Visible to the public Biblio

Found 274 results

Filters: First Letter Of Title is L  [Clear All Filters]
2020
Gaber, C., Vilchez, J. S., Gür, G., Chopin, M., Perrot, N., Grimault, J.-L., Wary, J.-P..  2020.  Liability-Aware Security Management for 5G. 2020 IEEE 3rd 5G World Forum (5GWF). :133—138.

Multi-party and multi-layer nature of 5G networks implies the inherent distribution of management and orchestration decisions across multiple entities. Therefore, responsibility for management decisions concerning end-to-end services become blurred if no efficient liability and accountability mechanism is used. In this paper, we present the design, building blocks and challenges of a Liability-Aware Security Management (LASM) system for 5G. We describe how existing security concepts such as manifests and Security-by-Contract, root cause analysis, remote attestation, proof of transit, and trust and reputation models can be composed and enhanced to take risk and responsibilities into account for security and liability management.

Khan, M., Rehman, O., Rahman, I. M. H., Ali, S..  2020.  Lightweight Testbed for Cybersecurity Experiments in SCADA-based Systems. 2020 International Conference on Computing and Information Technology (ICCIT-1441). :1—5.

A rapid rise in cyber-attacks on Cyber Physical Systems (CPS) has been observed in the last decade. It becomes even more concerning that several of these attacks were on critical infrastructures that indeed succeeded and resulted into significant physical and financial damages. Experimental testbeds capable of providing flexible, scalable and interoperable platform for executing various cybersecurity experiments is highly in need by all stakeholders. A container-based SCADA testbed is presented in this work as a potential platform for executing cybersecurity experiments. Through this testbed, a network traffic containing ARP spoofing is generated that represents a Man in the middle (MITM) attack. While doing so, scanning of different systems within the network is performed which represents a reconnaissance attack. The network traffic generated by both ARP spoofing and network scanning are captured and further used for preparing a dataset. The dataset is utilized for training a network classification model through a machine learning algorithm. Performance of the trained model is evaluated through a series of tests where promising results are obtained.

Inshi, S., Chowdhury, R., Elarbi, M., Ould-Slimane, H., Talhi, C..  2020.  LCA-ABE: Lightweight Context-Aware Encryption for Android Applications. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1—6.

The evolving of context-aware applications are becoming more readily available as a major driver of the growth of future connected smart, autonomous environments. However, with the increasing of security risks in critical shared massive data capabilities and the increasing regulation requirements on privacy, there is a significant need for new paradigms to manage security and privacy compliances. These challenges call for context-aware and fine-grained security policies to be enforced in such dynamic environments in order to achieve efficient real-time authorization between applications and connected devices. We propose in this work a novel solution that aims to provide context-aware security model for Android applications. Specifically, our proposition provides automated context-aware access control model and leverages Attribute-Based Encryption (ABE) to secure data communications. Thorough experiments have been performed and the evaluation results demonstrate that the proposed solution provides an effective lightweight adaptable context-aware encryption model.

Yang, Wenti, Wang, Ruimiao, Guan, Zhitao, Wu, Longfei, Du, Xiaojiang, Guizani, Mohsen.  2020.  A Lightweight Attribute Based Encryption Scheme with Constant Size Ciphertext for Internet of Things. ICC 2020 - 2020 IEEE International Conference on Communications (ICC). :1—6.

The Internet of Things technology has been used in a wide range of fields, ranging from industrial applications to individual lives. As a result, a massive amount of sensitive data is generated and transmitted by IoT devices. Those data may be accessed by a large number of complex users. Therefore, it is necessary to adopt an encryption scheme with access control to achieve more flexible and secure access to sensitive data. The Ciphertext Policy Attribute-Based Encryption (CP-ABE) can achieve access control while encrypting data can match the requirements mentioned above. However, the long ciphertext and the slow decryption operation makes it difficult to be used in most IoT devices which have limited memory size and computing capability. This paper proposes a modified CP-ABE scheme, which can implement the full security (adaptive security) under the access structure of AND gate. Moreover, the decryption overhead and the length of ciphertext are constant. Finally, the analysis and experiments prove the feasibility of our scheme.

