# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
###### Abstract  
In modern network security setups, Intrusion Detection Systems (IDS) are crucial elements that play a key role in protecting against unauthorized access, malicious actions, and policy breaches. Despite significant progress in IDS technology, two of the most major obstacles remain: how to avoid false alarms due to imbalanced data and accurately forecast the precise type of attacks before they even happen to minimize the damage caused. To deal with two problems in the most optimized way possible, we propose a two-task regression and classification strategy called Hybrid Regression-Classification (HRC), a deep learning-based strategy for developing an intrusion detection system (IDS) that can minimize the false alarm rate and detect and predict potential cyber-attacks before they occur to help the current wireless network in dealing with the attacks more efficiently and precisely. The experimental results show that our HRC strategy accurately predicts the incoming behavior of the IP data traffic in two different datasets. This can help the IDS to detect potential attacks sooner with high accuracy so that they can have enough reaction time to deal with the attack. Furthermore, our proposed strategy can also deal with imbalanced data. Even when the imbalance is large between categories. This will help significantly reduce the false alarm rate of IDS in practice. These strengths combined will benefit the IDS by making it more active in defense and help deal with the intrusion detection problem more effectively. intrusion detection systems; wireless network; deep learning +
Footnote †: journal:  
0000-0001-5000-2251-1100]C.-M.-M.M. * Anomaly-based IDS (or behavior-based detection, statistical-based detection)--A behavior or anomaly-based IDS solution acts beyond identifying particular attack signatures to detect and analyze malicious or unusual patterns of behavior. This type of system applies Artificial Intelligence (AI) and ML to analyze large quantities of data and network traffic to pinpoint the anomalies. Despite having higher false alarm rates than knowledge-based IDS, anomaly-based IDS can adapt to new, unique, or original attacks, and it is less dependent on identifying specific operating system vulnerabilities. Some contributions in this field are [8; 9; 10];
* Hybrid IDS--A combination of the types mentioned above. This type of system can effectively pinpoint the observed attack types and learn the pattern of traffic to track new attack types. It is one of the best solutions and has received the most focus from recent researchers but comes with the cost of high hardware resource consumption and complicated implementation depending on the components that create the system. The contributions in this field are [11; 12; 13; 14]  
The rise of ML created the opportunity to achieve more excellent IDS. Some frontiers that set the cornerstone for improving the IDS include [PERSON] and [PERSON] (2009) in [15], which investigates the challenges and opportunities of applying machine learning (ML) techniques for network intrusion detection in real-world settings. [PERSON] in [16], who introduced a multi-grade intrusion detection model based on data mining technology. The authors aim to address the shortcomings of traditional IDS, such as high false alarm rates and limited detection capabilities. Numerous more IDS designs based on Linear regression, Support Vector Machine (SVM), Naive Bayes mode, Tree-based family model and clustering models are mentioned in contributions [17; 18; 19; 20; 21] and surveys [22; 23; 24]. However, despite extensive research and promising results in controlled environments, the adoption of these proposed systems in operational settings remains limited. These contributions with ML and traditional methods often struggle to keep pace with the evolving threat landscape, where the amount of data features required to distinguish between anomaly and normal traffic behavior increases drastically as cyber-attacks become more and more delicate.