Introduction
Wireless networks enable the Internet of Things and connect many gadgets daily. Wi-Fi security has significantly improved. However, mobile Internet traffic, which accounts for over 75% of last-mile usage, exposed Wi-Fi networks to security threats, according to Gao et al. (2021). Real-world Wi-Fi vulnerabilities are assessed by analyzing 19 million Wi-Fi access points (APs), predominantly in China, in unprecedented depth (Gao et al., 2021). This extensive analysis is made possible by crowdsourcing security checks on 14 million mobile devices in various scenarios (Gao et al., 2021). This analysis shows a complicated Wi-Fi threat environment nationwide, challenging earlier studies. These threats are widespread because 4 % of Wi-Fi APs were attacked. Therefore, this paper examines recent Wi-Fi security issues to understand wireless network issues. This research examines the technology behind these attacks, predicts future trends, and evaluates global implications to understand wireless communication security’s evolving landscape.
Types of Attacks in Wireless Communication Networks
Wireless networks are crucial but vulnerable to security threats in our digital world. Protecting information security’s confidentiality, integrity, and availability requires understanding the complexity of these vulnerabilities (Hadi, 2022). One critical attack is wireless network data snooping (Hadi, 2022). Eavesdropping on data packets can obtain login credentials and personal messages. Covert snooping is highly harmful to consumer privacy. Another attack is a network traffic analysis attack (Hadi, 2022). Threatening user privacy, adversaries may derive meaningful information from communication patterns without seeing the content. Analysis of communication patterns can reveal user behavior, enabling more targeted and sophisticated assaults. Modification attacks and data packet content changes occur during transmission (2022). This attack threatens information integrity. Data can be altered to install malware, misinformation, or advanced exploits. Compromises in essential systems that demand precision can have catastrophic consequences; therefore, data integrity is crucial.
Hadi advises against spoofing attacks and impersonating legitimate businesses to get wireless network access (Hadi, 2022). It affects secrecy and safety because bad actors can launch more severe attacks under bogus identities. This attack is concerning since it impersonates trusted sources, revealing sensitive information. There are also repudiation attacks that undermine wireless communication accountability (Hadi, 2022). Attacks aim to disable networks. An intruder may alter data or commit crimes and deny involvement. Repudiation attacks harm information integrity and communication networks by doubting action authenticity. Another typical attack is a DoS attack. Malik et al. (2023) state that DoS attacks are wireless network disruptions. They can be disabled by flooding or exploiting network weaknesses. Denial of service attacks compromise emergency communication networks.
Another type of attack is jamming. Malik et al. (2023) report frequent and disruptive wireless system swarming. Signals on the same frequency or band are intentionally interfered with to interrupt lawful wireless connections. Malice, espionage, or communication disruption may inspire jamming attacks. Jamming affects secrecy, safety, and availability (Malik et al., 2023). Intercepted signals may compromise data confidentiality. Jamming countermeasures like frequency hopping or spread spectrum may be studied to prevent wireless system interruptions.
Other attacks are almost similar in their execution, like active and passive attacks (Hadi, 2022). Tampering and spying affect data immediately. Invisible assaults like traffic analysis and monitoring acquire data. Understanding this distinction helps design security methods for each attack type. Hadi (2022) found that wireless communication network threats are dynamic and threaten information security. Snooping and alteration assaults threaten data integrity and confidentiality, requiring complicated responses. First, we must notice these threats to strengthen wireless networks and keep our interconnected digital environment running smoothly.
Wi-Fi DDoS Attacks Using Race Conditions
Lunis and Zulkernine (2020) evaluated Wi-Fi availability attacks in the evolving cybersecurity ecosystem. They discuss the complicated dynamics of wireless network breaches and how they may impair Wi-Fi network availability. Three evil twin DDoS assaults and race-based vulnerabilities are examined. The evil twin approach involves building a phony Wi-Fi access point to fool others into connecting to a real network, according to Lounis and Zulkernine (2020). This deception uses users’ trust in network names to launch multiple assaults. This wicked twin can disrupt Wi-Fi networks.
Denial-of-service attacks by Lounis and Zulkernine (2020) exploit race-condition vulnerabilities. System vulnerabilities and unpredictability increase as processes or threads compete for resources (Lounis & Zulkernine, 2020). Using this vulnerability strategically can disrupt Wi-Fi networks. The study’s principal race-based DDoS assault successfully affects Wi-Fi user authentication (Lounis & Zulkernine, 2020). Multiple de-authentications reduce Wi-Fi availability and user connection. Attacks can damage public places, businesses, and essential structures. Lounis and Zulkernine (2020) propose race-related de-authentication DDoS for the target network. Exceeding network capacity with these queries increases authentication and blocks valid users. The exploit reduces network availability and the administrator’s ability to detect bogus authentication attempts.