Calzavara, S., Focardi, R., Grimm, N., Maffei, M., Tempesta, M..  2020.  Language-Based Web Session Integrity. 2020 IEEE 33rd Computer Security Foundations Symposium (CSF). :107—122.
Session management is a fundamental component of web applications: despite the apparent simplicity, correctly implementing web sessions is extremely tricky, as witnessed by the large number of existing attacks. This motivated the design of formal methods to rigorously reason about web session security which, however, are not supported at present by suitable automated verification techniques. In this paper we introduce the first security type system that enforces session security on a core model of web applications, focusing in particular on server-side code. We showcase the expressiveness of our type system by analyzing the session management logic of HotCRP, Moodle, and phpMyAdmin, unveiling novel security flaws that have been acknowledged by software developers.
Seliem, M., Elgazzar, K..  2020.  LPA-SDP: A Lightweight Privacy-Aware Service Discovery Protocol for IoT Environments. 2020 IEEE 6th World Forum on Internet of Things (WF-IoT). :1–7.
Latest forecasts show that 50 billion devices will be connected to the Internet by 2020. These devices will provide ubiquitous data access and enable smarter interactions in all aspects of our everyday life, including vital domains such as healthcare and battlefields, where privacy is a key requirement. With the increasing adoption of IoT and the explosion of these resource-constrained devices, manual discovery and configuration become significantly challenging. Despite there is a number of resource discovery protocols that can be efficiently used in IoT deployments, none of these protocols provides any privacy consideration. This paper presents LPA-SDT, a novel technique for service discovery that builds privacy into the design from the ground up. Performance evaluation demonstrates that LPA-SDT outperforms state-of-the-art discovery techniques for resource-constrained environments while preserving user and data privacy.
Sanjay, K. N., Shaila, K., Venugopal, K. R..  2020.  LA-ANA based Architecture for Bluetooth Environment. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :222—226.
Wireless Personal Area Network is widely used in day to day life. It might be a static or dynamic environment. As the density of the nodes increases it becomes difficult to handle the situation. The need of multiple sensor node technology in a desired environment without congestion is required. The use of autonomic network provides one such solution. The autonomicity combines the local automate and address agnostic features that controls the congestion resulting in improved throughput, fault tolerance and also with unicast and multicast packets delivery. The algorithm LA based ANA in a Bluetooth based dynamic environment provide 20% increase in throughput compared with LACAS based Wireless Sensor Network. The LA based ANA leads with 10% lesser fault tolerance levels and extended unicast and multi-cast packet delivery.
Huang, Y., Wang, W., Wang, Y., Jiang, T., Zhang, Q..  2020.  Lightweight Sybil-Resilient Multi-Robot Networks by Multipath Manipulation. IEEE INFOCOM 2020 - IEEE Conference on Computer Communications. :2185–2193.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

