling an imperative security and safety purpose, it also contributes towards operations monitoring, hazard detection and facility management in industry/smart factory settings. Most existing surveillance techniques use hand-crafted features analyzed using standard machine learning pipelines for action recognition and event detection. A key shortcoming of such techniques is the inability to learn from unlabeled video streams. The entire video stream is unlabeled when the requirement is to detect irregular, unforeseen and abnormal behaviors, anomalies. Recent developments in intelligent high-level video analysis have been successful in identifying individual elements in a video frame. However, the detection of anomalies in an entire video feed requires incremental and unsupervised machine learning. This paper presents a novel approach that incorporates high-level video analysis outcomes with incremental knowledge acquisition and self-learning for autonomous video surveillance. The proposed approach is capable of detecting changes that occur over time and separating irregularities from re-occurrences, without the prerequisite of a labeled dataset. We demonstrate the proposed approach using a benchmark video dataset and the results confirm its validity and usability for autonomous video surveillance.

This paper describes and verifies a method of implementing bit error rate (BER) calculation for FPGA-based physical layer security techniques for Software Defined Radio (SDR). Specifically, we describe an independent source signal processing architecture for an efficient calculation of BER for wireless communication modules across the transmitter and receiver nodes. The source components at the transmitter and the receiver both generate identical random bits independently from each other, allowing for the received data to be compared to the original bit stream to calculate BER completely on hardware. The described method is implemented on a Xilinx Virtex-6 ML605 FPGA and reduces processing time by more than four orders of magnitude less than hardware simulation techniques in regression testing and validation over billions of bits, shortening design turn around times and accelerating Physical layer based security development for wireless communication research. The described independent source approach utilizes a minimal amount of board resources, allowing it to be integrated seamlessly into SDR hardware designs. Experimental validation of the independent source based BER calculation is performed for an Orthogonal Frequency Division Multiplexing signal, and a comparison between different stages of hardware design for the execution time required for BER testing of a large number of bits is provided.

ICT systems have become an integral part of business and life. At the same time, these systems have become extremely complex. In such systems exist numerous vulnerabilities waiting to be exploited by potential threat actors. pwnPr3d is a novel modelling approach that performs automated architectural analysis with the objective of measuring the cyber security of the modeled architecture. Its integrated modelling language allows users to model software and hardware components with great level of details. To illustrate this capability, we present in this paper the metamodel of UNIX, operating systems being the core of every software and every IT system. After describing the main UNIX constituents and how they have been modelled, we illustrate how the modelled OS integrates within pwnPr3d's rationale by modelling the spreading of a self-replicating malware inspired by WannaCry.

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler (KL) divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.

Integer errors in C/C++ are caused by arithmetic operations yielding results which are unrepresentable in certain type. They can lead to serious safety and security issues. Due to the complicated semantics of C/C++ integers, integer errors are widely harbored in real-world programs and it is error-prone to repair them even for experts. An automatic tool is desired to 1) automatically generate fixes which assist developers to correct the buggy code, and 2) provide sufficient hints to help developers review the generated fixes and better understand integer types in C/C++. In this paper, we present a tool IntPTI that implements the desired functionalities for C programs. IntPTI infers appropriate types for variables and expressions to eliminate representation issues, and then utilizes the derived types with fix patterns codified from the successful human-written patches. IntPTI provides a user-friendly web interface which allows users to review and manage the fixes. We evaluate IntPTI on 7 real-world projects and the results show its competitive repair accuracy and its scalability on large code bases. The demo video for IntPTI is available at: https://youtu.be/9Tgd4A\_FgZM.

Information security is crucial to data storage and transmission, which is necessary to protect information under various hostile environments. Cryptography serves as a major element to ensure confidentiality in both communication and information technology, where the encryption and decryption schemes are implemented to scramble the pure plaintext and descramble the secret ciphertext using security keys. There are two dominating types of encryption schemes: deterministic encryption and chaotic encryption. Encryption and decryption can be conducted in either spatial domain or frequency domain. To ensure secure transmission of digital information, comparisons on merits and drawbacks of two practical encryption schemes are conducted, where case studies on the true color digital image encryption are presented. Both deterministic encryption in spatial domain and chaotic encryption in frequency domain are analyzed in context, as well as the information integrity after decryption.

