Biblio

Addressing vulnerability propagation has become a major issue in software ecosystems. Existing approaches hold the promise of detecting widespread vulnerabilities but cannot be applied to verify effectively whether propagated vulnerable code still poses threats. We present OCTOPOCS, which uses a reformed Proof-of-Concept (PoC), to verify whether a vulnerability is propagated. Using context-aware taint analysis, OCTOPOCS extracts crash primitives (the parts used in the shared code area between the original vulnerable software and propagated software) from the original PoC. OCTOPOCS then utilizes directed symbolic execution to generate guiding inputs that direct the execution of the propagated software from the entry point to the shared code area. Thereafter, OCTOPOCS creates a new PoC by combining crash primitives and guiding inputs. It finally verifies the propagated vulnerability using the created PoC. We evaluated OCTOPOCS with 15 real-world C and C++ vulnerable software pairs, with results showing that OCTOPOCS successfully verified 14 propagated vulnerabilities.

The use of gamified learning platforms as a method of introducing cyber security education, training and awareness has risen greatly. With this rise, the availability of platforms to create, host or otherwise provide the challenges that make up the foundation of this education has also increased. In order to identify the best of these platforms, we need a method to compare their feature sets. In this paper, we compare related work on identifying the best platforms for a gamified cyber security learning platform as well as contemporary literature that describes the most needed feature sets for an ideal platform. We then use this to develop a metric for comparing these platforms, before then applying this metric to popular current platforms.

The metal oxide arrester (MOA, shortly) is installed on the line side of the substation, which is the first line of defense for the overvoltage limitation of lightning intrusion wave. In order to deeply limit the switching overvoltage and cancel the closing resistance of the circuit breaker, the arrester is replaced by the controllable metal oxide arrester (CMOA, shortly) in the new technology. The controllable switch of CMOA can be mechanical switch or thyristor switch. Thyristor switches are sensitive to the current and current change rate (di/dt) under lightning intrusion wave. If the switch cannot withstand, appropriate protective measures must be taken to ensure the safe operation of the controllable switch under this working condition. The 1000kV West Beijing to Shijiazhuang UHV AC transmission and transformation expansion project is the first project of pilot application of CMOA. CMOA were installed at both ends of the outgoing branch of Dingtai line I. In order to study the influence of lightning intrusion wave on the controllable switch of CMOA, this paper selected this project to simulate the lightning stroke on the incoming section of Dingtai line I in Beijing West substation in the process of system air closing or single-phase reclosing, and obtained the current and di/dt of the controllable switch through CMOA under this working condition. Then the performances of mechanical and thyristor control switches were checked respectively. The results showed that the mechanical switch could withstand without protective measures. The tolerance of thyristor switch to i and di/dt exceeded the limit value, and measures should be taken to protect and limit it. In this paper, the protection measures of current limiting reactor were given, and the limiting effect of the protection measures was verified by simulation and test. It could fully meet the requirements and ensure the safe operation of thyristor controllable switch.

The need for cyber defense research is growing as more cyber-attacks are directed at critical infrastructure and other sensitive networks. Traditionally, the focus has been on hardening system defenses. However, other techniques are being explored including cyber and psychological deception which aim to negatively impact the cognitive and emotional state of cyber attackers directly through the manipulation of network characteristics. In this study, we present a preliminary analysis of survey data collected following a controlled experiment in which over 130 professional red teamers participated in a network penetration task that included cyber deception and psychological deception manipulations [7]. Thematic and inductive analysis of previously un-analyzed open-ended survey responses revealed factors associated with affective states. These preliminary results are a first step in our analysis efforts and show that there are potentially several distinct dimensions of cyber-behavior that induce negative affective states in cyber attackers, which may serve as potential avenues for supplementing traditional cyber defense strategies.

Model predictive control (MPC) has become one of the most attractive control techniques due to its outstanding dynamic performance for motor drives. Besides, MPC with constant switching frequency (CSF-MPC) maintains the advantages of MPC as well as constant frequency but the selection of weighting factors in the cost function is difficult for CSF-MPC. Fortunately, the application of artificial neural networks (ANN) can accelerate the selection without any additional computation burden. Therefore, this paper designs a specific artificial neural network optimized by genetic algorithm (GA-ANN) to select the optimal weighting factors of CSF-MPC for permanent magnet synchronous motor (PMSM) drives fed by three-level T-type inverter. The key performance metrics like THD and switching frequencies error (ferr) are extracted from simulation and this data are utilized to train and evaluate GA-ANN. The trained GA-ANN model can automatically and precisely select the optimal weighting factors for minimizing THD and ferr under different working conditions of PMSM. Furthermore, the experimental results demonstrate the validation of GA-ANN and robustness of optimal weighting factors under different torque loads. Accordingly, any arbitrary user-defined working conditions which combine THD and ferr can be defined and the optimum weighting factors can be fast and explicitly determined via the trained GA-ANN model.