Tan, R., Khan, N., Guan, L..  2020.  Locality Guided Neural Networks for Explainable Artificial Intelligence. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
In current deep network architectures, deeper layers in networks tend to contain hundreds of independent neurons which makes it hard for humans to understand how they interact with each other. By organizing the neurons by correlation, humans can observe how clusters of neighbouring neurons interact with each other. In this paper, we propose a novel algorithm for back propagation, called Locality Guided Neural Network (LGNN) for training networks that preserves locality between neighbouring neurons within each layer of a deep network. Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other. This method contributes to the domain of Explainable Artificial Intelligence (XAI), which aims to alleviate the black-box nature of current AI methods and make them understandable by humans. Our method aims to achieve XAI in deep learning without changing the structure of current models nor requiring any post processing. This paper focuses on Convolutional Neural Networks (CNNs), but can theoretically be applied to any type of deep learning architecture. In our experiments, we train various VGG and Wide ResNet (WRN) networks for image classification on CIFAR100. In depth analyses presenting both qualitative and quantitative results demonstrate that our method is capable of enforcing a topology on each layer while achieving a small increase in classification accuracy.
Abdelaal, M., Karadeniz, M., Dürr, F., Rothermel, K..  2020.  liteNDN: QoS-Aware Packet Forwarding and Caching for Named Data Networks. 2020 IEEE 17th Annual Consumer Communications Networking Conference (CCNC). :1–9.
Recently, named data networking (NDN) has been introduced to connect the world of computing devices via naming data instead of their containers. Through this strategic change, NDN brings several new features to network communication, including in-network caching, multipath forwarding, built-in multicast, and data security. Despite these unique features of NDN networking, there exist plenty of opportunities for continuing developments, especially with packet forwarding and caching. In this context, we introduce liteNDN, a novel forwarding and caching strategy for NDN networks. liteNDN comprises a cooperative forwarding strategy through which NDN routers share their knowledge, i.e. data names and interfaces, to optimize their packet forwarding decisions. Subsequently, liteNDN leverages that knowledge to estimate the probability of each downstream path to swiftly retrieve the requested data. Additionally, liteNDN exploits heuristics, such as routing costs and data significance, to make proper decisions about caching normal as well as segmented packets. The proposed approach has been extensively evaluated in terms of the data retrieval latency, network utilization, and the cache hit rate. The results showed that liteNDN, compared to conventional NDN forwarding and caching strategies, achieves much less latency while reducing the unnecessary traffic and caching activities.
Soni, G., Sudhakar, R..  2020.  A L-IDS against Dropping Attack to Secure and Improve RPL Performance in WSN Aided IoT. 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). :377—383.

In the Internet of Things (IoT), it is feasible to interconnect networks of different devices and all these different devices, such as smartphones, sensor devices, and vehicles, are controlled according to a particular user. These different devices are delivered and accept the information on the network. This thing is to motivate us to do work on IoT and the devices used are sensor nodes. The validation of data delivery completely depends on the checks of count data forwarding in each node. In this research, we propose the Link Hop Value-based Intrusion Detection System (L-IDS) against the blackhole attack in the IoT with the assist of WSN. The sensor nodes are connected to other nodes through the wireless link and exchange data routing, as well as data packets. The LHV value is identified as the attacker's presence by integrating the data delivery in each hop. The LHV is always equivalent to the Actual Value (AV). The RPL routing protocol is used IPv6 to address the concept of routing. The Routing procedure is interrupted by an attacker by creating routing loops. The performance of the proposed L-IDS is compared to the RPL routing security scheme based on existing trust. The proposed L-IDS procedure is validating the presence of the attacker at every source to destination data delivery. and also disables the presence of the attacker in the network. Network performance provides better results in the existence of a security scheme and also fully represents the inoperative presence of black hole attackers in the network. Performance metrics show better results in the presence of expected IDS and improve network reliability.

Arias, Orlando, Sullivan, Dean, Shan, Haoqi, Jin, Yier.  2020.  LAHEL: Lightweight Attestation Hardening Embedded Devices using Macrocells. 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST). :305—315.

In recent years, we have seen an advent in software attestation defenses targeting embedded systems which aim to detect tampering with a device's running program. With a persistent threat of an increasingly powerful attacker with physical access to the device, attestation approaches have become more rooted into the device's hardware with some approaches even changing the underlying microarchitecture. These drastic changes to the hardware make the proposed defenses hard to apply to new systems. In this paper, we present and evaluate LAHEL as the means to study the implementation and pitfalls of a hardware-based attestation mechanism. We limit LAHEL to utilize existing technologies without demanding any hardware changes. We implement LAHEL as a hardware IP core which interfaces with the CoreSight Debug Architecture available in modern ARM cores. We show how LAHEL can be integrated to system on chip designs allowing for microcontroller vendors to easily add our defense into their products. We present and test our prototype on a Zynq-7000 SoC, evaluating the security of LAHEL against powerful time-of-check-time-of-use (TOCTOU) attacks, while demonstrating improved performance over existing attestation schemes.

