Biblio

In recent years, many devices in industrial control systems (ICS) equip Ethernet modules for more efficient communication and more fiexible deployment. Many communication protocols of those devices are based on internet protocol, which brings the above benefits but also makes it easier to access by anyone including attackers. In the case of using the factory default configurations, we wiiˆ demonstrate how to easily modify the programmable logic controllers (PLCs) program through the Integrated Development Environment provided by the manufacturer under the security protection of PLC not set properly and discuss the severity of it.

We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet.

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance.

In this paper, Reversible data hiding using difference statistics technique incorporating QR codes was proposed. Here, Quick Response (QR) codes were employed as an additional feature and were hidden in the corners of the original image to direct to the hyperlink after authentication and then embedding the secret data bits was carried out. At the receiver side, when the QR codes were scanned by the user, the link to the webpage was accessed, and then the original image and the secret data bits were recovered by using the proposed reversible data hiding scheme. In the proposed scheme, the pixels of the cover image were scanned in row-major order fashion, and the differences between the adjacent pixels were computed, keeping the first pixel unaltered to maintain the size of the host and the difference image same. Now, the histogram was shifted towards the right or left to reduce the redundancy and then to embed the secret data bits were done. Due to the similarity exists between the pixel values, the difference between the host and the secret image reconstructs the marked image. The proposed scheme was carried out using MATLAB 2013. PSNR (Peak Signal to Noise Ratio) and payload have been computed for various test images to validate the proposed scheme and found to be better than the available literature.

In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now-a-days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber-attacks. For protecting the data's we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber-attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.

IP geolocation technology is widely adopted in network security, privacy protection, online advertising, etc. However, existing IP geolocation methods are vulnerable to delay inflation, which reduces their reliability and applicability, especially in weakly connected networks. To solve this problem, a ranking nodes based IP geolocation method (RNBG) is proposed. RNBG leverages the scale-free nature of complex networks to find a few important and stable nodes in networks. And then these nodes are used in the geolocation of IPs in different regions. Experimental results in China and the US show that RNBG can achieve high accuracy even in weakly connected network. Compared with typical methods, the geolocation accuracy is increased by 2.60%-14.27%, up to 97.55%.

The objective of the project is to structure a portable application for inspecting and putting away information through a distributed storage administration. The information is remotely put away in the cloud. In some distributed storage frameworks, the cloud record may contain some touchy data. Scrambling the entire shared record doesn't permit different clients to get to it. In the current framework the client needs to use the biometric information to guarantee the character of the client and afterward a marking key will be checked which is to ensure the personality and security of the client. The principle downside of utilizing the biometric information is that it can't be coordinated precisely because of the elements that influence the difference in biometric information. A reference id made is naturally changed over to the QR code and it is checked utilizing a scanner and the specific record is downloaded. This record whenever erased or lost in the customer's inward stockpiling it very well may be recovered again from the cloud.

Connected vehicles as a promising concept of Intelligent Transportation System (ITS), are a potential solution to address some of the existing challenges of emission, traffic congestion as well as fuel consumption. To achieve these goals, connectivity among vehicles through the wireless communication network is essential. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with purposes of disrupting the performance of the connected vehicles, lead to catastrophic collision and traffic congestion. In this study, we consider a platoon of connected vehicles equipped with Cooperative Adaptive Cruise Control (CACC) which are subjected to a specific type of cyber-attack namely "False Data Injection" attack. We developed a novel method to model the attack with ghost vehicles injected into the connected vehicles network to disrupt the performance of the whole system. To aid the analysis, we use a Partial Differential Equation (PDE) model. Furthermore, we present a PDE model-based diagnostics scheme capable of detecting the false data injection attack and isolating the injection point of the attack in the platoon system. The proposed scheme is designed based on a PDE observer with measured velocity and acceleration feedback. Lyapunov stability theory has been utilized to verify the analytically convergence of the observer under no attack scenario. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation study.

A distributed event-triggered controller is developed for formation control and leader tracking (FCLT) with robustness to adversarial Byzantine agents for a class of heterogeneous multi-agent systems (MASs). A reputation-based strategy is developed for each agent to detect Byzantine agent behaviors within their neighbor set and then selectively disregard Byzantine state information. Selectively ignoring Byzantine agents results in time-varying discontinuous changes to the network topology. Nonsmooth dynamics also result from the use of the event-triggered strategy enabling intermittent communication. Nonsmooth Lyapunov methods are used to prove stability and FCLT of the MAS consisting of the remaining cooperative agents.

Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user's tapping rhythm. In addition to verifying the RFID card's identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user's secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero.

In order to solve the problem that the Nearest Neighbor Data Association (NNDA) algorithm cannot detect the “dynamic” change of the target, this paper proposes the nearest neighbor data association algorithm based on the Targets “Dynamic” Monitoring Model (TDMM). Firstly, the gate searching and updating of targets are completed based on TDMM, then the NNDA algorithm is utilized to achieve the data association of targets to realize track updating. Finally, the NNDA algorithm based on TDMM is realized by simulation. The experimental results show that the algorithm proposed can achieve “dynamic” monitoring in multi-target data association, and have more obvious advantages than Multiple Hypothesis Tracking (MHT) in timeliness and association performance.

This paper combines the common ideas and methods in offensive and defensive confrontation in recent years, and uses artificial intelligence technology-based network asset automatic mining technology and artificial intelligence technology-based vulnerability automatic exploitation technology, carries out research and specific practices in discovering and using system vulnerability based on artificial intelligence technology, designs and implemented automatic binary vulnerability discovering and exploitation system, which improves improves the efficiency and success rate of vulnerability discovering and exploitation.

This study investigates how risk influences users' trust before and after interactions with technologies such as autonomous vehicles (AVs'). Also, the psychophysiological correlates of users' trust from users” eletrodermal activity responses. Eighteen (18) carefully selected participants embark on a hypothetical trip playing an autonomous vehicle driving game. In order to stay safe, throughout the drive experience under four risk conditions (very high risk, high risk, low risk and no risk) that are based on automotive safety and integrity levels (ASIL D, C, B, A), participants exhibit either high or low trust by evaluating the AVs' to be highly or less trustworthy and consequently relying on the Artificial intelligence or the joystick to control the vehicle. The result of the experiment shows that there is significant increase in users' trust and user's delegation of controls to AVs' as risk decreases and vice-versa. In addition, there was a significant difference between user's initial trust before and after interacting with AVs' under varying risk conditions. Finally, there was a significant correlation in users' psychophysiological responses (electrodermal activity) when exhibiting higher and lower trust levels towards AVs'. The implications of these results and future research opportunities are discussed.

Packet classification is used to determine the behavior of incoming packets to network devices. Because it is achieved using a linear search on a classification rule list, a larger number of rules leads to a longer communication latency. To decrease this latency, the problem is generalized as Optimal Rule Ordering (ORO), which aims to identify the order of rules that minimizes the classification latency caused by packet classification while preserving the classification policy. Because ORO is known to be NP-complete by Hamed and Al-Shaer [Dynamic rule-ordering optimization for high-speed firewall filtering, ASIACCS (2006) 332-342], various heuristics for ORO have been proposed. Sub-graph merging (SGM) by Tapdiya and Fulp [Towards optimal firewall rule ordering utilizing directed acyclical graphs, ICCCN (2009) 1-6] is the state of the art heuristic algorithm for ORO. In this paper, we propose a novel heuristic method for ORO. Although most heuristics try to recursively determine the maximum-weight rule and move it as far as possible to an upper position, our algorithm pairs rules that cause policy violations until there are no such rules to simply sort the rules by these weights. Our algorithm markedly decreases the classification latency and reordering time compared with SGM in experiments. The sets consisting of thousands of rules that require one or more hours for reordering by SGM can be reordered by the proposed method within one minute.

As RFID system has fewer calculation and storage resources for RF tag, it is difficult to adopt the traditional encryption algorithm technology with higher security, which leads to the system being vulnerable to counterfeiting, tampering, leakage and other problems. To this end, a lightweight bidirectional security authentication method based on the combined public key is proposed. The method deals with the key management problem of the power Internet of things (IoT) in the terminal layer device by studying the combined public key (CPK) technology. The elliptic curve cryptosystem in the CPK has the advantages of short key length, fast calculation speed and small occupied bandwidth, which is very suitable for the hardware environment of RFID system with limited performance. It also ensures the security of the keys used in the access of the IoT terminal equipment to the authentication, and achieves overall optimization of speed, energy consumption, processing capacity and security.

