Using metrics from multiple layers to detect attacks in wireless networks

The IEEE 802.11 networks are vulnerable to numerous wireless-specific attacks. Attackers can implement MAC address spoofing techniques to launch these attacks, while masquerading themselves behind a false MAC address. The implementation of Intrusion Detection Systems has become fundamental in the development of security infrastructures for wireless networks. This thesis proposes the designing a novel security system that makes use of metrics from multiple layers of observation to produce a collective decision on whether an attack is taking place. The Dempster-Shafer Theory of Evidence is the data fusion technique used to combine the evidences from the different layers. A novel, unsupervised and self- adaptive Basic Probability Assignment (BPA) approach able to automatically adapt its beliefs assignment to the current characteristics of the wireless network is proposed. This BPA approach is composed of three different and independent statistical techniques, which are capable to identify the presence of attacks in real time. Despite the lightweight processing requirements, the proposed security system produces outstanding detection results, generating high intrusion detection accuracy and very low number of false alarms. A thorough description of the generated results, for all the considered datasets is presented in this thesis. The effectiveness of the proposed system is evaluated using different types of injection attacks. Regarding one of these attacks, to the best of the author knowledge, the security system presented in this thesis is the first one able to efficiently identify the Airpwn attack

Performance analysis of wireless intrusion detection systems

Wireless intrusion detection system (WIDS) has become a matter of increasing concern in recent years as a crucial element in wireless network security. WIDS monitors 802.11 traffic to identify the intrusive activities, and then alerts the complementary prevention part to combat the attacks. Selecting a reliable WIDS system necessitates inevitably taking into account a credible evaluation of WIDSs performance. WIDS effectiveness is considered the basic factor in evaluating the WIDS performance, thus it receives great attention in this thesis. Most previous experimental evaluations of intrusion detection systems (IDSs) were concerned with the wired IDSs, with an apparent lack of evaluating the wireless IDSs (WIDSs). In this thesis, we try to manipulate three main critiques of most pervious evaluations; lack of comprehensive evaluation methodology, holistic attack classification, and expressive evaluation metrics. In this thesis, we introduce a comprehensive evaluation methodology that covers all the essential dimensions for a credible evaluation of WIDSs performance. The main pivotal dimensions in our methodology are characterizing and generating the evaluation dataset, defining reliable and expressive evaluation metrics, and overcoming the evaluation limitations. Basically, evaluation dataset consists of two main parts; normal traffic (as a background) and malicious traffic. The background traffic, which comprises normal and benign activities in the absence of attacks, was generated in our experimental evaluation tests as real controlled traffic. The second and important part of the dataset is the malicious traffic which is composed of intrusive activities. Comprehensive and credible evaluation of WIDSs necessitates taking into account all possible attacks. While this is operationally impossible, it is necessary to select representative attack test cases that are extracted mainly from a comprehensive classification of wireless attacks. Dealing with this challenge, we have developed a holistic taxonomy of wireless security attacks from the perspective of the WIDS evaluator. The second pivotal dimension in our methodology is defining reliable evaluation metrics. We introduce a new evaluation metric EID (intrusion detection effectiveness) that manipulates the drawbacks of the previously proposed metrics, especially the common drawback of their main notion that leads to measuring a relative effectiveness. The notion of our developed metric EID helps in measuring the actual effectiveness. We also introduce another metric RR (attack recognition rate) to evaluate the ability of WIDS to recognize the attack type. The third important dimension in our methodology is overcoming the evaluation limitations. The great challenge that we have faced in the experimental evaluation of WIDSs is the uncontrolled traffic over the open wireless medium. This uncontrolled traffic affects the accuracy of the measurements. We overcame this problem by constructing an RF shielded testbed to take all the measurements under our control without any interfering from any adjacent stations. Finally, we followed our methodology and conducted experimental evaluation tests of two popular WIDSs (Kismet and AirSnare), and demonstrated the utility of our proposed solutions