Huo, Dongdong, Wang, Yu, Liu, Chao, Li, Mingxuan, Wang, Yazhe, Xu, Zhen.  2020.  LAPE: A Lightweight Attestation of Program Execution Scheme for Bare-Metal Systems. 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :78—86.

Unlike traditional processors, Internet of Things (IoT) devices are short of resources to incorporate mature protections (e.g. MMU, TrustZone) against modern control-flow attacks. Remote (control-flow) attestation is fast becoming a key instrument in securing such devices as it has proven the effectiveness on not only detecting runtime malware infestation of a remote device, but also saving the computing resources by moving the costly verification process away. However, few control-flow attestation schemes have been able to draw on any systematic research into the software specificity of bare-metal systems, which are widely deployed on resource-constrained IoT devices. To our knowledge, the unique design patterns of the system limit implementations of such expositions. In this paper, we present the design and proof-of-concept implementation of LAPE, a lightweight attestation of program execution scheme that enables detecting control-flow attacks for bare-metal systems without requiring hardware modification. With rudimentary memory protection support found in modern IoT-class microcontrollers, LAPE leverages software instrumentation to compartmentalize the firmware functions into several ”attestation compartments”. It then continuously tracks the control-flow events of each compartment and periodically reports them to the verifier. The PoC of the scheme is incorporated into an LLVM-based compiler to generate the LAPE-enabled firmware. By taking experiments with several real-world IoT firmware, the results show both the efficiency and practicality of LAPE.

Olaimat, M. Al, Lee, D., Kim, Y., Kim, J., Kim, J..  2020.  A Learning-based Data Augmentation for Network Anomaly Detection. 2020 29th International Conference on Computer Communications and Networks (ICCCN). :1–10.
While machine learning technologies have been remarkably advanced over the past several years, one of the fundamental requirements for the success of learning-based approaches would be the availability of high-quality data that thoroughly represent individual classes in a problem space. Unfortunately, it is not uncommon to observe a significant degree of class imbalance with only a few instances for minority classes in many datasets, including network traffic traces highly skewed toward a large number of normal connections while very small in quantity for attack instances. A well-known approach to addressing the class imbalance problem is data augmentation that generates synthetic instances belonging to minority classes. However, traditional statistical techniques may be limited since the extended data through statistical sampling should have the same density as original data instances with a minor degree of variation. This paper takes a learning-based approach to data augmentation to enable effective network anomaly detection. One of the critical challenges for the learning-based approach is the mode collapse problem resulting in a limited diversity of samples, which was also observed from our preliminary experimental result. To this end, we present a novel "Divide-Augment-Combine" (DAC) strategy, which groups the instances based on their characteristics and augments data on a group basis to represent a subset independently using a generative adversarial model. Our experimental results conducted with two recently collected public network datasets (UNSW-NB15 and IDS-2017) show that the proposed technique enhances performances up to 21.5% for identifying network anomalies.
Ye, S., Feigh, K., Howard, A..  2020.  Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :1186—1189.

Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device.

