LSTM deep learning method for network intrusion detection system

The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services or steal information, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-TermMemory (LSTM) to recognize menaces and to obtain a long-term memory on them, inorder to stop the new attacks that are like the existing ones, and at the sametime, to have a single mean to block intrusions. According to the results of the experiments of detections that we have carried out, the Accuracy reaches upto 99.98 % and 99.93 % for respectively the classification of two classes and several classes, Also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is very effective, it has a great ability to memorize and differentiate between normal traffic and attack traffic and its identification is more accurate than other Machine Learning classifiers

Anomaly-Based Intrusion Detection System To Detect Advanced Persistent Threats: Environmental Sustainability

In an evolving digital world, Advanced Persistent Threats (APTs) pose severe cybersecurity challenges. These extended, stealthy cyber-attacks, often elude conventional Intrusion Detection Systems (IDS). To bridge this gap, our research introduces a novel, environmentally conscious, deep learning-based IDS designed for APT detection. The system encompasses various stages from objective definition, data collection and preprocessing, to model development, integration, validation, and deployment. The system, utilizing deep learning algorithms, scrutinizes network traffic to detect patterns characteristic of APTs. This approach improves IDS accuracy and allows real-time threat detection, enabling prompt response to potential threats. Importantly, our system contributes to environmental protection by minimizing power consumption and electronic waste associated with cyberattacks, promoting sustainable cybersecurity practices. Our research outcomes are expected to enhance APT detection, providing robust defense against sophisticated cyber threats. Our environmentally-conscious perspective adds a unique dimension to the cybersecurity domain, underlining its role in sustainable practices

SDN/NFV architectures for edge-cloud oriented IoT

Thanks to Software Defined Networking (SDN) and Network Functions Virtualization (NFV), the use and behaviour of interconnect network backhauls to provide virtualization services has changed completely. Several benefits have been discovered in various application areas that combine SDN and NFV. As a result, we explored the SDN / NFV paradigm to determine if network services could be efficiently deployed, managed, and distributed to end users. The Internet of Things (IoT) is inseparable from improving SDN / b NFV to improve this task. However, until now, problems related to Edge cloud communications and network services have not been effectively mitigated. The rest of this article is organized as follows. We first present the background of this work. Then we present the new technologies around these topics and the extended architecture, and e. Finally, we conclude this work

Mobile Forensics Data Acquisition

Mobile technology is among the fastest developing technologies that have changed the way we live our lives. And, with the increase of the need to protect our personal information, smartphone companies have developed multiple types of security protection measures on their devices which makes the forensic data acquisition for law enforcement purposes so much harder. As we all know, one of the biggest tasks in mobile forensics investigation is the step of data acquisition, it is the step of extracting all the valuable information that will help the investigators to bring out all the evidences. In this paper, we will explain the traditional forensic data acquisition methods and the impact of encryption and security protection that been implemented in new smartphones on these methods, we will also present some new mobile forensics methods that will help to bypass the security measures in new generation smartphones, and finally, we will propose a new data extraction model using artificial intelligence

Anomaly-Based Intrusion Detection System To Detect Advanced Persistent Threats: Environmental Sustainability

In an evolving digital world, Advanced Persistent Threats (APTs) pose severe cybersecurity challenges. These extended, stealthy cyber-attacks, often elude conventional Intrusion Detection Systems (IDS). To bridge this gap, our research introduces a novel, environmentally conscious, deep learning-based IDS designed for APT detection. The system encompasses various stages from objective definition, data collection and preprocessing, to model development, integration, validation, and deployment. The system, utilizing deep learning algorithms, scrutinizes network traffic to detect patterns characteristic of APTs. This approach improves IDS accuracy and allows real-time threat detection, enabling prompt response to potential threats. Importantly, our system contributes to environmental protection by minimizing power consumption and electronic waste associated with cyberattacks, promoting sustainable cybersecurity practices. Our research outcomes are expected to enhance APT detection, providing robust defense against sophisticated cyber threats. Our environmentally-conscious perspective adds a unique dimension to the cybersecurity domain, underlining its role in sustainable practices

Access control in IoT environments: Feasible scenarios

International audienceThe Internet of Things (IoT) is the extension of the internet to the physical world where all objects collect information and interact with their environments with no or little human intervention. They collect and transfer sensitive and private data from various users. This puts security and privacy issues at the forefront: the ability to manage the digital identity of millions of people and billions of devices is fundamental for success. As most of the information contained in IoT environment may be personal or sensitive data, there is a requirement to support anonymity and restrain access to information. This article will focus on access control and authentication mechanisms as well as supporting the cryptography algorithms in constrained devices

Advanced IoT Network Topologies to Optimize Medical Monitoring Platforms based on a Constrained and Secured IOT Application Protocol CoAP

The Internet of Things (IoT) became, and still an important and critical element during the covid-19 pandemic, and this paper was written within that framework, as it proposes a synchronized medical IoT platform that is used to monitor citizens’ access to public areas, and where the access is only authorized if one of the three following conditions is fulfilled: Be vaccinated (which is verified via a QR code), having a negative PCR test (valid for only 48 hours), undergoing a body temperature measurement. Of course, a confirmation of identity with a facial recognition test is mandatory. This automatic process will allow us to reduce the possibility of spreading the disease due to the congestion of the checkpoints, as well as to detect citizens who could be potential patients of the covid-19 virus

Teaching Android Mobile Security

International audienceAt present, computer science studies generally offer courses addressing mobile development and they use mobile technologies for illustrating theoretical concepts such as operating system, design patterns, and compilation because Android and iOS use a large variety of technologies for developing applications. Teaching courses on security is also becoming an important concern for academics, and the use of mobile platforms (such as Android) as supporting material is becoming a reasonable option. In this paper, we intend to bridge a gap in the literature by reversing this paradigm: Android is not only an opportunity to learn security concepts but requires strong pedagogical efforts for covering all the aspects of mobile security. Thus, we propose teaching Android mobile security through a two-dimensional approach. The first dimension addresses the cognitive process of the Bloom taxonomy, and the second dimension addresses the technical layers of the architecture of the Android operating system. We describe a set of comprehensive security laboratory courses covering various concepts, ranging from the application development perspective to a deep investigation of the Android Open Source Project and its interaction with the Linux kernel. We evaluated this approach, and our results verify that the designed security labs impart the required knowledge to the students