The construction of power Internet of things is an important development direction of power grid enterprises in the future. Big data not only brings economic and social benefits to the power system industry, but also brings many information security problems. Therefore, in the case of accelerating the construction of ubiquitous electric Internet of things, it is urgent to standardize the data security protection in the ubiquitous electric Internet of things environment. By analyzing the characteristics of big data in power system, this paper discusses the security risks faced by big data in power system. Finally, we propose some methods of data security protection based on the defects of big data security in current power system. By building a data security intelligent management and control platform, it can automatically discover and identify the types and levels of data assets, and build a classification and grading information base of dynamic data assets. And through the detection and identification of data labels and data content characteristics, tracking the use of data flow process. So as to realize the monitoring of data security state. By protecting sensitive data against leakage based on the whole life cycle of data, the big data security of power grid informatization can be effectively guaranteed and the safety immunity of power information system can be improved.

Ultra high frequency (UHF) partial discharge detection technology has been widely used in on-line monitoring of electrical equipment, for the influence factors of UHF signal's transfer function is complicated, the calibration of UHF method is still not realized until now. In order to study the calibration influence factors of UHF partial discharge (PD) detector, the discharge mechanism of typical PD defects is analyzed, and use a PD UHF signal simulator with multiple adjustable parameters to simulate types of PD UHF signals of electrical equipment, then performed the relative experimental research in propagation characteristics and Sensor characteristics of UHF signals. It is concluded that the calibration reliability has big differences between UHF signal energy and discharge capacity of different discharge source. The calibration curve of corona discharge and suspended discharge which can representation the severity of equipment insulation defect more accurate, and the calibration curve of internal air gap discharge and dielectric surface discharge is poorer. The distance of UHF signal energy decays to stable period become smaller with increase of frequency, and the decay of UHF signal energy is irrelevant to its frequencies when the measuring angle is changing. The frequency range of measuring UHF signal depends on effective frequency range of measurement sensor, moreover, the gain and standing-wave ratio of sensor and the energy of the received signal manifested same change trend. Therefore, in order to calibration the UHF signal, it is necessary to comprehensive consideration the specific discharge type and measuring condition. The results provide the favorable reference for a further study to build the calibration system of UHF measuring method, and to promote the effective application of UHF method in sensor characteristic fault diagnosis and insulation evaluation of electrical equipment.

Fully securing networks from remote attacks is recognized by the IT industry as a critical and imposing challenge. Even highly secure systems remain vulnerable to attacks and advanced persistent threats. Air-gapped networks may be secure from remote attack. One-way flows are a novel approach to improving the security of telemetry for critical infrastructure, retaining some of the benefits of interconnectivity whilst maintaining a level of network security analogous to that of unconnected devices. Simple and inexpensive techniques can be used to provide this unidirectional security, removing the risk of remote attack from a range of potential targets and subnets. The application of one-way networks is demonstrated using IEEE compliant PMU data streams as a case study. Scalability is demonstrated using SDN techniques. Finally, these techniques are combined, demonstrating a node which can be secured from remote attack, within defined limitations.

Register Transfer Level (RTL) locking seeks to prevent intellectual property (IP) theft of a design by locking the RTL description that functions correctly on the application of a key. This paper evaluates the security of a state-of-the-art RTL locking scheme using a satisfiability modulo theories (SMT) based algorithm to retrieve the secret key. The attack first obtains the high-level behavior of the locked RTL, and then use an SMT based formulation to find so-called distinguishing input patterns (DIP)1 The attack methodology has two main advantages over the gate-level attacks. First, since the attack handles the design at the RTL, the method scales to large designs. Second, the attack does not apply separate unlocking strategies for the combinational and sequential parts of a design; it handles both styles via a unifying abstraction. We demonstrate the attack on locked RTL generated by TAO [1], a state-of-the-art RTL locking solution. Empirical results show that we can partially or completely break designs locked by TAO.