Shen, Chao.  2020.  Laser-based high bit-rate visible light communications and underwater optical wireless network. 2020 Photonics North (PN). :1–1.
This talk presents an overview of the latest visible light communication (VLC) and underwater wireless optical communication (UWOC) research and development from the device to the system level. The utilization of laser-based devices and systems for LiFi and underwater Internet of Things (IoT) has been discussed.
Lyu, L..  2020.  Lightweight Crypto-Assisted Distributed Differential Privacy for Privacy-Preserving Distributed Learning. 2020 International Joint Conference on Neural Networks (IJCNN). :1–8.
The appearance of distributed learning allows multiple participants to collaboratively train a global model, where instead of directly releasing their private training data with the server, participants iteratively share their local model updates (parameters) with the server. However, recent attacks demonstrate that sharing local model updates is not sufficient to provide reasonable privacy guarantees, as local model updates may result in significant privacy leakage about local training data of participants. To address this issue, in this paper, we present an alternative approach that combines distributed differential privacy (DDP) with a three-layer encryption protocol to achieve a better privacy-utility tradeoff than the existing DP-based approaches. An unbiased encoding algorithm is proposed to cope with floating-point values, while largely reducing mean squared error due to rounding. Our approach dispenses with the need for any trusted server, and enables each party to add less noise to achieve the same privacy and similar utility guarantees as that of the centralized differential privacy. Preliminary analysis and performance evaluation confirm the effectiveness of our approach, which achieves significantly higher accuracy than that of local differential privacy approach, and comparable accuracy to the centralized differential privacy approach.
Wu, Shanglun, Yuan, Yujie, Kar, Pushpendu.  2020.  Lightweight Verification and Fine-grained Access Control in Named Data Networking Based on Schnorr Signature and Hash Functions. 2020 IEEE 20th International Conference on Communication Technology (ICCT). :1561–1566.
Named Data Networking (NDN) is a new kind of architecture for future Internet, which is exactly satisfied with the rapidly increasing mobile requirement and information-depended applications that dominate today's Internet. However, the current verification-data accessed system is not safe enough to prevent data leakage because no strongly method to resist any device or user to access it. We bring up a lightweight verification based on hash functions and a fine-grained access control based on Schnorr Signature to address the issue seamlessly. The proposed scheme is scalable and protect data confidentiality in a NDN network.
Chen, J., Lin, X., Shi, Z., Liu, Y..  2020.  Link Prediction Adversarial Attack Via Iterative Gradient Attack. IEEE Transactions on Computational Social Systems. 7:1081–1094.
Increasing deep neural networks are applied in solving graph evolved tasks, such as node classification and link prediction. However, the vulnerability of deep models can be revealed using carefully crafted adversarial examples generated by various adversarial attack methods. To explore this security problem, we define the link prediction adversarial attack problem and put forward a novel iterative gradient attack (IGA) strategy using the gradient information in the trained graph autoencoder (GAE) model. Not surprisingly, GAE can be fooled by an adversarial graph with a few links perturbed on the clean one. The results on comprehensive experiments of different real-world graphs indicate that most deep models and even the state-of-the-art link prediction algorithms cannot escape the adversarial attack, such as GAE. We can benefit the attack as an efficient privacy protection tool from the link prediction of unknown violations. On the other hand, the adversarial attack is a robust evaluation metric for current link prediction algorithms of their defensibility.
Liu, Xinghua, Bai, Dandan, Jiang, Rui.  2020.  Load Frequency Control of Multi-area Power Systems under Deception Attacks*. 2020 Chinese Automation Congress (CAC). :3851–3856.
This paper investigated the sliding mode load frequency control (LFC) for an multi-area power system (MPS) under deception attacks (DA). A Luenberger observer is designed to obtain the state estimate of MPS. By using the Lyapunov-Krasovskii method, a sliding mode surface (SMS) is designed to ensure the stability. Then the accessibility analysis ensures that the trajectory of the MPS can reach the specified SMS. Finally, the serviceability of the method is explained by providing a case study.
Shi, W., Liu, S., Zhang, J., Zhang, R..  2020.  A Location-aware Computation Offloading Policy for MEC-assisted Wireless Mesh Network. 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). :53–58.