Analyse de performance des systèmes de détection d’intrusion sans-fil

La sécurité des réseaux sans fil fait l’objet d’une attention considérable ces dernières années. Toutefois, les communications sans fil sont confrontées à plusieurs types de menaces et d’attaques. Par conséquent, d’importants efforts, visant à sécuriser davantage les réseaux sans fil, ont dû être fournis pour en vue de lutter contre les attaques sans fil. Seulement, croire qu’une prévention intégrale des attaques peut s’effectuer au niveau de la première ligne de défense d’un système (pare-feux, chiffrement, …) n’est malheureusement qu’illusion. Ainsi, l’accent est de plus en plus porté sur la détection des attaques sans fil au travers d’une seconde ligne de défense, matérialisée par les systèmes de détection d’intrusions sans fil (WIDS). Les WIDS inspectent le trafic sans fil, respectant la norme 802.11, ainsi que les activités du système dans le but de détecter des activités malicieuses. Une alerte est ensuite envoyée aux briques chargées de la prévention pour contrer l’attaque. Sélectionner un WIDS fiable dépend principalement de l’évaluation méticuleuse de ses performances. L’efficacité du WIDS est considérée comme le facteur fondamental lors de l’évaluation de ses performances, nous lui accordons donc un grand intérêt dans ces travaux de thèse. La majeure partie des études expérimentales visant l’évaluation des systèmes de détection d’intrusions (IDS) s’intéressait aux IDS filaires, reflétant ainsi une carence claire en matière d’évaluation des IDS sans fil (WIDS). Au cours de cette thèse, nous avons mis l’accent sur trois principales critiques visant la plupart des précédentes évaluations : le manque de méthodologie d’évaluation globale, de classification d’attaque et de métriques d’évaluation fiables. Au cours de cette thèse, nous sommes parvenus à développer une méthodologie complète d’évaluation couvrant toutes les dimensions nécessaires pour une évaluation crédible des performances des WIDSs. Les axes principaux de notre méthodologie sont la caractérisation et la génération des données d’évaluation, la définition de métriques d’évaluation fiables tout en évitant les limitations de l’évaluation. Fondamentalement, les données d’évaluation sont constituées de deux principales composantes à savoir: un trafic normal et un trafic malveillant. Le trafic normal que nous avons généré au cours de nos tests d’évaluation était un trafic réel que nous contrôlions. La deuxième composante des données, qui se trouve être la plus importante, est le trafic malveillant consistant en des activités intrusives. Une évaluation complète et crédible des WIDSs impose la prise en compte de tous les scénarios et types d’attaques éventuels. Cela étant impossible à réaliser, il est nécessaire de sélectionner certains cas d’attaque représentatifs, principalement extraits d’une classification complète des attaques sans fil. Pour relever ce défi, nous avons développé une taxinomie globale des attaques visant la sécurité des réseaux sans fil, d’un point de vue de l’évaluateur des WIDS. Le deuxième axe de notre méthodologie est la définition de métriques fiables d’évaluation. Nous avons introduit une nouvelle métrique d’évaluation, EID (Efficacité de la détection d’intrusion), visant à pallier les limitations des précédentes métriques proposées. Nous avons démontré l’utilité de la métrique EID par rapport aux autres métriques proposées précédemment et comment elle parvenait à mesurer l’efficacité réelle tandis que les précédentes métriques ne mesuraient qu’une efficacité relative. L’EID peut tout aussi bien être utilisé pour l’évaluation de l’efficacité des IDS filaires et sans fil. Nous avons aussi introduit une autre métrique notée RR (Taux de Reconnaissance), pour mesurer l’attribut de reconnaissance d’attaque. Un important problème se pose lorsque des tests d’évaluation des WIDS sont menés, il s’agit des données de trafics incontrôlés sur le support ouvert de transmission. Ce trafic incontrôlé affecte sérieusement la pertinence des mesures. Pour outrepasser ce problème, nous avons construit un banc d’essai RF blindé, ce qui nous a permis de prendre des mesures nettes sans aucune interférence avec quelconque source de trafic incontrôlé. Pour finir, nous avons appliqué notre méthodologie et effectué des évaluations expérimentales relatives à deux WIDSs populaires (Kismet et AirSnare); nous avons démontré à l’issue de ces évaluations pratiques et l’utilité de nos solutions proposées. ABSTRACT : Wireless intrusion detection system (WIDS) has become a matter of increasing concern in recent years as a crucial element in wireless network security. WIDS monitors 802.11 traffic to identify the intrusive activities, and then alerts the complementary prevention part to combat the attacks. Selecting a reliable WIDS system necessitates inevitably taking into account a credible evaluation of WIDSs performance. WIDS effectiveness is considered the basic factor in evaluating the WIDS performance, thus it receives great attention in this thesis. Most previous experimental evaluations of intrusion detection systems (IDSs) were concerned with the wired IDSs, with an apparent lack of evaluating the wireless IDSs (WIDSs). In this thesis, we try to manipulate three main critiques of most pervious evaluations; lack of comprehensive evaluation methodology, holistic attack classification, and expressive evaluation metrics. In this thesis, we introduce a comprehensive evaluation methodology that covers all the essential dimensions for a credible evaluation of WIDSs performance. The main pivotal dimensions in our methodology are characterizing and generating the evaluation dataset, defining reliable and expressive evaluation metrics, and overcoming the evaluation limitations. Basically, evaluation dataset consists of two main parts; normal traffic (as a background) and malicious traffic. The background traffic, which comprises normal and benign activities in the absence of attacks, was generated in our experimental evaluation tests as real controlled traffic. The second and important part of the dataset is the malicious traffic which is composed of intrusive activities. Comprehensive and credible evaluation of WIDSs necessitates taking into account all possible attacks. While this is operationally impossible, it is necessary to select representative attack test cases that are extracted mainly from a comprehensive classification of wireless attacks. Dealing with this challenge, we have developed a holistic taxonomy of wireless security attacks from the perspective of the WIDS evaluator. The second pivotal dimension in our methodology is defining reliable evaluation metrics. We introduce a new evaluation metric EID (intrusion detection effectiveness) that manipulates the drawbacks of the previously proposed metrics, especially the common drawback of their main notion that leads to measuring a relative effectiveness. The notion of our developed metric EID helps in measuring the actual effectiveness. We also introduce another metric RR (attack recognition rate) to evaluate the ability of WIDS to recognize the attack type. The third important dimension in our methodology is overcoming the evaluation limitations. The great challenge that we have faced in the experimental evaluation of WIDSs is the uncontrolled traffic over the open wireless medium. This uncontrolled traffic affects the accuracy of the measurements. We overcame this problem by constructing an RF shielded testbed to take all the measurements under our control without any interfering from any adjacent stations. Finally, we followed our methodology and conducted experimental evaluation tests of two popular WIDSs (Kismet and AirSnare), and demonstrated the utility of our proposed solutions