Protection of an organization's assets and information technology infrastructure is always crucial to any business. Securing and protecting businesses from cybersecurity threats became very challenging during the Covid-19 Pandemic. Organizations suddenly shifted towards remote work to maintain continuity and protecting against new cyber threats became a big concern for most business owners. This research looks into the following areas (i) outlining the shift from In-person to online work risks (ii) determine the cyber-attack type based on the list of 10 most prominent cybersecurity threats during the Covid-19 Pandemic (iii) and design a security policy to securing business continuity.

The electron mobility μ exhibits oscillatory behavior with gate electric field F in an asymmetrically doped double V-shaped AlxGa1-xAs quantum well field effect transistor structure. By changing F, single-double-single subband occupancy of the system is obtained. We show that μ oscillates within double subband occupancy as a function of F near resonance of subband states due to the relocation of subband wave functions between the wells through intersubband effects.

The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.

Digital forensic artifacts aim to provide evidence from digital sources for attributing blame to suspects, assessing their intents, corroborating their statements or alibis, etc. Textual data is a significant source of artifacts, which can take various forms, for instance in the form of communications. E-mails, memos, tweets, and text messages are all examples of textual communications. Complex statistical, linguistic and other scientific procedures can be manually applied to this data to uncover significant clues that point the way to factual information. While expert investigators can undertake this task, there is a possibility that critical information is missed or overlooked. The primary objective of this work is to aid investigators by partially automating the detection of suspicious e-mail deletions. Our approach consists in building a dynamic graph to represent the temporal evolution of communications, and then using a Variational Graph Autoencoder to detect possible e-mail deletions in this graph. Our model uses multiple types of features for representing node and edge attributes, some of which are based on metadata of the messages and the rest are extracted from the contents using natural language processing and text mining techniques. We use the autoencoder to detect missing edges, which we interpret as potential deletions; and to reconstruct their features, from which we emit hypotheses about the topics of deleted messages. We conducted an empirical evaluation of our model on the Enron e-mail dataset, which shows that our model is able to accurately detect a significant proportion of missing communications and to reconstruct the corresponding topic vectors.

The reuse of libraries in software development has become prevalent for improving development efficiency and software quality. However, security vulnerabilities of reused libraries propagated through software project dependency pose a severe security threat, but they have not yet been well studied. In this paper, we present the first large-scale empirical study of project dependencies with respect to security vulnerabilities. We developed PDGraph, an innovative approach for analyzing publicly known security vulnerabilities among numerous project dependencies, which provides a new perspective for assessing security risks in the wild. As a large-scale software collection in dependency, we find 337,415 projects and 1,385,338 dependency relations. In particular, PDGraph generates a project dependency graph, where each node is a project, and each edge indicates a dependency relationship. We conducted experiments to validate the efficacy of PDGraph and characterized its features for security analysis. We revealed that 1,014 projects have publicly disclosed vulnerabilities, and more than 67,806 projects are directly dependent on them. Among these, 42,441 projects still manifest 67,581 insecure dependency relationships, indicating that they are built on vulnerable versions of reused libraries even though their vulnerabilities are publicly known. During our eight-month observation period, only 1,266 insecure edges were fixed, and corresponding vulnerable libraries were updated to secure versions. Furthermore, we uncovered four underlying dependency risks that can significantly reduce the difficulty of compromising systems. We conducted a quantitative analysis of dependency risks on the PDGraph.

In this paper, we propose a new provably secure cryptosystem for two party communication that provides security in the face of new technological breakthroughs. Most of the practical cryptosystems in use today can be breached in the future with new sophisticated methods. This jeopardizes the security of older but highly confidential messages. Our protocol is based on the bounded storage model first introduced in [1]. The protocol is secure as long as there is bound on the storage, however large it may be. We also suggest methods to extend the protocol to unbounded storage models where access to adversary is limited. Our protocol is a substantial improvement over previously known protocols and uses short key and optimal number of public random bits size of which is independent of message length. The smaller and constant length of key and public random string makes the scheme more practical. The protocol generates key using elements of the additive group \$\textbackslashtextbackslashmathbbZ\_\textbackslashtextbackslashmathrmn\$. Our protocol is very generalized and the protocol in [1] is a special case of our protocol. Our protocol is a step forward in making provably secure cryptosystems practical. An important open problem raised in [2] was designing an algorithm with short key and size of public random string \$O(\textbackslashtextbackslashmathcalB)\$ where \$\textbackslashtextbackslashmathcalB\$ bounds the storage of adversary. Our protocol satisfies the conditions and is easy to implement.