In Germany, as of 2017, a new smart metering infrastructure based on high security and privacy requirements will be deployed. It provides interfaces to connect meters for different commodities, to allow end users to retrieve the collected measurement data, to connect to the metering operators, and to connect Controllable Local Systems (CLSs) that establish a TLS secured connection to third parties in order to exchange data or for remote controlling of energy devices. This paper aims to connect industrial machines as CLS devices since it shows that the demands and main ideas of remotely controlled devices in the Smart Grid context and Industrial Cloud Applications match on the communication level. It describes the general architecture of the Smart Metering infrastructure in Germany, introduces the defined roles, depicts the configuration process on the different organizational levels, demonstrates the connection establishment and the initiating partners, concludes on the potential industrial use cases of this infrastructure, and provides open questions and room for further research.

Mobile ad-hoc network (MANET) contains various wireless movable nodes which can communicate with each other and they don't require any centralized administrator or network infrastructure and also can communicate with full capacity because it is composed of mobile nodes. They transmit data to each other with the help of intermediate nodes by establishing a path. But sometime malicious node can easily enter in network due to the mobility of nodes. That malicious node can harm the network by dropping the data packets. These type of attack is called gray hole attack. For detection and prevention from this type of attack a mechanism is proposed in this paper. By using network simulator, the simulation will be carried out for reporting the difficulties of prevention and detection of multiple gray hole attack in the Mobile ad-hoc network (MANET). Particle Swarm Optimization is used in this paper. Because of ad-hoc nature it observers the changing values of the node, if the value is infinite then node has been attacked and it prevents other nodes from sending data to that node. In this paper, we present possible solutions to prevent the network. Firstly, find more than one route to transmit packets to destination. Second, we provide minimum time delay to deliver the packet. The simulation shows the higher throughput, less time delay and less packet drop.

The collaborative recommendation mechanism is beneficial for the subject in an open network to find efficiently enough referrers who directly interacted with the object and obtain their trust data. The uncertainty analysis to the collected trust data selects the reliable trust data of trustworthy referrers, and then calculates the statistical trust value on certain reliability for any object. After that the subject can judge its trustworthiness and further make a decision about interaction based on the given threshold. The feasibility of this method is verified by three experiments which are designed to validate the model's ability to fight against malicious service, the exaggeration and slander attack. The interactive success rate is significantly improved by using the new model, and the malicious entities are distinguished more effectively than the comparative model.

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack.

In this paper we investigate whether and how hardware-based roots of trust, namely Trusted Platform Modules (TPMs) can improve the security of the communication protocol OPC UA (Open Platform Communications Unified Architecture) under reasonable assumptions, i.e. the Dolev-Yao attacker model. Our analysis shows that TPMs may serve for generating (RNG) and securely storing cryptographic keys, as cryptocoprocessors for weak systems, as well as for remote attestation. We propose to include these TPM functions into OPC UA via so-called ConformanceUnits, which can serve as building blocks of profiles that are used by clients and servers for negotiating the parameters of a session. Eventually, we present first results regarding the performance of a client-server communication including an additional OPC UA server providing remote attestation of other OPC UA servers.

As the malware threat landscape is constantly evolving and over one million new malware strains are being generated every day [1], early automatic detection of threats constitutes a top priority of cybersecurity research, and amplifies the need for more advanced detection and classification methods that are effective and efficient. In this paper, we present the application of machine learning algorithms to predict the length of time malware should be executed in a sandbox to reveal its malicious intent. We also introduce a novel hybrid approach to malware classification based on static binary analysis and dynamic analysis of malware. Static analysis extracts information from a binary file without executing it, and dynamic analysis captures the behavior of malware in a sandbox environment. Our experimental results show that by turning the aforementioned problems into machine learning problems, it is possible to get an accuracy of up to 90% on the prediction of the malware analysis run time and up to 92% on the classification of malware families.

