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دنیا بڑی مکار

دنیا بڑی مکار
ہر دم رہیں چوکنا یار
اکھاں کھول ٹریں دلدار
کسے وی تینوں معاف نہیں کرنا
پھر توں رونا دکھڑے جرنا
خالی بھانڈا عقل دا بھرنا
فیر سوچنا اے بیکار
اس دنیا نوں سمجھ توں بھائی
اندر وڑ کے کرن صفائی
رب رسول دی بات بھلائی
ایہہ دنیا ہے بڑی مکار
ایہہ دنیا سب دھوکے بازی
عشق مجازی تلکن بازی
نہ اوہ شہید تے نہ اوہ غازی
دنیا نال جو کردا پیار
ہر پاسے ہے افراتفری
پھردی اے ابلیس دی نفری
زندگی ہر دی اوکھی بسری
نہ ملیا چین قرار
گھر گھر ہوندی پئی بدخوئی
مہر محبت اُٹھ گیوئی
ہر دم دیندا یار دھروئی
توبہ اللہ استغفار

بھانویں گھر وچ ہون نہ دانے
کیبل چلدی ، وجدے گانے
ٹر گئے یار اوہ لوگ سیانے
آوے گھنگرو دی چھنکار

رناں روز بازار نوں جاون
اوتھے جا ایہہ خوشیاں پاون
کھڑ کھڑ ہسن ناں شرماون
ہوون ناں اوتھے بیزار

شادیاں دے کیہو جئے وطیرے
داج چ منگن موتی ہیرے
کھجل ہوون سب بے پیرے
پر نہیں کر دے گفتار
کڑیاں منڈے کالج پڑھدے
ہر کوئی تکے نکلدے وڑدے
چنگے لوکی ویکھ کے سڑدے
میری توبہ ہے لکھ وار

مطلب دی ہن رہ گئی یاری
مہر محبت اٹھ گئی ساری
ہر نے جانا وارو واری
قبر کریندی نت پکار

بھرے بازار مسیتاں خالی
اُجڑے باغ تے روون مالی
ہر جا ہوئی اے بدحالی
ہووے شالا فضل غفار

مسجد نوں آباد نہ کردے
ڈیریاں دے وچ حقے دھردے
رب رسول توں مول...

نقد سند و متن میں شیخ البانی کے تفردات

Shaykh Muhammad Nasiruddin Albani is known as the famous scholar of the twentieth century AD. He served in Hadith for almost 60 years. He has also some particularities in the hadith’s research in which he apposed a lot of scholars. The most important of them is that he has said that some Ahadith of Sahih Bukhari and Sahi Muslim are weak. Similarly, in contrast to the previous muhaddiseen, some weak traditions have said correct and some reliable narrators as weak. Apart from this, there are two particularities of him that are very important in the research world. One is that he has explored many of unknown Ahadith and secondly he has divided the books of Hadith into two parts; weak and accurate. Some detail of these particularities is presented in this article.

An Anomaly Based Adaptive Fuzzy Framework for Detecting Network Intrusions

Anomaly based Intrusion detection systems have proved their worth by detecting zero age intrusions but suffers from large number of false alarms mainly because of imprecise definitions of their normal profile or detection models. Building accurate and precise normal profiles or detection models for intrusion detection is a complex process. It is because it involves highly dynamic network behavior, concept drift phenomenon and evolving intrusion patterns. To accommodate these network dynamics in intrusion de- tection models, we require extensive training data-sets. These data sets must contain a uniform distribution of theoretically possible intrusion patterns and normal network traffic instances. Deviation in training data-set with real time network data and skewed class distribution in training data set will result in a biased detection model. Concept drift phenomenon, huge network data, highly imbalance traffic distribution, addition of new applications and abstract boundaries between normal and abnormal behavior has limited the accuracy of generalized detection models or shortened their detection models useful life. Due to these limitations and complexities in building long term intrusion de- tection models, it is proposed in this thesis that instead of building a generalized profile responsible for detecting all the intrusions it is more helpful if short-term profiles are used to detect an intrusion or even a phase of an intrusion active in certain time space. These short term profiles are evolved by changing cost functions according to changed anomaly conditions, current network traffic patterns and security policies. The evolved profiles remain valid for a short period of time in which network dynamics can be as- sumed as piece-wise linear. In this thesis an anomaly based Adaptive SEmi-supervised Evolutionary Security (ASEES) fuzzy framework is proposed. It is based on adaptive distributed and cooperative fuzzy agents which use evolved short-term profiles. These profiles are evolved for different objectives to detect specific intrusions. Evolved pro- files are switched and activated according to current network and anomaly conditions, network security policies and based on forecasted attacks. The ASEES fuzzy framework is tested under two different attacks; DoS attack and viireconnaissance attack i.e. port scan. The results show good detection times and high detection rate due to similarity of the training and testing data-set. The results also shows a performance increase in using short term profiles along with generalize normal profiles for denial of service attacks.
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