In Mobile Ad-hoc Network based Internet of things (MANET-IoT), nodes are mobile, infrastructure less, managed and organized by themselves that have important role in many areas such as Mobile Computing, Military Sector, Sensor Networks Commercial Sector, medical etc. One major problem in MANET based IoT is security because nodes are mobile, having not any central administrator and are also not reliable. So, MANET-IoT is more defenseless to denial-of-service attacks for-example Blackhole, Wormhole, Gray-hole etc. To compare the performance of network under different attacks for checking which attack is more affecting the performance of network, we implemented Blackhole and Wormhole attack by modifying AODV routing protocol in NS-3. After preprocessing of data that is obtained by using Flow-monitor module, we calculated performance parameters such as Average Throughput, Average Packet Delivery Ratio, Average End to End Delay, Average Jitter-Sum and compared it with no. of nodes in MANET-IoT network. Throughput and goodput performance of each node in the network is also calculated by using Trace metric module and compared with each node in the network. This approach is also very helpful for further research in MANET-IoT Security.

With an increasing number of adversarial attacks against Industrial Control Systems (ICS) networks, enhancing the security of such systems is invaluable. Although attack prevention strategies are often in place, protecting against all attacks, especially zero-day attacks, is becoming impossible. Intrusion Detection Systems (IDS) are needed to detect such attacks promptly. Machine learning-based detection systems, especially deep learning algorithms, have shown promising results and outperformed other approaches. In this paper, we study the efficacy of a deep learning approach, namely, Multi Layer Perceptron (MLP), in detecting abnormal behaviors in ICS network traffic. We focus on very common reconnaissance attacks in ICS networks. In such attacks, the adversary focuses on gathering information about the targeted network. To evaluate our approach, we compare MLP with isolation Forest (i Forest), a statistical machine learning approach. Our proposed deep learning approach achieves an accuracy of more than 99% while i Forest achieves only 75%. This helps to reinforce the promise of using deep learning techniques for anomaly detection.

In protocol verification we observe a wide spectrum from fully automated methods to interactive theorem proving with proof assistants like Isabelle/HOL. The latter provide overwhelmingly high assurance of the correctness, which automated methods often cannot: due to their complexity, bugs in such automated verification tools are likely and thus the risk of erroneously verifying a flawed protocol is non-negligible. There are a few works that try to combine advantages from both ends of the spectrum: a high degree of automation and assurance. We present here a first step towards achieving this for a more challenging class of protocols, namely those that work with a mutable long-term state. To our knowledge this is the first approach that achieves fully automated verification of stateful protocols in an LCF-style theorem prover. The approach also includes a simple user-friendly transaction-based protocol specification language embedded into Isabelle, and can also leverage a number of existing results such as soundness of a typed model

The integration of advanced information, communication and data analytic technologies has transformed the traditional grid into an intelligent bidirectional system that can automatically adapt its services for utilities or consumers' needs. However, this change raises new privacy-related challenges. Privacy leakage has become a severe issue in the grid paradigm as adversaries run malicious analytics to identify the system's internal insight or use it to interrupt grids' operation by identifying real-time demand-based supply patterns. As a result, current grid authorities require an integrated mechanism to improve the system's sensitive data's privacy preservation. To this end, we present a multilayered smart grid architecture by characterizing the privacy issues that occur during data sharing, aggregation, and publishing by individual grid end nodes. Based on it, we quantify the nodes preferred privacy requirements. We further introduce personalized differential privacy (PDP) scheme based on trust distance in our proposed framework to provide the system with the added benefit of a user-specific privacy guarantee to eliminate differential privacy's limitation that allows the same level of privacy for all data providers. Lastly, we conduct extensive experimental analysis on a real-world grid dataset to illustrate that our proposed method is efficient enough to provide privacy preservation on sensitive smart grid data.

We consider that in order to improve the utilization rate of spectrum resources and the security rate of unmanned aerial vehicle (UAV) Communication system, a secure transmission scheme of UAV relay assisted cognitive radio network (CRN) is proposed. In the presence of primary users and eavesdroppers, the UAV acts as the decoding and forwarding mobile relay to assist the secure transmission from the source node to the legitimate destination node. This paper optimizes the flight trajectory and transmission power of the UAV relay to maximize the security rate. Since the design problem is nonconvex, the original problem is approximated to a convex constraint by constructing a surrogate function with nonconvex constraints, and an iterative algorithm based on continuous convex approximation is used to solve the problem. The simulation results show that the algorithm can effectively improve the average security rate of the secondary system and successfully optimize the UAV trajectory.