The tree-based tags anti-collision algorithm is an important method in the anti-collision algorithms. In this paper, several typical tree algorithms are evaluated. The comparison of algorithms is summarized including time complexity, communication complexity and recognition, and the characteristics and disadvantages of each algorithm are pointed out. Finally, the improvement strategies of tree anti-collision algorithm are proposed, and the future research directions are also prospected.

Underwater sensing is envisioned using inexpensive underwater sensor nodes distributed over a wide area, deployed close to the bottom, and networked through underwater acoustic communications. In this paper, an underwater acoustic sensor node to perform the underwater sensing is designed and implemented. Specifically, we describe the design criteria, architecture and functional modules of underwater acoustic sensor node. Moreover, we give the experiment results of ocean current field estimation using the designed underwater acoustic sensor nodes at the sea area of Liuheng, Zhoushan, China.

This poster documents three approaches that we are undertaking to increase security awareness within undergraduate computer science classes. The first approach is a verbal password entry system, with surreptitious photos being taken when the mobile device is stolen. The second approach is a lab where students develop a password entry and verification system between a mobile device and a remote server. The third approach is a captcha system, where students implement a simple challenge that can be verified. Like password entry, the captcha communications must be secure and difficult to automatically manipulate. Unlike password entry, the captcha is meant to allow humans access while denying other computers.

When created, the Java platform was among the first runtimes designed with security in mind. Yet, numerous Java versions were shown to contain far-reaching vulnerabilities, permitting denial-of-service attacks or even worse allowing intruders to bypass the runtime's sandbox mechanisms, opening the host system up to many kinds of further attacks. This paper presents a systematic in-depth study of 87 publicly available Java exploits found in the wild. By collecting, minimizing and categorizing those exploits, we identify their commonalities and root causes, with the goal of determining the weak spots in the Java security architecture and possible countermeasures. Our findings reveal that the exploits heavily rely on a set of nine weaknesses, including unauthorized use of restricted classes and confused deputies in combination with caller-sensitive methods. We further show that all attack vectors implemented by the exploits belong to one of three categories: single-step attacks, restricted-class attacks, and information hiding attacks. The analysis allows us to propose ideas for improving the security architecture to spawn further research in this area.

To systematically study the implications of additional information about routes provided to certain users (e.g., via GPS-based route guidance systems), we introduce a new class of congestion games in which users have differing information sets about the available edges and can only use routes consisting of edges in their information set. After defining the notion of Information Constrained Wardrop Equilibrium (ICWE) for this class of congestion games and studying its basic properties, we turn to our main focus: whether additional information can be harmful (in the sense of generating greater equilibrium costs/delays). We formulate this question in the form of Informational Braess' Paradox (IBP), which extends the classic Braess' Paradox in traffic equilibria, and asks whether users receiving additional information can become worse off. We provide a comprehensive answer to this question showing that in any network in the series of linearly independent (SLI) class, which is a strict subset of series-parallel network, IBP cannot occur, and in any network that is not in the SLI class, there exists a configuration of edge-specific cost functions for which IBP will occur. In the process, we establish several properties of the SLI class of networks, which are comprised of linearly independent networks joined together. These properties include the characterization of the complement of the SLI class in terms of embedding a specific set of subgraphs, and also show that whether a graph is SLI can be determined in linear time. We further prove that the worst-case inefficiency performance of ICWE is no worse than the standard Wardrop Equilibrium with one type of users.

This paper proposed a novel sparse representation-based infrared target tracking method using multi-feature fusion to compensate for incomplete description of single feature. In the proposed method, we extract the intensity histogram and the data on-Local Entropy and Local Contrast Mean Difference information for feature representation. To combine various features, particle candidates and multiple feature descriptors of dictionary templates were encoded as kernel matrices. Every candidate particle was sparsely represented as a linear combination of a set of atom vectors of a dictionary. Then, the sparse target template representation model was efficiently constructed using a kernel trick method. Finally, under the framework of particle filter the weights of particles were determined by sparse coefficient reconstruction errors for tracking. For tracking, a template update strategy employing Adaptive Structural Local Sparse Appearance Tracking (ASLAS) was implemented. The experimental results on benchmark data set demonstrate the better performance over many existing ones.