In the third DDoS attack, Lounis and Zulkernine (2020) uncovered a race condition vulnerability that inhibits reconnection after de-authentication. Attackers might purposefully delay or hinder reconnections, extending network outages. Users who rely on the network for reconnection are skeptical and dissatisfied after repeated vulnerability disruptions. Wi-Fi outages. In a wireless-dependent society, Wi-Fi outages can affect many aspects of life. Organizations, services, and individuals may lose online access due to operational issues. Race-related vulnerabilities must be understood to combat denial-of-service attacks.
Lounis and Zulkernine (2020) acknowledge the problem and propose methods to reduce risks. Secure authentication, network traffic monitoring, and race condition protection safeguard Wi-Fi networks from DDoS attacks. Understanding DDoS attacks, especially race-related weaknesses, helps design response techniques. Lounis and Zulkernine (2020) acknowledge the gravity of the situation and provide solutions. In 2020, Lounis and Zulkernine recognized and mitigated the gravity. Secure authentication, aberrant network activity monitoring, and race condition protection safeguard Wi-Fi networks from DDoS attacks.
Network Security Technology of Intelligent Information Terminal based on Mobile IoT.
As IoT devices grow and revolutionize our networked environment, security is essential. Sun et al. (2021) researched encryption, key management, and traffic characteristic aggregation. Encryption prevents data leaks and unauthorized access. The study addresses the pros and cons of encryption technologies in this context. Understanding encryption is essential to building resilient security frameworks that secure intelligent information terminal data. Sun et al. observed that mobile IoT information terminal security depends on key management. Securely connecting devices requires efficient and safe key management. The study could examine dynamic IoT key distribution, revocation, and upgrading. Robust critical management solutions protect communication from hostile actors trying to access essential data. Traffic patterns can be irregular or malicious, claims Sun et al. (2021). As a result, such traits enable proactive security systems that provide real-time detection of assaults and stop them immediately. Since cyberattacks are increasingly becoming complex daily, institutions require effective predictive and preventative measures.
Sun et al. (2021) propose the lightweight authentication model as one of the efficient ways to ensure safe connections. Mobile IoT smart information terminals need authentication to connect securely. Light variants improve safety and efficiency. IoT resource restrictions necessitate a precise balance. New authentication methods are required to improve user experience and solve mobile IoT intelligent terminal issues. Sun et al. (2021) propose the nonlinear security data fusion approach as one of the most innovative security measures for mobile IoT. The approach combines security data from multiple sources to ensure the user receives comprehensive threat information and solutions. As an advantage, this approach can adapt to any emerging or changing threats. This protocol may increase detection and reaction, making intelligent information terminals more resilient and adaptive using analytics and AI. Their research helps create resilient and flexible security solutions to defend our interconnected future as smart terminals spread and play vital roles in many industries.
Intelligent Techniques for Detecting Network Attacks
Machine and deep learning can protect networks from attacks (Aljabri et al., 2021). These intelligent methods can automatically learn network traffic patterns, anomalies, and deviations to detect threats in real time. ML and DL models are essential as attacks become more sophisticated and diverse. Intelligent methods find subtle patterns and irregularities that rule-based systems miss. Intelligent-based techniques need high-quality, various training datasets. Aljabri et al. (2021) emphasize well-curated datasets for ML and DL model robustness and generalizability. These methods use training datasets to distinguish attack-related network behavior from regular behavior. A considerable dataset helps models discover known and new threats by exposing them to different scenarios. Training dataset composition and representativeness are crucial to building resilient, intelligent systems. Intelligent network attack detection uses algorithms. Network traffic is dynamic; therefore, algorithms influence how effectively models detect trends and anomalies.
According to Aljabri et al. (2021), evaluation measures intelligent-based detecting system performance. Assessing these systems’ accuracy, precision, recall, and false favorable rates is crucial. Practical deployment needs to balance network attack detection and false positives. The study may analyze how evaluation methods affect the efficacy of intelligent approaches, highlighting the trade-offs and problems of evaluating complex systems. Intelligent-based detection systems are crucial to the resilience and security of our interconnected digital ecosystems as internet and network technologies grow.
Global Implications
Many incidents have revealed critical institution cyber assaults’ vulnerabilities and potential consequences. For example, Yahoo went down for about three hours in 2000 after a DoS attack. The Smurf attack and Tribe Flood Network Technique attacked Yahoo with gigabit-per-second data demands (Riggs et al., 2023). Riggs et al. cited DoS attacks on Los Angeles and Salt Lake County electric systems. A roughly 10-hour breach disrupted utilities’ office computer systems but did not impact power distribution. Similar attacks on critical infrastructure without mitigation are worrisome (Riggs et al., 2023). Cyberattacks using phishing spread ransomware and threaten critical infrastructure. The 2020 Federal Bureau of Information’s Internet Crime Report ranks phishing as the most common cyber-attack on U.S. citizens, showing cybercrime’s increased sophistication (Riggs et al., 2023). Phishing attacks on the Ukrainian power grid showed their severity. Phishing took control of remote terminal units in 2015, disrupting 225,000 customers. A similar attack in 2016 knocked out Kiev transmission station power for one hour (Riggs et al., 2023).
