Abstract A knowledge graph (KG) is a structured form of knowledge describing real‐world entities, properties and relationships as a graph. Question answering over knowledge graphs (KGQA) allows people to ask questions in natural language and extract answers from KG accurately and more quickly. The main task of a KGQA is to convert a natural language query to the corresponding structured query form like SPARQL. However, generating the precise SPARQL query from a question is challenging and highly error‐prone. Here we propose a question‐answering framework that uses KG to answer simple questions without using SPARQL. Question classification, dependency parsing, entity linking, BERT‐based relation finding and answer extraction constitute the main modules of the approach. We have used the DBpedia as the KG and tested the end‐to‐end system with a subset of QALD‐4, LC‐QuAD and SimpleQuestions datasets. Results show considerable improvement compared to other approaches in terms of F1‐score.

The use of encrypted data, the diversity of new protocols, and the surge in the number of malicious activities worldwide have posed new challenges for intrusion detection systems (IDS). In this scenario, existing signature-based IDS are not performing well. Various researchers have proposed machine learning-based IDS to detect unknown malicious activities based on behaviour patterns. Results have shown that machine learning-based IDS perform better than signature-based IDS (SIDS) in identifying new malicious activities in the communication network. In this paper, the authors have analyzed the IDS dataset that contains the most current common attacks and evaluated the performance of network intrusion detection systems by adopting two data resampling techniques and 10 machine learning classifiers. It has been observed that the top three IDS models—KNeighbors, XGBoost, and AdaBoost—outperform binary-class classification with 99.49%, 99.14%, and 98.75% accuracy, and XGBoost, KNneighbors, and GaussianNB outperform in multi-class classification with 99.30%, 98.88%, and 96.66% accuracy.

Emotion recognition from the speech is a key challenging task and an active area of research in effective Human-Computer Interaction (HCI). Though many deep learning and machine learning approaches have been proposed to tackle the problem, they lack in both accuracy and learning robust representations agnostic to changes in voice. Additionally, there is a lack of sufficient labelled speech data for bigger models. To overcome these issues, we propose supervised contrastive learning with transformers for the task of speech emotion recognition (ScSer) and evaluate it on different standard datasets. Further, we experiment the supervised contrastive setting with different augmentations from WavAugment library and some custom augmentations. Finally, we propose a custom augmentation random cyclic shift with which ScSer outperforms other competitive methods and produce a state of the art accuracy of 96% on RAVDESS dataset with 7600 samples (Big-Ravdess) and a 2-4% boost over other wav2vec methods.

Securely and unobtrusively authenticating a user is an important problem given the pervasiveness of smartphones. Existing approaches, such as password, fingerprints, or facial recognition, are vulnerable to various attacks, and/or degrade usability. To overcome this problem, we propose Shakespeer, which differentiates users based on uniqueness in the propagation of haptic vibrations through hand, forearm muscles and bones. These vibrations are generated by the user’s smartphone and sensed by their smartphone and smartwatch. The unobtrusive haptic vibrational response makes this biometric feature hard to be replicated. Meanwhile, it provides the co-presence detection function, which allows the devices to confirm the co-presence on the user’s body. We implement Shakespeer using smartphones and smartwatches and tested it across 32 subjects under real-world settings. From our preliminary exploratory evaluation, Shakespeer achieves an equal error rate (EER) of 0.59 %, demonstrating its feasibility.

Second factor (2FA) or passwordless authentication based on notifications pushed to a user's personal device (e.g., a phone) that the user can simply approve (or deny) has become widely popular due to its convenience. In this paper, we show that the effortlessness of this approach gives rise to a fundamental design vulnerability. The vulnerability stems from the fact that the notification, as shown to the user, is not uniquely bound to the user's login session running through the browser, and thus if two notifications are sent around the same time (one for the user's session and one for an attacker's session), the user may not be able to distinguish between the two, likely ending up accepting the notification of the attacker's session.