Wireless local area network management frame denial- of-service attack detection and mitigation schemes

Wireless Local Area Networks (WLAN) are increasingly deployed and in widespread use worldwide due to its convenience and low cost. However, due to the broadcasting and the shared nature of the wireless medium, WLANs are vulnerable to a myriad of attacks. Although there have been concerted efforts to improve the security of wireless networks over the past years, some attacks remain inevitable. Attackers are capable of sending fake de-authentication or disassociation frames to terminate the session of active users; thereby leading to denial of service, stolen passwords, or leakage of sensitive information amongst many other cybercrimes. The detection of such attacks is crucial in today's critical applications. Many security mechanisms have been proposed to effectively detect these issues, however, they have been found to suffer limitations which have resulted in several potential areas of research. This thesis aims to address the detection of resource exhaustion and masquerading DoS attacks problems, and to construct several schemes that are capable of distinguishing between benign and fake management frames through the identification of normal behavior of the wireless stations before sending any authentication and de-authentication frames. Thus, this thesis proposed three schemes for the detection of resource exhaustion and masquerading DoS attacks. The first scheme was a resource exhaustion DoS attacks detection scheme, while the second was a de- authentication and disassociation detection scheme. The third scheme was to improve the detection rate of the de-authentication and disassociation detection scheme using feature derived from an unsupervised method for an increased detection rate. The effectiveness of the performance of the proposed schemes was measured in terms of detection accuracy under sophisticated attack scenarios. Similarly, the efficiency of the proposed schemes was measured in terms of preserving the resources of the access point such as memory consumptions and processing time. The validation and analysis were done through experimentation, and the results showed that the schemes have the ability to protect wireless infrastructure networks against denial of service attacks

Collaboration Enforcement In Mobile Ad Hoc Networks

Mobile Ad hoc NETworks (MANETs) have attracted great research interest in recent years. Among many issues, lack of motivation for participating nodes to collaborate forms a major obstacle to the adoption of MANETs. Many contemporary collaboration enforcement techniques employ reputation mechanisms for nodes to avoid and penalize malicious participants. Reputation information is propagated among participants and updated based on complicated trust relationships to thwart false accusation of benign nodes. The aforementioned strategy suffers from low scalability and is likely to be exploited by adversaries. To address these problems, we first propose a finite state model. With this technique, no reputation information is propagated in the network and malicious nodes cannot cause false penalty to benign hosts. Misbehaving node detection is performed on-demand; and malicious node punishment and avoidance are accomplished by only maintaining reputation information within neighboring nodes. This scheme, however, requires that each node equip with a tamper-proof hardware. In the second technique, no such restriction applies. Participating nodes classify their one-hop neighbors through direct observation and misbehaving nodes are penalized within their localities. Data packets are dynamically rerouted to circumvent selfish nodes. In both schemes, overall network performance is greatly enhanced. Our approach significantly simplifies the collaboration enforcement process, incurs low overhead, and is robust against various malicious behaviors. Simulation results based on different system configurations indicate that the proposed technique can significantly improve network performance with very low communication cost