Mobile edge computing (MEC), an emerging technology, has the characteristics of low latency, mobile energy savings, and context-awareness. As a type of access network, wireless mesh network (WMN) has gained wide attention due to its flexible network architecture, low deployment cost, and self-organization. The combination of MEC and WMN can solve the shortcomings of traditional wireless communication such as storage capacity, privacy, and security. In this paper, we propose a location-aware (LA) algorithm to cognize the location and a location-aware offloading policy (LAOP) algorithm considering the energy consumption and time delay. Simulation results show that the proposed LAOP algorithm can obtain a higher completion rate and lower average processing delay compared with the other two methods.
Naderi, Pooria Taghizadeh, Taghiyareh, Fattaneh.  2020.  LookLike: Similarity-based Trust Prediction in Weighted Sign Networks. 2020 6th International Conference on Web Research (ICWR). :294–298.
Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.
Chakravarty, S., Hopkins, A..  2020.  LoRa Mesh Network with BeagleBone Black. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :306–311.
This paper investigates the use of BeagleBone Black Wireless single-board Linux computers with Long Range (LoRa) transceivers to send and receive information in a mesh network while one of the transmitting/receiving nodes is acting as a relay in the system. An experiment is conducted to examine how long each LoRa node needed to learn the transmission intervals of any other transmitting nodes on the network and to synchronize with the other nodes prior to transmission. The spread factor, bandwidth, and coding rate are all varied for a total of 18 different combinations. A link to the Python code used on the BeagleBone Black is provided at the end of this paper. The best parameter combinations for each individual node and for the system as a whole is investigated. Additional experiments and applications of this technology are explored in the conclusions.
Akand, Tawhida, Islam, Md Jahirul, Kaysir, Md Rejvi.  2020.  Low loss hollow core optical fibers combining lattice and negative curvature structures. 2020 IEEE Region 10 Symposium (TENSYMP). :698—701.
Negative curvature hollow core fibers (NC-HCFs) realize great research attention due to their comparatively low losses with simplified design and fabrication simplicity. Recently, revolver type fibers that combine the NC-HCF and conventional lattice structured photonic crystal fiber (PCF) have opened up a new era in communications due to their low loss, power confinement capacity, and multi-bandwidth applications. In this study, we present a customized optical fiber design that comprises the PCF with the NC-HCF to get lowest confinement loss. Extensive numerical simulations are performed and a noteworthy low loss of 4.47×10-05dB/m at a wavelength of 0.85 μm has been recorded for the designed fiber, which is almost 4600 times lower than annular revolver type fibers. In addition, a conspicuous low loss transmission bandwidth ranging from 0.6 μm to 1.8 μm has found in this study. This may have potential applications in spectroscopy, material processing, chemical and bio-molecular sensing, security, and industry applications.
2019
Palacio, David N., McCrystal, Daniel, Moran, Kevin, Bernal-Cárdenas, Carlos, Poshyvanyk, Denys, Shenefiel, Chris.  2019.  Learning to Identify Security-Related Issues Using Convolutional Neural Networks. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). :140–144.
Software security is becoming a high priority for both large companies and start-ups alike due to the increasing potential for harm that vulnerabilities and breaches carry with them. However, attaining robust security assurance while delivering features requires a precarious balancing act in the context of agile development practices. One path forward to help aid development teams in securing their software products is through the design and development of security-focused automation. Ergo, we present a novel approach, called SecureReqNet, for automatically identifying whether issues in software issue tracking systems describe security-related content. Our approach consists of a two-phase neural net architecture that operates purely on the natural language descriptions of issues. The first phase of our approach learns high dimensional word embeddings from hundreds of thousands of vulnerability descriptions listed in the CVE database and issue descriptions extracted from open source projects. The second phase then utilizes the semantic ontology represented by these embeddings to train a convolutional neural network capable of predicting whether a given issue is security-related. We evaluated SecureReqNet by applying it to identify security-related issues from a dataset of thousands of issues mined from popular projects on GitLab and GitHub. In addition, we also applied our approach to identify security-related requirements from a commercial software project developed by a major telecommunication company. Our preliminary results are encouraging, with SecureReqNet achieving an accuracy of 96% on open source issues and 71.6% on industrial requirements.