Non-orthogonal multiple access (NOMA) is emerging as a popular radio access technique to serve multiple users under the same resource block to improve spectral efficiency in 5G and 6G communication. But the resource sharing in NOMA causes concerns on data security. Since power domain NOMA exploits the difference in channel properties for bandwidth-efficient communication, it is feasible to ensure data confidentiality in NOMA communication through physical layer security techniques. In this work, we propose to ensure resistance against internal eavesdropping in NOMA communication through a secret key derived from channel randomness. A unique secret key is derived from the channel of each NOMA user; which is used to randomize the data of the respective user before superposition coding (SC) to prevent internal eavesdropping. The simulation results show that the proposed system provides very good security against internal eavesdropping in NOMA.

Among many application domains of machine learning in real-world settings, cyber security can benefit from more automated techniques to combat sophisticated adversaries. Modern network intrusion detection systems leverage machine learning models on network logs to proactively detect cyber attacks. However, the risk of adversarial attacks against machine learning used in these cyber settings is not fully explored. In this paper, we investigate poisoning attacks at training time against machine learning models in constrained cyber environments such as network intrusion detection; we also explore mitigations of such attacks based on training data sanitization. We consider the setting of poisoning availability attacks, in which an attacker can insert a set of poisoned samples at training time with the goal of degrading the accuracy of the deployed model. We design a white-box, realizable poisoning attack that reduced the original model accuracy from 95% to less than 50 % by generating mislabeled samples in close vicinity of a selected subset of training points. We also propose a novel Nested Training method as a defense against these attacks. Our defense includes a diversified ensemble of classifiers, each trained on a different subset of the training set. We use the disagreement of the classifiers' predictions as a data sanitization method, and show that an ensemble of 10 SVM classifiers is resilient to a large fraction of poisoning samples, up to 30% of the training data.

A code smells detection rule is a combination of metrics with their corresponding crisp thresholds and labels. The goal of this paper is to deal with metrics' thresholds uncertainty; as usually such thresholds could not be exactly determined to judge the smelliness of a particular software class. To deal with this issue, we first propose to encode each metric value into a binary possibility distribution with respect to a threshold computed from a discretization technique; using the Possibilistic C-means classifier. Then, we propose ADIPOK-UMT as an evolutionary algorithm that evolves a population of PK-NN classifiers for the detection of smells under thresholds' uncertainty. The experimental results reveal that the possibility distribution-based encoding allows the implicit weighting of software metrics (features) with respect to their computed discretization thresholds. Moreover, ADIPOK-UMT is shown to outperform four relevant state-of-art approaches on a set of commonly adopted benchmark software systems.

A trusted execution environment (TEE) such as Intel Software Guard Extension (SGX) runs attestation to prove to a data owner the integrity of the initial state of an enclave, including the program to operate on her data. For this purpose, the data-processing program is supposed to be open to the owner or a trusted third party, so its functionality can be evaluated before trust being established. In the real world, however, increasingly there are application scenarios in which the program itself needs to be protected (e.g., proprietary algorithm). So its compliance with privacy policies as expected by the data owner should be verified without exposing its code.To this end, this paper presents DEFLECTION, a new model for TEE-based delegated and flexible in-enclave code verification. Given that the conventional solutions do not work well under the resource-limited and TCB-frugal TEE, we come up with a new design inspired by Proof-Carrying Code. Our design strategically moves most of the workload to the code generator, which is responsible for producing easy-to-check code, while keeping the consumer simple. Also, the whole consumer can be made public and verified through a conventional attestation. We implemented this model on Intel SGX and demonstrate that it introduces a very small part of TCB. We also thoroughly evaluated its performance on micro-and macro-benchmarks and real-world applications, showing that the design only incurs a small overhead when enforcing several categories of security policies.

