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Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. 

With the ever-growing concern for internet security, the field of quantum cryptography emerges as a promising solution for enhancing the security of networking systems. In this paper, 20 notable papers from leading conferences and journals are reviewed and categorized based on their focus on various aspects of quantum cryptography, including key distribution, quantum bit commitment, post-quantum cryptography, and counterfactual quantum key distribution. The paper explores the motivations and challenges of employing quantum cryptography, addressing security and privacy concerns along with existing solutions. Secure key distribution, a critical component in ensuring the confidentiality and integrity of transmitted information over a network, is emphasized in the discussion. The survey examines the potential of quantum cryptography to enable secure key exchange between parties, even when faced with eavesdropping, and other applications of quantum cryptography. Additionally, the paper analyzes the methodologies, findings, and limitations of each reviewed study, pinpointing trends such as the increasing focus on practical implementation of quantum cryptography protocols and the growing interest in post-quantum cryptography research. Furthermore, the survey identifies challenges and open research questions, including the need for more efficient quantum repeater networks, improved security proofs for continuous variable quantum key distribution, and the development of quantum-resistant cryptographic algorithms, showing future directions for the field of quantum cryptography. 

The field of DevOps security education necessitates innovative approaches to effectively address the ever evolving challenges of cybersecurity. Adopting a student-centered approach, there is the need for the design and development of a comprehensive set of hands-on learning modules. In this paper, we introduce hands-on learning modules that enable learners to be familiar with identifying known security weaknesses, based on taint tracking to accurately pinpoint vulnerable code. To cultivate an engaging and motivating learning environment, our hands-on approach includes a pre-lab, hands-on and post-lab sections. They all provide introduction to specific DevOps topics and software security problems at hand, followed by practicing with real world code examples having security issues to detect them using tools. The initial evaluation results from a number of courses across multiple schools show that the hands-on modules are enhancing the interests among students on software security and cybersecurity, while preparing them to address DevOps security vulnerabilities. 

One of the most significant challenges in the field of software code auditing is the presence of vulnerabilities in software source code. Every year, more and more software flaws are discovered, either internally in proprietary code or publicly disclosed. These flaws are highly likely to be exploited and can lead to system compromise, data leakage, or denial of service. To create a large-scale machine learning system for function-level vulnerability identification, we utilized a sizable dataset of C and C++ open-source code containing millions of functions with potential buffer overflow exploits. We have developed an efficient and scalable vulnerability detection method based on neural network models that learn features extracted from the source codes. The source code is first converted into an intermediate representation to remove unnecessary components and shorten dependencies. We maintain the semantic and syntactic information using state-ofthe- art word embedding algorithms such as GloVe and fastText. The embedded vectors are subsequently fed into neural networks such as LSTM, BiLSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we have proposed a neural network model that can overcome issues associated with traditional neural networks. We have used evaluation metrics such as F1 score, precision, recall, accuracy, and total execution time to measure the performance. We have conducted a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We have found that all neural network models provide higher accuracy when we use semantic and syntactic information as features. However, this approach requires more execution time due to the added complexity of the word embedding algorithm. Moreover, our proposed model provides higher accuracy than LSTM, BiLSTM, LSTM-Autoencoder, word2vec and BERT models, and the same accuracy as the GPT-2 model with greater efficiency. 

A search for pair production of squarks or gluinos decaying via sleptons or weak bosons is reported. The search targets a final state with exactly two leptons with same-sign electric charge or at least three leptons without any charge requirement. The analysed data set corresponds to an integrated luminosity of 139 fb⁻¹of proton-proton collisions collected at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC. Multiple signal regions are defined, targeting several SUSY simplified models yielding the desired final states. A single control region is used to constrain the normalisation of theWZ+ jets background. No significant excess of events over the Standard Model expectation is observed. The results are interpreted in the context of several supersymmetric models featuring R-parity conservation or R-parity violation, yielding exclusion limits surpassing those from previous searches. In models considering gluino (squark) pair production, gluino (squark) masses up to 2.2 (1.7) TeV are excluded at 95% confidence level.

 

A search for supersymmetry targeting the direct production of winos and higgsinos is conducted in final states with either two leptons (eorμ) with the same electric charge, or three leptons. The analysis uses 139 fb⁻¹ofppcollision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected with the ATLAS detector during Run 2 of the Large Hadron Collider. No significant excess over the Standard Model expectation is observed. Simplified and complete models with and withoutR-parity conservation are considered. In topologies with intermediate states including eitherWhorWZpairs, wino masses up to 525 GeV and 250 GeV are excluded, respectively, for a bino of vanishing mass. Higgsino masses smaller than 440 GeV are excluded in a naturalR-parity-violating model with bilinear terms. Upper limits on the production cross section of generic events beyond the Standard Model as low as 40 ab are obtained in signal regions optimised for these models and also for anR-parity-violating scenario with baryon-number-violating higgsino decays into top quarks and jets. The analysis significantly improves sensitivity to supersymmetric models and other processes beyond the Standard Model that may contribute to the considered final states.

 

Search for a new pseudoscalar a-boson decaying to muons in events with additional top quark pairs. 

A search for dark matter produced in association with a Higgs boson in final states with two hadronically decayingτ-leptons and missing transverse momentum is presented. The analysis uses 139 fb⁻¹of proton-proton collision data at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. No evidence of physics beyond the Standard Model is found. The results are interpreted in terms of a 2HDM+amodel featuring two scalar Higgs doublets and a pseudoscalar singlet field. Exclusion limits on the parameters of the model in selected benchmark scenarios are derived at 95% confidence level. Model-independent limits are also set on the visible cross-section for processes beyond the Standard Model producing missing transverse momentum in association with a Higgs boson decaying intoτ-leptons.

 

This paper describes a search for the single production of an up-type vector-like quark (T) decaying asT→HtorT→Zt. The search utilises a dataset ofppcollisions at$$ \sqrt{s} $$$\sqrt{s}$= 13 TeV collected with the ATLAS detector during the 2015–2018 data-taking period of the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb⁻¹. Data are analysed in final states containing a single lepton with multiple jets andb-jets. The presence of boosted heavy resonances in the event is exploited to discriminate the signal from the Standard Model background. No significant excess above the Standard Model expectation is observed, and 95% CL upper limits are set on the production cross section ofTquarks in different decay channels. The results are interpreted in several benchmark scenarios to set limits on the mass and universal coupling strength (κ) of the vector-like quark. For singletTquarks,κvalues above 0.53 are excluded for all masses below 2.3 TeV. At a mass of 1.6 TeV,κvalues as low as 0.35 are excluded. ForTquarks in the doublet scenario, where the production cross section is much lower,κvalues above 0.72 are excluded for all masses below 1.7 TeV, and this exclusion is extended toκabove 0.55 for low masses around 1.0 TeV.