Recently, there has been a dramatic increase in cyber attacks around the globe. Hundreds of 0day vulnerabilities on different platforms are discovered by security researchers worldwide. The attack vectors are becoming more and more difficult to be discovered by any anti threat detection engine. Inorder to bypass these smart detection mechanisms, attackers now started carrying out attacks at extremely low level where no threat inspection units are present. This makes the attack more stealthy with increased success rate and almost zero detection rate. A best case example for this scenario would be attacks like Meltdown and Spectre that targeted the modern processors to steal information by exploiting out-of-order execution feature in modern processors. These types of attacks are incredibly hard to detect and patch. Even if a patch is released, a wide range of normal audience are unaware of this both the vulnerability and the patch. This paper describes one such low level attacks that involves the process of reverse engineering firmwares and manually backdooring them with several linux utilities. Also, compromising a real world WiFi router with the manually backdoored firmware and attaining reverse shell from the router is discussed. The WiFi routers are almost everywhere especially in public places. Firmwares are responsible for controlling the routers. If the attacker manipulates the firmware and gains control over the firmware installed in the router, then the attacker can get a hold of the network and perform various MITM attacks inside the network with the help of the router.

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.

Service providers typically utilize Web APIs to enable the sharing of tenant data and resources with numerous third party web, cloud, and mobile applications. Security mechanisms such as OAuth 2.0 and API keys are commonly applied to manage authorization aspects of such integrations. However, these mechanisms impose functional and security drawbacks both for service providers and their users due to their static design, coarse and context insensitive capabilities, and weak interoperability. Implementing secure, feature-rich, and flexible data sharing services still poses a challenge that many providers face in the process of opening their interfaces to the public.To address these issues, we design the framework that allows pluggable and transparent externalization of authorization functionality for service providers and flexibility in defining and managing security aspects of resource sharing with third parties for their users. Our solution applies a holistic perspective that considers service descriptions, data fragments, security policies, as well as system interactions and states as an integrated space dynamically exposed and collaboratively accessed by agents residing across organizational boundaries.In this work we present design aspects of our contribution and illustrate its practical implementation by analyzing case scenario involving resource sharing of a popular service.

Compressive sensing (CS) is regarded as one of the promising solutions for IoT data encryption as it achieves simultaneous sampling, compression, and encryption. Theoretical work in the literature has proved that CS provides computational secrecy. It also provides asymptotic perfect secrecy for Gaussian sensing matrix with constraints on input signal. In this paper, we design an attack decoding algorithm based on block compressed sensing decoding algorithm to perform ciphertext-only attack on real-life time series IoT data. It shows that it is possible to retrieve vital information in the plaintext under some conditions. Furthermore, it is also applied to a State-of-the Art CS-based encryption scheme for smart grid, and the power profile is reconstructed using ciphertext-only attack. Additionally, the statistical analysis of Gaussian and Binomial measurements is conducted to investigate the randomness provided by them.

The design of large-scale quantum networks and any future quantum internet will rely on quantum repeaters and how quantum information flows through it. Tasks performed on such networks will go well beyond quantum key distribution and are likely to include quantum remote sensing and distributed quantum computation. In this presentation we will explore the various ways that such networks could be designed to support those advanced tasks. Critical to this will be quantum routing which we should is highly dependent on the repeater architecture. We introduce a quantum quality of service to help characterize the systems performance and shows how it leads interesting network and routing behavior.

Object recognition with the help of outdoor video surveillance cameras is an important task in the context of ensuring the security at enterprises, public places and even private premises. There have long existed systems that allow detecting moving objects in the image sequence from a video surveillance system. Such a system is partially considered in this research. It detects moving objects using a background model, which has certain problems. Due to this some objects are missed or detected falsely. We propose to combine the moving objects detection results with the classification, using a deep neural network. This will allow determining whether a detected object belongs to a certain class, sorting out false detections, discarding the unnecessary ones (sometimes individual classes are unwanted), to divide detected people into the employees in the uniform and all others, etc. The authors perform a network training in the Keras developer-friendly environment that provides for quick building, changing and training of network architectures. The performance of the Keras integration into a video analysis system, using direct Python script execution techniques, is between 6 and 52 ms, while the precision is between 59.1% and 97.2% for different architectures. The integration, made by freezing a selected network architecture with weights, is selected after testing. After that, frozen architecture can be imported into video analysis using the TensorFlow interface for C++. The performance of such type of integration is between 3 and 49 ms. The precision is between 63.4% and 97.8% for different architectures.