Order-preserving encryption (OPE) plays an important role in securing outsourced databases. OPE schemes can be either Stateless or Stateful. Stateful schemes can achieve the ideal security of order-preserving encryption, i.e., “reveal no information about the plaintexts besides order.” However, comparing to stateless schemes, stateful schemes require maintaining some state information locally besides encryption keys and the ciphertexts are mutable. On the other hand, stateless schemes only require remembering encryption keys and thus is more efficient. It is a common belief that stateless schemes cannot provide the same level of security as stateful ones because stateless schemes reveal the relative distance among their corresponding plaintext. In real world applications, such security defects may lead to the leakage of statistical and sensitive information, e.g., the data distribution, or even negates the whole encryption. In this paper, we propose a practical and secure stateless order-preserving encryption scheme. With prior knowledge of the data to be encrypted, our scheme can achieve IND-CCPA (INDistinguishability under Committed ordered Chosen Plaintext Attacks) security for static data set. Though the IND-CCPA security can't be met for dynamic data set, our new scheme can still significantly improve the security in real world applications. Along with the encryption scheme, in this paper we also provide methods to eliminate access pattern leakage in communications and thus prevents some common attacks to OPE schemes in practice.

Ad Hoc Network is wireless networks that get more attention from past to present. Mobile ad hoc network (MANET) is one of the types of ad hoc networks, it deployed rapidly because it infrastructure-less. A node in a mobile ad hoc network communicates through wireless links without wired channels. When source nodes want to communicate with the destination outside its transmission range it uses multi-hop mechanisms. The intermediate node forwards the data packet to the next node until the data packet reaches its destination. Due wireless links and lack of centralized administration device, mobile ad hoc network is more vulnerable for security attacks. The rushing attack is one of the most dangerous attacks in the on-demand routing protocol of mobile ad hoc networks. Rushing attack highly transmits route request with higher transmission power than the genuine nodes and become participate between source and destination nodes, after that, it delays or drop actual data pass through it. In this study, the researcher incorporates rushing attack in one of the most commonly used mobile ad hoc network routing protocols namely Ad hoc on-demand multipath distance vector and provides a rushing attack prevention method based on the time threshold value and random route selection. Based on the time RREQ arrives a node takes a decision, if the RREQ packet arrives before threshold value, the RREQ packet consider as came from an attacker and discarded else RREQ packet received then randomly select RREQ to forward. In this study performance metrics like packet delivery ratio, end-to-end delay and throughput have been evaluated using Network simulation (NS-2.35). As a result of simulation shows newly proposed prevention mechanism improves network performance in all cases than the network under attacker. For example, the average packet delivery ratio enhanced from 54.37% to 97.69%, throughput increased from 20.84bps to 33.06bpsand the average delay decreased from 1147.22ms to 908.04ms. It is concluded that the new proposed techniques show improvement in all evaluated performance metrics.

Visible light communications (VLC) have been the focus of many recent investigations due to its potential for transmitting data at a higher bitrate than conventional communication systems. Alongside the advantages of being energy efficient through the use of LEDs (Light Emitting Diodes), it is imperative that these systems also take in consideration privacy and security measures available. This work highlights the technical aspects of a typical 16-QAM (Quadrature Amplitude Modulation) VLC system incorporating an enhanced privacy feature using an hyperchaotic map to scramble the symbols. The results obtained in this study showed a low dispersion symbol constellation while communicating at 100 Baud and with a 1 m link. Using the measured EVM (Error Vector Magnitude) of the constellation, the BER (Bit Error Rate) of this system was estimated to be bellow 10−12 which is lower than the threshold limit of 3.8.10−3 that corresponds to the 7% hard-decision forward error correction (HD- FEC) for optimal transmission, showing that this technique can be implemented with higher bitrates and with a higher modulation index.

We perform the first post EU General Data Protection Regulation (GDPR) usability study of privacy policies for mobile apps. For our analysis, we collect a dataset of historical (prior to GDPR implementation in May 2018) and contemporary privacy policies in different categories. In contrast to the common belief, that after the GDPR most of the privacy policies are easier to understand, our analysis shows that this is not so.

Mobile edge computing (MEC) emerges recently to help process the computation-intensive and delay-sensitive applications of resource limited mobile devices in support of MEC servers. Due to the wireless offloading, MEC faces many security challenges, like eavesdropping and privacy leakage. The anti-eavesdropping offloading or privacy preserving offloading have been studied in existing researches. However, both eavesdropping and privacy leakage may happen in the meantime in practice. In this paper, we propose a privacy preserved secure offloading scheme aiming to minimize the energy consumption, where the location privacy, usage pattern privacy and secure transmission against the eavesdropper are jointly considered. We formulate this problem as a constrained Markov decision process (CMDP) with the constraints of secure offloading rate and pre-specified privacy level, and solve it with reinforcement learning (RL). It can be concluded from the simulation that this scheme can save the energy consumption as well as improve the privacy level and security of the mobile device compared with the benchmark scheme.