Electrical systems keep things running in modern life, but they often run into problems like imbalances, short circuits, ground faults, and overloading, which can cause equipment to break down, fires to break out, and even large-scale blackouts. To make matters worse, acts of sabotage, physical damage, or cyberattacks on systems like SCADA can mess up operations, throw grids off balance, and set off cascading failures. To avoid these risks, there is a growing need for smarter tools that can keep track of system performance and flag potential issues before they get out of hand. In this paper, we suggest a deep learning model built on the inception architecture, designed to monitor electrical systems, call out potential security faults, and spot malicious actions. Taking advantage of deep learning, our approach helps increase fault prediction accuracy and keep operations on track.

Intentionally mutilated fingerprints pose a significant challenge in forensic identification. Such deliberate actions typically stem from individuals seeking to evade detection or association with past or prospective criminal activities. The detection of damaged fingerprints presents a formidable obstacle for most of current forensic systems, often leading to a pronounced incidence of false negatives. The ramifications of a false negative are profound, as they preclude the establishment of links between suspects and crime scenes, impeding the acquisition of vital evidence and potentially stalling investigative progress. In response to this critical issue, this paper delves into the development of a deep learning based model expressly designed to accurately discern and capture patterns present in damaged fingerprints.

The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs), Word Embeddings, and data augmentation techniques. Through the fusion of CNNs’ capacity for hierarchical feature extraction with the semantic representations afforded by Word Embeddings, our method achieves exemplary discrimination performance. Additionally, the incorporation of data augmentation enriches the training dataset, thereby augmenting model generalization and resilience. Empirical evaluation on a larger dataset of statements demonstrates the efficacy of our approach, surpassing many baseline approaches in terms of discovery accuracy, precision, recall and F1-score.

Spam consists of unwanted messages that are often containers of malicious code and/or links pointing to shady sites or objects that pose real dangers to a company’s machines, software, or data. Spam detection is therefore a primary security objective. Nevertheless, the detection tools available on the market are few in number and their efficiency is often limited. In this paper, we propose a spam detection tool based on deep-learning. Our tool uses bidirectional Long-Short Term Memory networks while relying on Stanford Global Vectors for word representation. We present the techniques we use. Then, we conduct a series of experiments on a family of candidate detectors. Finally, we present the performance of the selected detector.

Propaganda, defamation, abuse, insults, disinformation and fake news are not new phenomena and have been around for several decades. However, with the advent of the Internet and social networks, their magnitude has increased and the damage caused to individuals and corporate entities is becoming increasingly greater, even irreparable. In this paper, we tackle the detection of text-based cyberpropaganda using Machine Learning and NLP techniques. We use the eXtreme Gradient Boosting (XGBoost) algorithm for learning and detection, in tandem with Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) for text vectorization. We highlight the contribution of gradient boosting and regularization mechanisms in the performance of the explored model.

Racism is an unequal treatment based on race, color, origin, ethnicity or religion. It is often associated with rejection, inequality, and value judgment. A racist act, whether conscious or unconscious, goes beyond insult and aggression and leaves a devastating psychological effect on the victim. Although almost all laws around the world punish racist acts and speech, racist messages are on the rise on social networks. As a result, there is a strong need for reliable and accurate detectors of racist comments to identify the offenders and take appropriate punitive action against them. In this paper, we propose a model for the detection of racist statements in text messages by Bidirectional Gated Recurrent Units. For the word representation, we use different word embedding techniques, namely Word2Vec and GloVe. We show that this combination works well and provides a good level of detection. At the end of our study, we will suggest new horizons to improve the quality of our model.

With the wide variety of applications offered by Android, this system has been able to dominate the smartphone market. These applications provide all kinds of features and services that have become highly requested and welcomed by users. Besides, these applications represent risky vehicles for malware on Android devices. In this paper, we propose a novel formal technique to enforce the security of Android applications. We start off with an untrusted Android application and a security policy, and we end up in a new version of the application that behaves according to the policy. To ensure the correctness of results, we use formal methods in each step of the process, either in the system and the security policy specification or in the enforcement technique itself. The target application is reverse-engineered to its assembly-like code, Smali. An executable semantics called k-Smali was defined for this code using a language definitional framework, called k Framework. Security policies are specified in LTL-logic. The enforcement step consists of integrating the LTL formula in the k-Smali program using rewriting. It aims to rewrite the system specification automatically so that it satisfies the requested formula.

This paper explores the performance of an Advanced Driving Assistance System (ADAS) during navigation in urban traffic and a winter condition. The selected ADAS technology, Mobileye, has been integrated into a hydrogen electric vehicle. A set of three cameras (visible spectrum) has also been installed to give a surrounding view of the test vehicle. The tests were carried out during the dusk as well as in the night in winter condition. Using Matlab, the messages provided by Mobileye system have been analyzed. More than 2800 samples (short sequences of 5s Mobileye messages) have been processed and compared with the corresponding video samples recorded by the three cameras. In average, the selected ADAS device was able to provide 99% of true positive vehicle detection and classification, even in poor ambient lighting condition in winter. However, 72% of samples involving a pedestrian was correctly classified.

In several critical military missions, more than one decision level are involved. These decision levels are often independent and distributed, and sensitive pieces of information making up the military mission must be kept hidden from one level to another even if all of the decision levels cooperate to accomplish the same task. Usually, a mission is negotiated through insecure networks such as the Internet using cryptographic protocols. In such protocols, few security properties have to be ensured. However, designing a secure cryptographic protocol that ensures several properties at once is a very challenging task. In this paper, we propose a new secure protocol for multipart military missions that involve two independent and distributed decision levels having different security levels. We show that it ensures the secrecy, authentication, and non-repudiation properties. In addition, we show that it resists against man-in-the-middle attacks.

Proving that a cryptographic protocol is correct for secrecy is a hard task. One of the strongest strategies to reach this goal is to show that it is increasing, which means that the security level of every single atomic message exchanged in the protocol, safely evaluated, never deceases. Recently, two families of functions have been proposed to measure the security level of atomic messages. The first one is the family of interpretation-functions. The second is the family of witness-functions. In this paper, we show that the witness-functions are more efficient than interpretation-functions. We give a detailed analysis of an ad-hoc protocol on which the witness-functions succeed in proving its correctness for secrecy while the interpretation-functions fail to do so.

In this paper, we are proposing a new tool for reversing Java applications called SinJAR. SinJAR is a lightweight software written in Java aiming at inspecting bytecode at compile time and producing the structure tree of a targeted application. Besides, it is able to detect vulnerabilities and security weaknesses inside the Java code. SinJAR can be used for two purposes. The first one is sane and consists in using it to verify whether or not an application is safe and compliant with its specification. The second one is malicious and consists in spying applications through their bytecode and exploiting vulnerabilities that they may enclose. In this paper, we will show how to detect SinJAR malicious actions after showing the capabilities of the tool through few ad hoc attack scenarios conducted in a real military context.