The Stuxnet computer worm shows advanced threats to critical infrastructure. The 2010 Iranian Stuxnet attack on nuclear power station OT systems is examined by Riggs et al. Strong cybersecurity is needed since the worm can affect control systems, threatening global infrastructure (Riggs et al., 2023). As Riggs et al. predicted, these historical events demonstrate the need for attack detection and prevention research. More frequent and severe critical infrastructure cyber-attacks require comprehensive cybersecurity policies and solutions to protect confidentiality, safety, and availability.
Technologies Involved
Wireless networks make internet-connected devices perform seamlessly in the digital age. The presence of these networks raises security risks that require effective security measures. These attacks use Wi-Fi protocol flaws and mobile IoT encryption. Understanding the complexity of modern wireless technology helps create effective cybersecurity solutions. Unprotected wireless networks risk piggybacking, wardriving, evil twin attacks, wireless sniffing, computer access, shoulder surfing, and mobile device theft (CISA, 2021). A multilayered defense is needed because attackers use multiple approaches to penetrate wireless network security. Unsecured wireless networks allow piggybacking and possibly illegal conduct or internet traffic interception. Wardriving includes aggressively seeking unsecured Wi-Fi networks. To steal network data, evil twin attacks spoof real access points. When public access points lack encryption, wireless sniffing enables hackers to collect passwords and credit card numbers. File sharing and public Wi-Fi networks can compromise personal info. Shoulder surfing at general typing is another low-tech but effective approach to gathering sensitive information.
Some are easy, like shoulder surfing, but others are difficult. Nonetheless, intelligent network attack detection and mitigation utilizing machine learning and deep learning are rising. Defenses are added by algorithms that learn and react to new threats in real-time. Security is essential to reduce wireless communication network risks. Change default passwords, restrict access with MAC address filtering, encrypt data with WPA3, protect the SSID, install firewalls, update antivirus software, and use file sharing cautiously (CISA, 2021). Using VPNs, updating access point software, and researching wireless security options from ISPs and router manufacturers protect wireless networks. We need technological breakthroughs, security standards, and user awareness to protect wireless networks and the myriad internet-connected goods in our increasingly connected society.
Future Trends
Emerging wireless technology will require new security methods to combat new dangers. Ansari et al. (2022) and Mohamed (2023) claim that AI and ML will secure wireless networks. AI has grown in several domains since the 20th century. Cybersecurity organizations can use AI integration to scale machine learning and deep learning models (Ansari et al., 2022). As cyber threats rise, AI is being utilized to detect and restrict cybercrime, proving its security benefits (Ansari et al., 2022). Experience is critical to AI cybersecurity learning. By recognizing and learning from threats, AI algorithms boost cybersecurity. AI helps cybersecurity systems adapt and protect against new attacks (Ansari et al., 2022). AI in cybersecurity can detect suspect traffic and activate system security in real-time (Ansari et al., 2022). Learning-based security prevents recurring attacks and improves cybersecurity (Ansari et al., 2022).
Cybersecurity signature-based methods use AI. Signature-based approaches identify malware and cyberattacks by matching attack signatures or patterns to a blacklist. This method works but suffers from new threats. However, AI has improved signature-based techniques, stopping many cyberattacks (Ansari et al., 2022). AI and ML boost computer network security. A prominent AI tool, behavior-based intrusion detection, categorizes network traffic by behavior to avoid unlawful access and interruptions (Mohamed, 2023). Network security AI finds hidden threats and adapts to changing attack patterns (Mohamed, 2023). Firewalls, intrusion detection, and vulnerability management enabled by AI protect networked systems (Mohamed, 2023). Cybersecurity relies on rule-based and signature-based IDR solutions. These systems cannot detect new threats or respond to changing assault patterns; hence, AI and ML have been incorporated. AI-based anomaly detection, supervised learning, and unsupervised learning improve IDR accuracy and efficiency (Mohamed, 2023).
AI and ML can increase malware detection. Advanced CNNs and RNNs are trained on enormous datasets to classify malware. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) train malware detection systems with synthetic data (Mohamed, 2023). AI and ML boost cybersecurity but face challenges. Interpretability, biases, errors, imbalanced datasets, and false alarms must be addressed to apply AI and ML fully and improve cybersecurity (Mohamed, 2023). These technologies must be enhanced and integrated to detect and prevent future threats, securing wireless networks.
Conclusion
Finally, more complex wireless network attacks require a complete and proactive security paradigm. Threats range from Wi-Fi availability exploitation to critical infrastructure intrusions. Wireless network security requires ingenious methods, strong encryption, and proactive mitigation. AI and ML enhance intrusion detection, malware detection, network security, real-time monitoring, and adaptive learning. Predictive powers enable wireless networks to resist attacks. In the future, AI and ML will be essential to fighting new cybersecurity threats. These technologies must be developed and combined for security, research, and collaboration. AI and ML must be employed in cybersecurity to safeguard wireless networks from evolving threats.
References
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