Ninu Cremona

Ġabra ta’ poeżiji u proża li tinkludi: Għawdex ta’ A. Cremona – Meta tixjieħ ta’ A. Cremona –Tmenin! ta’ Ġużè Chetcuti – Lil Ninu Cremona ta’ Rużar Briffa – Is-Sur Nin Cremona poeta, filolgu, folklorista u grammatika f’għeluq it-80 sena tiegħu ta’ Karmenu Vassallo – Li kieku ma kienx... ta’ Karmenu Ellul Galea – Lis-Sur Nin Cremona grammatku, poeta, filolgu u prożatur f’għeluq it-80 sena minn twelidu 27 ta’ Mejju, 1880-1960 ta’ Ġużè Cardona – Xbieha bil-kitba ta’ N. Biancardi – Is-Sur Nin Cremona ta’ Kan. P. Cauchi – Lil Ninu Cremona ta’ Alfred M. Previ – Li ma kontx int, Sur Nin... ta’ Ġużè Cardona – “Ħannieqa ġiżimin” lis-Sur Nin Cremona ta’ Mary Meilak – Lis-Sur Nin Cremona f’għeluq it-tmenin sena ta’ ħajtu tal-Kan. Kap. Ġużeppi Farrugia – Lil Ninu Cremona fl-akkademja li saritlu bil-“B.I.” Victoria, Għawdex, 28. 5. 1960 ta’ M. A. Apap – Lil Ninu Cremona f’għeluq it-tmenin sena mit-twelid tiegħu ta’ Joe Mejlak – Ninu Cremona ta’ Joseph Zammit – Lil Ninu Cremona ta’ Frank Mercieca.peer-reviewe

Detecting malicious software by monitoring anomalous windows registry accesses

Abstract. We present a host-based intrusion detection system (IDS) for Microsoft Windows. The core of the system is an algorithm that detects attacks on a host machine by looking for anomalous accesses to the Windows Registry. The key idea is to first train a model of normal registry behavior on a windows host, and use this model to detect abnormal registry accesses at run-time. The normal model is trained using clean (attack-free) data. At run-time the model is used to check each access to the registry in real time to determine whether or not the behavior is abnormal and (possibly) corresponds to an attack. The system is effective in detecting the actions of malicious software while maintaining a low rate of false alarms. 1 Introduction Microsoft Windows is one of the most popular operating systems today, and also one of the most often attacked. Malicious software running on the host is often used to perpetrate these attacks. There are two widely deployed first lines of defense against malicious software, virus scanners and security patches. Virus scanners attempt to detect malicious software on the host, and security patches are operating systems updates to fix the security holes that malicious software exploits. Both of these methods suffer from the same drawback. They are effective against known attacks but are unable to detect and prevent new types of attacks. Most virus scanners are signature based meaning they use byte sequences or embedded strings in software to identify certain programs as malicious [10, 24]. If a virus scanner's signature database does not contain a signature for a specific malicious program, the virus scanner can not detect or protect against that program. In general, virus scanners require frequent updating of signature databases, otherwise the scanners become useless [29]. Similarly, security patches protect systems only when they have been written, distributed and applied to host systems. Until then, systems remain vulnerable and attacks can and do spread widely

A Comparative Evaluation of Two Algorithms for Windows Registry Anomaly Detection

We present a component anomaly detector for a host-based intrusion detection system (IDS) for Microsoft Windows. The core of the detector is a learning-based anomaly detection algorithm that detects attacks on a host machine by looking for anomalous accesses to the Windows Registry. We present and compare two anomaly detection algorithms for use in our IDS system and evaluate their performance. One algorithm called PAD, for Probabilistic Anomaly Detection, is based upon a probability density estimation while the second uses the Support Vector Machine framework. The key idea behind the detector is to first train a model of normal Registry behavior on a Windows host, even when noise may be present in the training data, and use this model to detect abnormal Registry accesses. At run-time the model is used to check each access to the Registry in real-time to determine whether or not the behavior is abnormal and possibly corresponds to an attack. The system is effective in detecting the actions of malicious software while maintaining a low rate of false alarms. We show that the probabilistic anomaly detection algorithm exhibits better performance in accuracy and in computational complexity over the support vector machine implementation under three different kernel functions