Automatic Dependent Surveillance Broadcast (ADS- B) is one the most prominent air traffic surveillance technologies that emerged in recent years. It can be used not only as a complement to existing radar-based technologies to locate air- craft, but also as a replacement in areas without radar coverage. However, a major flaw in the deployment of this technology is its reliance on clear-text, open broadcast of messages, which makes it vulnerable to attackers. In this paper, we propose ADS-Bsec, a holistic framework that solves the root cause of ADS-B problems by enforcing the much needed authenticity and integrity in ADS- B messages. It does so without changing the message format, and along with a proper key exchange mechanism. In addition, our framework also leverages results from radio-location and risk- adaptive techniques in order to prevent attacks targeting ADS- B. An important characteristic of our framework is that it was mostly implemented using a test bed with actual prototype radios, which mimics as much as possible real flight conditions. As a result, our framework is a promising approach for integrating ADS-B to NextGen infrastructure in a secure and affordable fashion.

In recent years, web-based platforms and business applications have been rising in popularity deeming themselves indispensable as they constitute the main backbone of business processes and information sharing. However, the unprecedented increased number of cyber-attacks have been threatening their day-to-day operations. In particular, the Standard Query Language Injection Attack (SQLIA) remains one of the most prevalent cyber attacks targeting web-based applications. As a consequence, the SQLIA detection techniques need to be constantly revamped and stay up-to-date in order to achieve the full potential of mitigating such threats. In this paper, we propose an artificial intelligence model based on supervised machine learning techniques to detect SQLIA. As part of the proposed model, we introduce an input string validation technique as a primary anomaly identifier using pattern matching for SQL Query data with anomalies-injections. To evaluate our approach we injected one type of SQLIA that is tautology attacks and measured the performance of our model. We used three main classifiers in our model and our findings indicate a model prediction accuracy of 98.3605% for Support Vector Machine (SVM), 96.296% for K-Nearest Neighbors (KNN), and 97.530% for Random Forest. The approach proposed in this paper has the potential of being used to integrate an automated SQL Injection detection mechanism with Intrusion Detection Systems (IDS) and Intrusion Protection Systems (IPS).

The ubiquitous access to wired and wireless networks is making information access possible from anywhere, anytime, and any device. Today, end-users are also highly mobile, often equipped with a range of portable devices, and they expect service availability when they require it. In addition, they do not want to be burdened by complex configurations before they can discover and use services. The Zero Configuration (Zeroconf) Networking technology promises to alleviate this configuration burden by allowing users to discover services and devices with little end-user intervention. We compare two popular implementations of Zeroconf namely, Avahi and Mono.Zeroconf running on Linux and Windows XP operating systems, respectively. We evaluate their performance using service discovery time as the performance metric. Our empirical results show that Linux Avahi yields almost 99% improvements in service discovery time over Windows Mono.Zeroconf. We also discuss security solutions that can be deployed to enhance the security of Zeroconf networks. We further investigate the performance of the IP Security (IPSec) protocol when used by our Mono.Zeroconf implementation running on the Windows XP platform. With IPSec, service discovery time increases by almost 45% with our prototype implementation

Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field.

Automatic Dependent Surveillance Broadcast (ADSB) is a newer air traffic surveillance protocol that is expected to strengthen and replace current RADAR-based surveillance systems, which has brought worldwide attention. However, its nature of clear text broadcast introduces many vulnerabilities to the system. We proposed a solution that uses keyed-hash message authentication code (HMAC) to address the authenticity and integrity deficiencies of ADS-B protocol Previously. In this paper we extend it by improving the HMAC computation procedure at the transmitter and the HMAC verification procedure at the receiver. In addition, this new approach is also evaluated under some types of attacks, comparing its performance against unmodified ADS-B transmissions.

In recent years, Android malware have been evolving and becoming more sophisticated at an alarming rate, highlighting the need for robust and evolving detection schemes. Despite the popularity of artificial intelligence-based approaches, they still struggle to generalize for various reasons. For instance, due to the reliance on handcrafted features for the machine learning approaches and the dependence on static datasets for the case of deep learning. In this paper, we bridge this gap by proposing Trandroid, an approach to detect diverse and real-world attack patterns targeting Android using transformers. This approach represents a major extension of our previous research to tackle this problem by developing a transformer-based Android attack detection system using the TUANDROMD dataset. Our choice of TUANDROMD was motivated by its wide coverage of Android attacks, support for metadata, and usage of feature extraction that makes it a good choice to build a holistic threat detection for Android using advanced AI models. We achieved a high accuracy rate of 99.25% with our state-of-the-art transformer model compared to the other classifiers we developed for comparison purposes, including Recurrent Neural Networks (RNNs), the Gated Recurrent Units (GRUs), Convolutional Neural Networks (CNNs), Long-Term Short-Term Memory (LSTM), and the hybrid CNN-LSTM model. Our Trandroid model also outperforms other approaches in the literature, considering all the performance indicators we used. These findings indicate the effectiveness of transformers in dealing with the evolving nature of Android malware and their promising potential for real-world deployment in mobile platforms.