Now a days, ATM is used for money transaction for the convenience of the user by providing round the clock 24*7 services in financial transaction. Bank provides the Debit or Credit card to its user along with particular PIN number (which is only known by the Bank and User). Sometimes, user's card may be stolen by someone and this person can access all confidential information as Credit card number, Card holder name, Expiry date and CVV number through which he/she can complete fake transaction. In this paper, we introduced the biometric encryption of "EYE RETINA" to enhance the security over the wireless and unreliable network as internet. In this method user can authorizeasthird person his/her behalf to make the transaction using Debit or Credit card. In proposed method, third person can also perform financial transaction by providing his/her eye retina for the authorization & identification purpose.

BGP is known to have many security vulnerabilities due to the very nature of its underlying assumptions of trust among independently operated networks. Most prior efforts have focused on attacks that can be addressed using traditional cryptographic techniques to ensure authentication or integrity, e.g., BGPSec and related works. Although augmenting BGP with authentication and integrity mechanisms is critical, they are, by design, far from sufficient to prevent attacks based on manipulating the complex BGP protocol itself. In this paper, we identify two serious attacks on two of the most fundamental goals of BGP—to ensure reachability and to enable ASes to pick routes available to them according to their routing policies—even in the presence of BGPSec-like mechanisms. Our key contributions are to 1 formalize a series of critical security properties, 2 experimentally validate using commodity router implementations that BGP fails to achieve those properties, 3 quantify the extent of these vulnerabilities in the Internet's AS topology, and 4 propose simple modifications to provably ensure that those properties are satisfied. Our experiments show that, using our attacks, a single malicious AS can cause thousands of other ASes to become disconnected from thousands of other ASes for arbitrarily long, while our suggested modifications almost completely eliminate such attacks.

Identifying threats contained within encrypted network traffic poses a unique set of challenges. It is important to monitor this traffic for threats and malware, but do so in a way that maintains the integrity of the encryption. Because pattern matching cannot operate on encrypted data, previous approaches have leveraged observable metadata gathered from the flow, e.g., the flow's packet lengths and inter-arrival times. In this work, we extend the current state-of-the-art by considering a data omnia approach. To this end, we develop supervised machine learning models that take advantage of a unique and diverse set of network flow data features. These data features include TLS handshake metadata, DNS contextual flows linked to the encrypted flow, and the HTTP headers of HTTP contextual flows from the same source IP address within a 5 minute window. We begin by exhibiting the differences between malicious and benign traffic's use of TLS, DNS, and HTTP on millions of unique flows. This study is used to design the feature sets that have the most discriminatory power. We then show that incorporating this contextual information into a supervised learning system significantly increases performance at a 0.00% false discovery rate for the problem of classifying encrypted, malicious flows. We further validate our false positive rate on an independent, real-world dataset.

Many technologies have been developed to provide effective opportunities to enhance the safety of roads and improve transportation system. In face of that, the concept of Vehicular Ad-Hoc Networks (VANET) was introduced to provide intelligent transportation systems. In this work, we propose the use of an OBD Bluetooth adapter and a smartphone to gather data from two cars, then we analyze the relationships between RPM and speed data to identify if this reflects the vehicle's current gear. As a result, we found a coefficient that indicates the behavior of each gear along the time in a trace. We conclude that these analysis, although in the beginning, suggests a way to determine the gear state. Therefore, many services can be developed using this information as, recommendation of gear shift time, eco-driving support, security patterns and entertainment applications.

Although static analysis tools detect potential code defects early in the development process, they do not fully support developers in resolving those defects. To accurately and efficiently resolve defects, developers must orchestrate several complex tasks, such as determining whether the defect is a false positive and updating the source code without introducing new defects. Without good defect resolution strategies developers may resolve defects erroneously or inefficiently. In this work, I perform a preliminary analysis of the successful and unsuccessful strategies developers use to resolve defects. Based on the successful strategies identified, I then outline a tool to support developers throughout the defect resolution process.