In this paper, we highlight a fundamental vulnerability associated with the widely adopted “Just Tap” push-based authentication in the face of a concurrency attack, and propose the method REPLICATE, a redesign to counter this vulnerability. In the concurrency attack, the attacker launches the login session at the same time the user initiates a session, and the user may be fooled, with high likelihood, into accepting the push notification which corresponds to the attacker's session, thinking it is their own. The attack stems from the fact that the login notification is not explicitly mapped to the login session running on the browser in the Just Tap approach. REPLICATE attempts to address this fundamental flaw by having the user approve the login attempt by replicating the information presented on the browser session over to the login notification, such as by moving a key in a particular direction, choosing a particular shape, etc. We report on the design and a systematic usability study of REPLICATE. Even without being aware of the vulnerability, in general, participants placed multiple variants of REPLICATE in competition to the Just Tap and fairly above PIN-based authentication.

This paper envisions cyber-farm systems along the lines of cyber-physical systems. It is imperative for corporates and nations to maintain health of the crops to ensure food security. In order to avoid any adversarial attack on agriculture sensors in farms, we propose a collaborative sensing based authentication protocol. It assures that the spoofing and tampering can be detected with high probability. The data collected from the experimental deployment of sensor nodes supports the solution proposed by the paper.

This paper finds that actions of the teeth, namely tapping and sliding, produce vibrations in the jaw and skull. These vibrations are strong enough to propagate to the edge of the face and produce vibratory signals at an earphone. By re-tasking the earphone speaker as an input transducer - a software modification in the sound card - we are able to sense teeth-related gestures across various models of ear/headphones. In fact, by analyzing the signals at the two earphones, we show the feasibility of also localizing teeth gestures, resulting in a human-to-machine interface. Challenges range from coping with weak signals, distortions due to different teeth compositions, lack of timing resolution, spectral dispersion, etc. We address these problems with a sequence of sensing techniques, resulting in the ability to detect 6 distinct gestures in real-time. Results from 18 volunteers exhibit robustness, even though our system - EarSense - does not depend on per-user training. Importantly, EarSense also remains robust in the presence of concurrent user activities, like walking, nodding, cooking and cycling. Our ongoing work is focused on detecting teeth gestures even while music is being played in the earphone; once that problem is solved, we believe EarSense could be even more compelling.

In an Industrial Control System (ICS), its complex network of sensors, actuators and controllers have raised security concerns. In this paper, we proposed a technique called Process Skew that uses the small deviations in the ICS process (herein called as a process fingerprint) for anomaly detection. The process fingerprint appears as noise in sensor measurements due to the process fluctuations. Such a fingerprint is unique to a process due to the intrinsic operational constraints of the physical process. We validated the proposed scheme using the data from a real-world water treatment testbed. Our results show that we can effectively identify a process based on its fingerprint, and detect process anomaly with a very low false-positive rate.

Wireless jamming is a critical challenge for sprawling and ubiquitous devices, which are connected using radio-frequency (RF) waves. Traditionally direct-sequence spread spectrum (DSSS) and its derivatives have been recognized as an effective jamming resilient technique. However, the effectiveness of DSSS relies on the use of either pre-shared secret code or a large bank of public codes, the generation, distribution, and management of which would be particularly difficult in future large-scale decentralized wireless networks. To tackle this problem, we present a framework which exploits the shared randomness inherent in wireless channels to generate and refresh secret seeds at each communicating node. We highlight why channel randomness cannot be used as it is and develop processing algorithms which ensure that RF based sources are suitable for entropy pooling and random seed generation.

In this paper, we propose a secret group-key generation scheme in physical layer, where an arbitrary number of multi-antenna LNs (LN) exist in mesh topology with a multi-antenna passive eavesdropper. In the first phase of the scheme, pilot signals are transmitted from selected antennas of all nodes and each node estimates channels linked to it. In the second phase, each node sequentially broadcasts a weighted combination of the estimated channel information using selected coefficients. The other LNs can obtain the channel information used for group-key generation while the eavesdropper cannot. Each node then can generate a group key by quantizing and encoding the estimated channels into keys. We apply well-known quantization schemes, such as scalar and vector quantizations, and compare their performance. To further enhance the key-generation performance, we also provide how to determine the antennas at each node used for group-key generation and the coefficients used in the broadcast phase. The simulation results verify the performance of the proposed secret group-key generation scheme using various key-related metrics. We also verify the practical robustness of our scheme by implementing a testbed using universal software radio peripheral. After generating secret common key among three nodes, we also test it using the National Institute of Standards and Technology test suit. The generated key passes the test and it is random enough for communication secrecy.